The document summarizes Teradata's data warehousing solutions and capabilities. It highlights Teradata's ability to provide unmatched performance, scalability, and manageability. It also emphasizes Teradata's architectural flexibility to meet various requirements, optimized decisioning through superior in-database analytics, and driving superior operational execution with better insights.
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
BI is the “Gathering of data from multiple sources to present it in a way that allows executives to make better business decisions”. I will describe in more detail exactly what BI is, what encompasses the Microsoft BI stack, why it is so popular, and why a BI career pays so much. I will review specific examples from previous projects of mine that show the benefits of BI and its huge return-on-investment. I'll go into detail on the components of a BI solution, and I will discuss key concepts for successfully implementing BI in your organization.
Third Nature - Open Source Data Warehousingmark madsen
An introductory presentation on open source for data warehousing and business intelligence. Covers some history of open source, projects in different areas, and some information on adoption.
You can download this and demo.case study PDFs at
http://thirdnature.net/tdwi_osbi_material.html
This exam measures your ability to accomplish the technical tasks listed below. The percentages indicate the relative weight of each major topic area on the exam. https://www.pass4sureexam.com/70-461.html
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
BI is the “Gathering of data from multiple sources to present it in a way that allows executives to make better business decisions”. I will describe in more detail exactly what BI is, what encompasses the Microsoft BI stack, why it is so popular, and why a BI career pays so much. I will review specific examples from previous projects of mine that show the benefits of BI and its huge return-on-investment. I'll go into detail on the components of a BI solution, and I will discuss key concepts for successfully implementing BI in your organization.
Third Nature - Open Source Data Warehousingmark madsen
An introductory presentation on open source for data warehousing and business intelligence. Covers some history of open source, projects in different areas, and some information on adoption.
You can download this and demo.case study PDFs at
http://thirdnature.net/tdwi_osbi_material.html
This exam measures your ability to accomplish the technical tasks listed below. The percentages indicate the relative weight of each major topic area on the exam. https://www.pass4sureexam.com/70-461.html
Big Data and BI Tools - BI Reporting for Bay Area Startups User GroupScott Mitchell
This presentation was presented at the July 8th 2014 user group meeting for BI Reporting for Bay Area Start Ups
Content - Creation Infocepts/DWApplications
Presented by: Scott Mitchell - DWApplications
Top SAP Online training institute in HyderabadAadhyaKrishnan
ERP tech is the one of the top & Best SAP Training institute in Hyderabad. We offers best training completely on all SAP Modules Like BPC Embedded, BPC Classic and HANA with Reasonable prices.
Lessons From Integrating Machine Learning into Data Products | Wrangle Confer...Cloudera, Inc.
In this talk, we will share practical lessons and patterns for building machine learning (ML) models in production, based on our experience with search ranking and recommendation systems at Instacart. As part of this I will include a detailed discussion on the technical challenges in building a ML features pipeline, one of which is now shared across multiple data products at Instacart.
Big Data Made Easy: A Simple, Scalable Solution for Getting Started with HadoopPrecisely
With so many new, evolving frameworks, tools, and languages, a new big data project can lead to confusion and unwarranted risk.
Many organizations have found Data Warehouse Optimization with Hadoop to be a good starting point on their Big Data journey. Offloading ETL workloads from the enterprise data warehouse (EDW) into Hadoop is a well-defined use case that produces tangible results for driving more insights while lowering costs. You gain significant business agility, avoid costly EDW upgrades, and free up EDW capacity for faster queries. This quick win builds credibility and generates savings to reinvest in more Big Data projects.
A proven reference architecture that includes everything you need in a turnkey solution – the Hadoop distribution, data integration software, servers, networking and services – makes it even easier to get started.
Data Warehouse Design and Best PracticesIvo Andreev
A data warehouse is a database designed for query and analysis rather than for transaction processing. An appropriate design leads to scalable, balanced and flexible architecture that is capable to meet both present and long-term future needs. This session covers a comparison of the main data warehouse architectures together with best practices for the logical and physical design that support staging, load and querying.
Business Intelligence Presentation - Data Mining (2/2)Bernardo Najlis
In this second part of the Business Intelligence Presentation, we dive into Data Mining, what it is, its business applications and some CRM related examples.
Analytic Excellence - Saying Goodbye to Old ConstraintsInside Analysis
The Briefing Room with Dr. Robin Bloor and Actian
Live Webcast August 6, 2013
http://www.insideanalysis.com
With all the innovations in compute power these days, one of the hardest hurdles to overcome is the tendency to think in old ways. By and large, the processing constraints of yesterday no longer apply. The new constraints revolve around the strategic management of data, and the effective use of business analytics. How can your organization take the helm in this new era of analysis?
Register for this episode of The Briefing Room to find out! Veteran Analyst Wayne Eckerson of The BI Leadership Forum, will explain how a handful of key innovations has significantly changed the game for data processing and analytics. He'll be briefed by John Santaferraro of Actian, who will tout his company's unique position in "scale-up and scale-out" for analyzing data.
Data warehouses have become a popular mechanism for collecting, organizing, and making information readily available for strategic decision making. The ability to review historical trends and monitor near real-time operational data has become a key competitive advantage for many organizations. Yet the methods for assuring the quality of these valuable assets are quite different from those of transactional systems. Ensuring that the appropriate testing is performed is a major challenge for many enterprises. Geoff Horne has led a number of data warehouse testing projects in both the telecommunications and ERP sectors. Join Geoff as he shares his approaches and experiences, focusing on the key “uniques” of data warehouse testing including methods for assuring data completeness, monitoring data transformations, and measuring quality. He also explores the opportunities for test automation as part of the data warehouse process, describing how it can be harnessed to streamline and minimize overhead.
Data summit connect fall 2020 - rise of data opsRyan Gross
Data governance teams attempt to apply manual control at various points for consistency and quality of the data. By thinking of our machine learning data pipelines as compilers that convert data into executable functions and leveraging data version control, data governance and engineering teams can engineer the data together, filing bugs against data versions, applying quality control checks to the data compilers, and other activities. This talk illustrates how innovations are poised to drive process and cultural changes to data governance, leading to order-of-magnitude improvements.
What is a Data Warehouse and How Do I Test It?RTTS
ETL Testing: A primer for Testers on Data Warehouses, ETL, Business Intelligence and how to test them.
Are you hearing and reading about Big Data, Enterprise Data Warehouses (EDW), the ETL Process and Business Intelligence (BI)? The software markets for EDW and BI are quickly approaching $22 billion, according to Gartner, and Big Data is growing at an exponential pace.
Are you being tasked to test these environments or would you like to learn about them and be prepared for when you are asked to test them?
RTTS, the Software Quality Experts, provided this groundbreaking webinar, based upon our many years of experience in providing software quality solutions for more than 400 companies.
You will learn the answer to the following questions:
• What is Big Data and what does it mean to me?
• What are the business reasons for a building a Data Warehouse and for using Business Intelligence software?
• How do Data Warehouses, Business Intelligence tools and ETL work from a technical perspective?
• Who are the primary players in this software space?
• How do I test these environments?
• What tools should I use?
This slide deck is geared towards:
QA Testers
Data Architects
Business Analysts
ETL Developers
Operations Teams
Project Managers
...and anyone else who is (a) new to the EDW space, (b) wants to be educated in the business and technical sides and (c) wants to understand how to test them.
Building enterprise advance analytics platformHaoran Du
By Raymond Fu - Practice Architect
This lecture talks about the best practices in building an advanced analytics platform to help companies apply machine learning, deep learning and data science to their structured and unstructured data.
At Southern California Data Science Conference Sept.25.2016 at USC
http://socaldatascience.org/
http://www.datalaus.com/en/
Teradata Technology Leadership and InnovationTeradata
Teradata is the world's leader in data warehousing and integrated marketing management through its database software, data warehouse appliances, and enterprise analytics. For more information, visit teradata.com.
AWS Webcast - Sales Productivity Solutions with MicroStrategy and RedshiftAmazon Web Services
Sales Force Automation (SFA) and Customer Relationship Management (CRM) tools, such as Salesforce.com and Microsoft Dynamics CRM, are ubiquitous tools that provide all of the transactional capabilities required to manage a company's sales pipeline. SFA and CRM data alone, however, is limited and so combining it with information from other sources enables you to create unique and powerful insights. When combined with product and financial data, for example, get visibility into relationships between geographies, sales reps, product performance, and revenue to ultimately optimize profits. Layer on advanced analytic to make predictions about future product sales based on seasonality and other market conditions. To unleash the full power of the CRM and dramatically increase operational performance and top-line revenue, companies are leveraging advanced analytic and data visualization to deliver new insights to the entire sales organization. Moreover, delivering these sales enablement productivity solutions on mobile devices, ensures strong adoption across every sales team. Join us in this webinar to learn how to use MicroStrategy together with Amazon Redshift to build mobile sales productivity solutions for your business.
Big Data and BI Tools - BI Reporting for Bay Area Startups User GroupScott Mitchell
This presentation was presented at the July 8th 2014 user group meeting for BI Reporting for Bay Area Start Ups
Content - Creation Infocepts/DWApplications
Presented by: Scott Mitchell - DWApplications
Top SAP Online training institute in HyderabadAadhyaKrishnan
ERP tech is the one of the top & Best SAP Training institute in Hyderabad. We offers best training completely on all SAP Modules Like BPC Embedded, BPC Classic and HANA with Reasonable prices.
Lessons From Integrating Machine Learning into Data Products | Wrangle Confer...Cloudera, Inc.
In this talk, we will share practical lessons and patterns for building machine learning (ML) models in production, based on our experience with search ranking and recommendation systems at Instacart. As part of this I will include a detailed discussion on the technical challenges in building a ML features pipeline, one of which is now shared across multiple data products at Instacart.
Big Data Made Easy: A Simple, Scalable Solution for Getting Started with HadoopPrecisely
With so many new, evolving frameworks, tools, and languages, a new big data project can lead to confusion and unwarranted risk.
Many organizations have found Data Warehouse Optimization with Hadoop to be a good starting point on their Big Data journey. Offloading ETL workloads from the enterprise data warehouse (EDW) into Hadoop is a well-defined use case that produces tangible results for driving more insights while lowering costs. You gain significant business agility, avoid costly EDW upgrades, and free up EDW capacity for faster queries. This quick win builds credibility and generates savings to reinvest in more Big Data projects.
A proven reference architecture that includes everything you need in a turnkey solution – the Hadoop distribution, data integration software, servers, networking and services – makes it even easier to get started.
Data Warehouse Design and Best PracticesIvo Andreev
A data warehouse is a database designed for query and analysis rather than for transaction processing. An appropriate design leads to scalable, balanced and flexible architecture that is capable to meet both present and long-term future needs. This session covers a comparison of the main data warehouse architectures together with best practices for the logical and physical design that support staging, load and querying.
Business Intelligence Presentation - Data Mining (2/2)Bernardo Najlis
In this second part of the Business Intelligence Presentation, we dive into Data Mining, what it is, its business applications and some CRM related examples.
Analytic Excellence - Saying Goodbye to Old ConstraintsInside Analysis
The Briefing Room with Dr. Robin Bloor and Actian
Live Webcast August 6, 2013
http://www.insideanalysis.com
With all the innovations in compute power these days, one of the hardest hurdles to overcome is the tendency to think in old ways. By and large, the processing constraints of yesterday no longer apply. The new constraints revolve around the strategic management of data, and the effective use of business analytics. How can your organization take the helm in this new era of analysis?
Register for this episode of The Briefing Room to find out! Veteran Analyst Wayne Eckerson of The BI Leadership Forum, will explain how a handful of key innovations has significantly changed the game for data processing and analytics. He'll be briefed by John Santaferraro of Actian, who will tout his company's unique position in "scale-up and scale-out" for analyzing data.
Data warehouses have become a popular mechanism for collecting, organizing, and making information readily available for strategic decision making. The ability to review historical trends and monitor near real-time operational data has become a key competitive advantage for many organizations. Yet the methods for assuring the quality of these valuable assets are quite different from those of transactional systems. Ensuring that the appropriate testing is performed is a major challenge for many enterprises. Geoff Horne has led a number of data warehouse testing projects in both the telecommunications and ERP sectors. Join Geoff as he shares his approaches and experiences, focusing on the key “uniques” of data warehouse testing including methods for assuring data completeness, monitoring data transformations, and measuring quality. He also explores the opportunities for test automation as part of the data warehouse process, describing how it can be harnessed to streamline and minimize overhead.
Data summit connect fall 2020 - rise of data opsRyan Gross
Data governance teams attempt to apply manual control at various points for consistency and quality of the data. By thinking of our machine learning data pipelines as compilers that convert data into executable functions and leveraging data version control, data governance and engineering teams can engineer the data together, filing bugs against data versions, applying quality control checks to the data compilers, and other activities. This talk illustrates how innovations are poised to drive process and cultural changes to data governance, leading to order-of-magnitude improvements.
What is a Data Warehouse and How Do I Test It?RTTS
ETL Testing: A primer for Testers on Data Warehouses, ETL, Business Intelligence and how to test them.
Are you hearing and reading about Big Data, Enterprise Data Warehouses (EDW), the ETL Process and Business Intelligence (BI)? The software markets for EDW and BI are quickly approaching $22 billion, according to Gartner, and Big Data is growing at an exponential pace.
Are you being tasked to test these environments or would you like to learn about them and be prepared for when you are asked to test them?
RTTS, the Software Quality Experts, provided this groundbreaking webinar, based upon our many years of experience in providing software quality solutions for more than 400 companies.
You will learn the answer to the following questions:
• What is Big Data and what does it mean to me?
• What are the business reasons for a building a Data Warehouse and for using Business Intelligence software?
• How do Data Warehouses, Business Intelligence tools and ETL work from a technical perspective?
• Who are the primary players in this software space?
• How do I test these environments?
• What tools should I use?
This slide deck is geared towards:
QA Testers
Data Architects
Business Analysts
ETL Developers
Operations Teams
Project Managers
...and anyone else who is (a) new to the EDW space, (b) wants to be educated in the business and technical sides and (c) wants to understand how to test them.
Building enterprise advance analytics platformHaoran Du
By Raymond Fu - Practice Architect
This lecture talks about the best practices in building an advanced analytics platform to help companies apply machine learning, deep learning and data science to their structured and unstructured data.
At Southern California Data Science Conference Sept.25.2016 at USC
http://socaldatascience.org/
http://www.datalaus.com/en/
Teradata Technology Leadership and InnovationTeradata
Teradata is the world's leader in data warehousing and integrated marketing management through its database software, data warehouse appliances, and enterprise analytics. For more information, visit teradata.com.
AWS Webcast - Sales Productivity Solutions with MicroStrategy and RedshiftAmazon Web Services
Sales Force Automation (SFA) and Customer Relationship Management (CRM) tools, such as Salesforce.com and Microsoft Dynamics CRM, are ubiquitous tools that provide all of the transactional capabilities required to manage a company's sales pipeline. SFA and CRM data alone, however, is limited and so combining it with information from other sources enables you to create unique and powerful insights. When combined with product and financial data, for example, get visibility into relationships between geographies, sales reps, product performance, and revenue to ultimately optimize profits. Layer on advanced analytic to make predictions about future product sales based on seasonality and other market conditions. To unleash the full power of the CRM and dramatically increase operational performance and top-line revenue, companies are leveraging advanced analytic and data visualization to deliver new insights to the entire sales organization. Moreover, delivering these sales enablement productivity solutions on mobile devices, ensures strong adoption across every sales team. Join us in this webinar to learn how to use MicroStrategy together with Amazon Redshift to build mobile sales productivity solutions for your business.
We have embraced Cloud and Open-Source further enabling the analytics ecosystems by creating new integration capabilities at scale.
Simplifying technology footprints to make it easier to buy
Bringing scale to analytics
Fast Cycle, Multi-Terabyte Data Analysis with Amazon Redshift and ClearStory ...ClearStory Data
Organizations storing large volumes of data in Amazon Redshift rely on faster cycle analysis to quickly uncover actionable insights. Their challenge when data volumes grow in Redshift is finding an analysis solution that removes the headaches of tedious ETL, data wrangling and allows scalable, visual data analysis. These slides shared during the webinar demonstrates ClearStory Data’s solution for scalable, fast-cycle, visual data analysis, that is used by CPG, Retail, Consumer Internet companies on Redshift.
To watch the on-demand webinar, visit:
Analyst View of Data Virtualization: Conversations with Boulder Business Inte...Denodo
In this presentation, executives from Denodo preview the new Denodo Platform 6.0 release that delivers Dynamic Query Optimizer, cloud offering on Amazon Web Services, and self-service data discovery and search. Over 30 analysts, led by Claudia Imhoff, provide input on strategic direction and benefits of Denodo 6.0 to the data virtualization and the broader data integration market.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/DR6r3m.
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
Customer value analysis of big data productsVikas Sardana
Business value analysis through Customer Value Model for software technology choices with a case study from Mobile Advertising industry for Big Data use case.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
What is OLAP -Data Warehouse Concepts - IT Online Training @ NewyorksysNEWYORKSYS-IT SOLUTIONS
NEWYORKSYSTRAINING are destined to offer quality IT online training and comprehensive IT consulting services with complete business service delivery orientation.
Achieving Business Value by Fusing Hadoop and Corporate DataInside Analysis
The Briefing Room with Richard Hackathorn and Teradata
Live Webcast March 25, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e7254708146d056339a0974f097f569b2
Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful analytic solutions require a fusion of all relevant data, big and small, which has proven challenging for many companies. By allowing business analysts to quickly access data wherever it rests, success factors shift to focus on three key aspects: 1) business objectives, 2) organizational workflow, and 3) data placement.
Register for this Special Edition of The Briefing Room to hear veteran Analyst Richard Hackathorn as he provides details from his recent research report focused on success stories using Teradata QueryGrid. Examples of use cases described will include:
Joining sensor data in Hadoop with data warehouse labor schedules in seconds
How bridging corporate cultures and systems creates new business opportunities
The 360 view of customer journeys using weblogs in Hadoop via BI tools
How can you put the data where you want and query it however you want
Virtualizing Hadoop data with Teradata QueryGrid
Visit InsideAnalysis.com for more information.
FSI201 FINRA’s Managed Data Lake – Next Gen Analytics in the CloudAmazon Web Services
FINRA’s Data Lake unlocks the value in its data to accelerate analytics and machine learning at scale. FINRA's Technology group has changed its customer's relationship with data by creating a Managed Data Lake that enables discovery on Petabytes of capital markets data, while saving time and money over traditional analytics solutions. FINRA’s Managed Data Lake includes a centralized data catalog and separates storage from compute, allowing users to query from petabytes of data in seconds. Learn how FINRA uses Spot instances and services such as Amazon S3, Amazon EMR, Amazon Redshift, and AWS Lambda to provide the 'right tool for the right job' at each step in the data processing pipeline. All of this is done while meeting FINRA’s security and compliance responsibilities as a financial regulator.
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...Hortonworks
Many enterprises are turning to Apache Hadoop to enable Big Data Analytics and reduce the costs of traditional data warehousing. Yet, it is hard to succeed when 80% of the time is spent on moving data and only 20% on using it. It’s time to swap the 80/20! The Big Data experts at Attunity and Hortonworks have a solution for accelerating data movement into and out of Hadoop that enables faster time-to-value for Big Data projects and a more complete and trusted view of your business. Join us to learn how this solution can work for you.
7 Emerging Data & Enterprise Integration Trends in 2022Safe Software
2021 was a year full of unexpected data integration challenges, but one thing that didn’t change was the continued growth of the importance and value of data. By watching our customers adapt and cope through the consistent application of technology, we’ve learned that the future can be quickly adjusted to if we have up-to-date and readily available data to make decisions.
As we consider the data integration landscape and look forward into 2022, we see a set of trends (some new, some old) that data leaders will need to consider as they work to provide competitive business value to their organizations:
- The Continued Importance of Spatial
- Data Ops as a Practice
- Rising Data Volumes Demand Data Quality
- Ubiquitous Hardware Supporting Augmented Reality
- Agile Enterprise Integration Effortlessly Connects Systems
- Real-Time Data Stream Processing
- Flexible, Hybrid Deployment Options
- Cost effective ARM based processing
In this webinar, join co-founders Don Murray and Dale Lutz as they offer insight and predictions on what’s to come in these areas. To follow, they’ll host a Q&A session where you can get feedback and advice on solutions to your data challenges.
Similar to Maximizing Business Value: Optimizing Technology Investment (20)
How to Use Algorithms to Scale Digital BusinessTeradata
Gartner defines digital business as the creation of new business designs by blurring the digital and physical worlds. Digital business creates new business opportunities, but the amount of data generated will eclipse the human ability to process it. Further, many complex decisions will need to be made in timeframes, and at scales, that are impossible by human actors. Gartner analyst Chet Geschickter will explain share advice on how to leverage algorithmic business principles to drive digital business success.
Humans are sentient. We perceive. We feel. We listen. The problem is the more you put together, the more we lose these capabilities. We get slower. The idea is, how we create a company that acts like a single organism, where we identify opportunities, and that allows us to work in a faster and exponential world world where development happens in months rather than years. Don't let digital transformation become a war of competitive attrition. You may need to invest in your future to change the game.
Teradata Listener™: Radically Simplify Big Data StreamingTeradata
Teradata Listener™ is an intelligent, self-service solution for ingesting and distributing extremely fast moving data streams throughout the analytical ecosystem. Listener
is designed to be the primary ingestion framework for organizations with multiple data streams. Listener reliably delivers data without loss and provides low-latency ingestion for near real-time applications.
Telematics data provides a wealth of new, actionable insights, particularly when integrated with other enterprise data. But where do you start? How do you prioritize? What is the roadmap? In an interactive workshop learn how to derive more from data so you can do more in your business.
- Find the value of integrating telematics data with traditional data elements, including financial, customer, manufacturing, location and weather data
- How integrated telematics data can improve customer satisfaction, lifecycle management, warranty reserves, supply chain performance, and even engineering & design choices
- Gain practical examples from top manufacturers to improve operational efficiencies, develop new revenue streams, create customer insights, and better understand product performance
The Tools You Need to Build Relationships and Drive Revenue Checklist Teradata
This Campaign Manager Leadership series paper provides a checklist for marketers when considering blending offline data with online data to improve the customer experience.
Right Message, Right Time: The Secrets to Scaling Email Success Teradata
This Campaign Manager Leadership Series ebook outlines the 4 keys to an automated email marketing strategy and how marketers can scale to meet these “always-on” customer expectations.
BSI Teradata: The Shocking Case of Home Electronics PlanetTeradata
Home Electronics Planet, a big-box retailer, has digital marketing campaigns that are failing. Their Chief Marketing Officer gets some analytics and data science help from Business Scenario Investigators who recommend changing their search keywords mix, creating tighter customer segments based on product purchase sequencing coupled with real-time web page personalizations, and revising their e-mail marketing to improve business results.
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Great Brands, a major food producer, faces yet another recall. The government is pointing at Turkey Broccoli Lasagna as the culprit, so the Chief Risk Officer and Chief Supply Chain officer bring in BSI investigators to help them build a better/faster track and trace system, using Big Data analytics.
To see more BSI: Teradata, go to http://www.facebook.com/bsiTeradata
Teradata BSI: Case of the Retail Turnaround Teradata
This set of Powerpoint slides describes the analytics work of Teradata: Business Scenario Investigation employees who help move Taylor & Swift, a big-box retailer, from a silo’d stores vs. web approach to an integrated Omni-Channel Retailing approach to customers, marketing, and sales. The team comes up with 5 ideas, 2 of which are tried out. The story illustrates the use Teradata, Aster, Aprimo, and Tableau as tools to glean faster and deeper analytical insights on Big Data, specifically web walks.
Maximizing Business Value: Optimizing Technology Investment
1. Maximizing Business Value Optimizing Technology Investment Randy S. Lea Vice President, Products and Services Marketing Masters 2010
2. Teradata Leadership and Innovation The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
3. Teradata Leadership and Innovation The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
5. Teradata – Easy to Manage The Database Should Manage Data, Not People Teradata DBA Self managing administration Few or automatic controls “ Bolt-On” Parallelism 100s of controls and parameters RDBMS resists change block sizes extents reorgs file placement buffer sizing temp space many indexes sort/spool space hints parallel threads partition, repartition memory controls static priorities constant monitoring data distribution sort area I can help you with that business problem
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8. Teradata Viewpoint: Rewind Feature Scroll Back in Time Like Your Home DVR Scrolling back through history “ My query ran a long time this morning”
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11. Teradata Active Systems Management Some Workload Controls Date and time range controls Periods Exempt specific users from all rules Bypass Privileges Limit concurrent load and export jobs Load Utility Throttles Limit logons and/or queries active by account or performance group Object Throttles DDL, DML, select Type of SQL Databases, tables, views, macros, stored procedures Query Objects Time, row counts, joins, scans Query Resource Rules Parameters for a user or group of users Profiles
12. Viewpoint: Workload Monitor Portlet Teradata Ease of Use Number of Incoming Queries Outside of SLG Number of Queries After Filters and Throttles Exception Reclass Filter Reject Throttle Delay Throttle Reject Exception Reject
13. Workload Priority vs Actual Consumption Managed Dynamically by the Database R M H L Actual Server Utilization Workload Assigned Priorities Relative Weights Used R Real time – 47% H Tactical – 45% M Loads – 5% L DSS Queries – 1% L M H R
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17. Automatic data migration is what makes Teradata Virtual Storage a disruptive innovation… 6 sectors/rev 12 sectors/rev Automatically Place Data on Faster Sectors Teradata Virtual Storage 6 sectors/rev Slower Access “ Cold” Data 12 sectors/rev Faster Access “ Hot” Data
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19. Teradata – The Clear Data Warehousing Leader Leadership in Execution and Vision Ability to Execute Completeness of Vision Gartner Magic Quadrant for Data Warehouse DBMS, 2009 The Magic Quadrant is copyrighted 23 December 2008 by Gartner, Inc. and is reused with permission. The Magic Quadrant is a graphical representation of a marketplace at and for a specific time period. It depicts Gartner's analysis of how certain vendors measure against criteria for that marketplace, as defined by Gartner. Gartner does not endorse any vendor, product or service depicted in the Magic Quadrant, and does not advise technology users to select only those vendors placed in the "Leaders" quadrant. The Magic Quadrant is intended solely as a research tool, and is not meant to be a specific guide to action. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
20. Teradata Leadership and Innovation The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
27. Insurance: Data Reuse Analysis Faster Time to Market on New Insight and Apps New Business Improvement Opportunities through Data Leverage
28. Architecture Alternatives Distributed, Hub and Spoke, Federated Transactional Data Decision Users Transactional Users Data Transformation Operational Data Store (ODS) Data Warehouse Data Replication Data Marts Strategic Users Tactical Users Reporting OLAP Users Event-driven/ Closed Loop Data Miners Optional ETL Hub Hub and Spoke Enterprise Information Integration Middleware/Enterprise Message Bus Metadata Logical Data Model Physical Database Design Enterprise, System, and Database Management Business and Technology – Consultation Support and Education Services
29. Architecture Alternatives Distributed, Hub and Spoke, Federated Transactional Data Decision Users Transactional Users Data Transformation Operational Data Store (ODS) Data Warehouse Data Replication Data Marts Strategic Users Tactical Users Reporting OLAP Users Event-driven/ Closed Loop Data Miners Optional ETL Hub Hub and Spoke Enterprise Information Integration What are some of the challenges? 1. There are too many copies of the data. 2. There is too much latency . Everyone sees different data with inconsistent points in time. 3. The solution is too complex . Multiple ETL processes and increased security risk. 4. The solution is too expensive . DBAs, system admin, maint., underutilized servers, data models. Middleware/Enterprise Message Bus Metadata Logical Data Model Physical Database Design Enterprise, System, and Database Management Business and Technology – Consultation Support and Education Services
30. Advocated Architecture by Teradata Enabling Superior Business Value Transactional Data Decision Users Transactional Users Data Transformation Operational Data Store (ODS) Data Warehouse Data Replication Data Marts Strategic Users Tactical Users Reporting OLAP Users Event-driven/ Closed Loop Data Miners How is Teradata different? 1. Dramatic reduction in the number of copies of the data. 2. Reduced latency time with fewer input and access paths. 3. The architecture is simplified for faster solution development and quicker ROI! 4. Proven to be less expensive . Lower Total Cost of Ownership! Much fewer systems, people and processes. Middleware/Enterprise Message Bus Metadata Logical Data Model Physical Database Design Enterprise, System, and Database Management Business and Technology – Consultation Support and Education Services
31. Teradata’s Active Enterprise Intelligence™ Five Stages of Evolution and Maturity Continuous update and time-sensitive queries become important OPERATIONALIZING WHAT IS happening? Event-based triggering takes hold ACTIVATING MAKE it happen! Primarily batch and some ad hoc reports Increase in ad hoc analysis ANALYZING WHY did it happen? REPORTING WHAT happened? Analytical modeling grows PREDICTING WHAT WILL happen? Workload Sophistication Scalability Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
32. Capability Database and Platform Requirements Enablement for Superior Business Value Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads
33. Database and Platform Requirements Enablement for Superior Business Value Capability Extreme Performance X Database Functionality X Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads
34. Database and Platform Requirements Enablement for Superior Business Value Capability Extreme Performance XXX X Database Functionality XXX X Data Scalability XX Concurrency XX Cross Functional X Availability Workload Management Investment Protection Active Access/Loads
35. Database and Platform Requirements Enablement for Superior Business Value Capability Extreme Performance XXXX XXX X Database Functionality XXXX XXX X Data Scalability XXXX XX Concurrency XXXX XX Cross Functional XXX X Availability XX Workload Management XXX Investment Protection Active Access/Loads
36. Database and Platform Requirements Enablement for Superior Business Value Capability XX Extreme Performance XXXXX XXXX XXX X Database Functionality XXXXX XXXX XXX X Data Scalability XXXX XXXX XX Concurrency XXXX XXXX XX Cross Functional XXX XXX X Availability XXXXX XX Workload Management XXXX XXX Investment Protection XXXX Active Access/Loads
37. Database and Platform Requirements Enablement for Superior Business Value Capability XXX XX Extreme Performance XXXXX XXXXX XXXX XXX X Database Functionality XXXXX XXXXX XXXX XXX X Data Scalability XXXXXX XXXX XXXX XX Concurrency XXXX XXXX XXXX XX Cross Functional XXXXXX XXX XXX X Availability XXXXX XXXXX XX Workload Management XXXXXX XXXX XXX Investment Protection XXXXX XXXX Active Access/Loads
38. Database and Platform Requirements Enablement for Superior Business Value Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
39. Capability Database and Platform Requirements Enablement for Superior Business Value Seven Customers in the Teradata “Petabyte Club” Proven References Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
40. Database and Platform Requirements Enablement for Superior Business Value “ Intelligent Scanning”, In-Database Analytics, Triggers, Stored Procedures, etc. Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
41. Database and Platform Requirements Enablement for Superior Business Value Millions of Queries – Reports, Ad-hoc, Complex Analytics, Data Mining, Web Site, Call Center, etc Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
42. Database and Platform Requirements Enablement for Superior Business Value 128-Way Join Capable with the Most Mature Data Warehouse Optimizer in the Industry! Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
43. Database and Platform Requirements Enablement for Superior Business Value Full Performance Continuity with Hot Standby Nodes/Disks and Dual “Active” Systems Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
44. Database and Platform Requirements Enablement for Superior Business Value Achieving “Business SLG’s” Industry’s Most Advanced Workload Management Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
45. Database and Platform Requirements Enablement for Superior Business Value Multi-Generation Node Co-Existence Reduce “Floor Sweep” Upgrades Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
46. Database and Platform Requirements Enablement for Superior Business Value Batch and Continuous Loads While Supporting “Back Office” and “Front-Line” SLG’s Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
47. Database and Platform Requirements Enablement for Superior Business Value Recognized as the Industry’s Leading Data Warehouse Platform Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
48. Database and Platform Requirements Enablement for Superior Business Value Recognized as the Industry’s Leading Data Warehouse Platform “ Appliances” Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
49. Teradata Purpose-Built Platform Family Capability Extreme Performance XXXX XXX X Database Functionality XXXX XXX X Data Scalability XXXX XX Concurrency XXXX XX Cross Functional XXX X Availability XX Workload Management XXX Investment Protection Active Access/Loads
50. Teradata Purpose-Built Platform Family Capability Data Warehouse Appliance Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads
51. Teradata Purpose-Built Platform Family Capability Extreme Performance XXX X Database Functionality XXX X Data Scalability XX Concurrency XX Cross Functional Availability Workload Management Investment Protection Active Access/Loads
52. Teradata Purpose-Built Platform Family Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Data Mart Appliance
53. Teradata Purpose-Built Platform Family Capability Extreme Performance XXXXX XXXXX XXXX XXX X Database Functionality XXXXX XXXXX XXXX XXX X Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads
54. Teradata Purpose-Built Platform Family Capability Extreme Data Appliance Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads
55. Teradata Purpose-Built Platform Family Data Warehouse Appliance Data Mart Appliance Capability Extreme Data Appliance Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse
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59. Teradata Purpose-Built Platform Family Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse Extreme Data Appliance Data Warehouse Appliance Data Mart Appliance Extreme Performance Appliance
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61. Mechanical Rotation and Seek Limit HDD Speed 22X Faster on Typical Data Warehouse Workloads Mechanical Rotation and Seek Limit HDD Speed data data data data Database Server Database Server
62. Eliminating the Bottleneck! Direct Connect Array Controller Data Protection Enabled by Fallback Extreme Performance Appliance Competitive Systems Array Controller kmh
63. Teradata SSD versus Typical Flash Cache Focused on Data Warehousing, not OLTP! HDD Flash Cache 2.4 TB Data Space per Node ~.4 TB Data Space per Server Data Loads Data Loads Consistent Query Response Non Cache Hits Up to 22X Slower Unix and Windows Servers Teradata Node
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65. Teradata Purpose-Built Platform Family Capability Extreme Performance Database Functionality Data Scalability Concurrency Cross Functional Availability Workload Management Investment Protection Active Access/Loads Active Enterprise Data Warehouse Extreme Data Appliance Data Warehouse Appliance Data Mart Appliance Extreme Performance Appliance
66. 6 sectors/rev 12 sectors/rev Automatically Place Data on Faster Sectors or Drives Teradata Virtual Storage 6 sectors/rev Slower Access “ Cold” Data 12 sectors/rev Faster Access “ Hot” Data 12 sectors/rev Faster Access “ Warm” Data SSDs Fastest Access “ Hot” Data What If You Could…? Teradata Virtual Storage data data data Database Server
67. View of the Future Leverage SSD for Multi-Temperature Data 300GB SAS Drives 1 TB SAS Drives Solid State Drives (SSD) Traditional Performance On-Line Compliance /Archival Extreme Performance Teradata Active Enterprise Data Warehouse Auto-migration w/TVS
68. Architecture Flexibility – Platform Choices Business Value, Ease of Management, Cost 1 TB SAS Drives Solid State Drives (SSD) Active Enterprise Data Warehouse Independent Extreme Data Appliance 1600 Extreme Performance Appliance 4600 Integrated Auto-migration w/TVS
69. Architecture Flexibility – Platform Choices Business Value, Ease of Management, Cost 1 TB SAS Drives Solid State Drives (SSD) Active Enterprise Data Warehouse Integrated Independent Extreme Data Appliance 1600 Extreme Performance Appliance 4600 Auto-migration w/TVS
70. Architecture Flexibility – Platform Choices Business Value, Ease of Management, Cost 1 TB SATA Drives Solid State Drives (SSD) Integrated Independent Extreme Data Appliance 1600 Extreme Performance Appliance 4600 Auto-migration w/TVS In-database operations Data Movement Hot <-> Cold Inter-system operations Harder Ease of Workload Management Easier Single Database Integrated Data Silo’d Data (data model consistent) ~.8X (est.) Price 1X (est.) In-database operations Query Access Inter-system operations Single system simplicity Ease of Data Management Multi-System complexity EDW Single System / TVS Architecture Data Marts Multi-systems
71. Teradata Analytical Ecosystem Management Monitor and Manage All Teradata Systems Data Integration Business Intelligence Enterprise Data Warehouse Dual Active Production #2 Teradata Data Mover, Replication Services, Dual Load, ETL Partners Active Enterprise Data Warehouse Advanced Workload Management Master Data Management Teradata Multi-System Manager Monitoring, Alerting, Administration, Control Teradata Viewpoint Single Operational View of All Systems Test & Dev Extreme Data Data Mart BAR
72. Teradata Is About Choice Choose an Architecture to Meet Your Business Teradata Data Mover, Replication Services, Dual Load, ETL Partners Active Enterprise Data Warehouse Analytical Ecosystem Management Extreme Performance DSS Single, Integrated Active Data Warehouse SSD Entrp. HDD Fat HDD Unlimited Business Value Architecture Flexibility DSS Extreme Performance Extreme Data Extreme Data
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76. Trusted Advisor on Your Architecture With Teradata It’s Your Choice Cloud Enterprise Data Warehouse Data Marts Star Schema Active Data Warehouse™ Sand Boxes 3 rd Normal Form Hub and Spoke Teradata is the only database that does not lock you into a sub-optimal enterprise approach to data architecture, but supports a cross-section of approaches to address all analytical requirements.
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78. Teradata Leadership and Innovation The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
79. Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on “what is going to happen?” Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
80. Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse 20-40+% wasted moving data OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on “what is going to happen?” Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
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82. Teradata Business Intelligence Optimizer Building Dimensional Structure in Teradata TBIO Aggregate Designer TBIO Schema Workbench Excel or MDX Client Optimized SQL Excel or MDX Client BI Tool Schema Definitions TBIO Schema Definitions MDX AJIs
83. Teradata Business Intelligence Optimizer Building Dimensional Structure in Teradata TBIO Aggregate Designer Optimized SQL Schema Definition BI Schema Definition MDX Excel or MDX Client AJIs Teradata Schema Workbench
88. Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on “what is going to happen?” Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
89. In-Database Data Mining Optimization More Models and More Business Value Sample Data Desktop and Server Analytic Architecture Results Enabling Mining In-database Results In-Teradata Analytic Architecture Database Processing from Hours to Minutes Data Mining Process from Days to Hours
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91. Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on “what is going to happen?” Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
92. Teradata Geospatial Solution: The Integrated Environment for Faster Analytics GIS Data Mart Teradata System Geographic Information Systems Approach Teradata In-database Approach BI and GIS reports, Tools and applications Geographic data Address coordinates GIS applications EDW IT Department BI reports, tools, and applications Before After IT Department
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94. Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on “what is going to happen?” Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
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98. Elastic Mart Builder Simple, Self Service Provisioning Verify Data Table Name Integer Date Char Decimal Elastic Marts POC Sand box Virtual mart Enterprise Data Warehouse xyz Col3 PI Type 123 abc Sample Col2 Col1 CSV Upload File Upload Password Myfile.csv File name User Name Elastic Mart Builder Create UserName MySpecialID Password Perm space 500MB Import 1 2 3 4 5
99. Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse 20-40+% wasted moving data Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on what is going to happen? Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
100. Superior In-Database Analytics and Processes Enabling Better, Faster Insight Data Warehouse OLAP Cubes “ What if” analysis and reporting Agile Analytics “ Sand Boxes”, new theories on new data Data Mining Predictive analysis on what is going to happen? Geospatial New analytics on geocoded data Excel/ Access Reporting, individual analysis and models
101.
102. Teradata Leadership and Innovation The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
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104. Active Enterprise Intelligence™ An Obvious Trend: More Speed, More Users Days Seconds 1,000s to 1,000,000s of “Front Line” Operational Decisions 100s of Corporate “Knowledge Workers” Operational Intelligence Strategic Intelligence
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106. Active Enterprise Intelligence™ An Obvious Trend: More Speed, More Users Days Seconds EDW Enterprise Integration SOA, BPMS, IDEs Portals/composite applications Mixed workload management Enterprise Data Warehouse BI Tools and reports Analysis and visualization Predictive Analytics Operational Intelligence Strategic Intelligence
107. Active Enterprise Intelligence™ in Retail Detecting Retail Fraud Associates query Teradata to quickly check if a return has already occurred on that receipt number. Also used by analysts to understand and prevent excessive returns. Solution Associates in returns department did not have historical POS receipt retrieval access to verify against previously “returned” receipts or to do returns without receipts. Problem Thieves make copies of cash register receipts, walk into the store, pick up merchandise, and return items for cash. Situation
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111. Improved C redit D ecision -m aking P rocess After Scenario and Future S cenario Approval in 15 Minutes Customer Branch Call center Internet ATM External data providers Real time data gathering Automated scoring Automated pricing Automated special conditions Internal data sources/EDW Result Monitoring Data gathering and credit decision in 5 seconds today! Contract in 15 min. retail customers | 30 min. for corporate customers
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115. STRATEGIC INTELLIGENCE Active Enterprise Intelligence™ INSIGHTS ACTION OPERATIONAL INTELLIGENCE Insights Into Action Results Intelligence Respond Assess Sense Plan Report Analyze Predict
116. Teradata Leadership and Innovation Staying Ahead of the Competition The Best Database for Analytics Leverage unmatched performance, scalability, and manageability Architectural Flexibility Meet your requirements with any architecture, price point, and data model Optimized Decisioning Discover smarter insights faster using superior in-database and analytical processes Superior Operational Execution Drive faster front line actions with better, more relevant insights
122. New Teradata Business Messaging Enterprise Agility Establish the most comprehensive and transparent view of the enterprise for smarter decision making. Cut through the complexities of business dimensions to execute strategies and drive results at any scale. Apply unmatched capabilities and expertise to foster game-changing innovation. Enhance Visibility Conquer Complexity Ignite Ingenuity
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124. Enterprise Agility Examples Enhance Visibility Establish the most comprehensive and transparent view of the enterprise for smarter decision making. Connect data silos to eliminate information black holes Found millions in revenue by evaluating customer behavior AT&T Mobility migrated customers to more profitable plans based on analysis of their call detail records and location. Customers who roamed excessively were profiled by handset purchase and home address, and moved to better plans to protect profitability. Provide access to consistent and comprehensive information in a role-specific context Recovered 8% of sales lost to pricing discrepancies Haggen combines business intelligence solutions to monitor every aspect of the business from operations to inventory to promotions. Store and regional managers have access to transaction data within 15 minutes of occurrence and can respond quickly to standardize pricing. Strengthen customer and supplier relationships through dynamic information access Connected 30,000+ internal and external users to self-serve insight Norfolk Southern cut out time and labor from directly addressing the queries of power users on shipment schedule and delivery. Building a web-based “Report Wizard” enabled NS partners and employees to run more than 10,000 types of customized reports without waiting for NS to help. Dramatically increased productivity and cut cost.
125. Enterprise Agility Examples Conquer Complexity Cut through the complexities of business dimensions to execute strategies and drive results at any scale. Anticipate and act with speed and confidence as business conditions change. Increased operating income by $63M during a retail downturn JCPenney uses DCM to support advanced forecasting models to reduce inventory for items that are experiencing a decrease in demand. By aligning inventory with sales trends as they happen, JCP sells more merchandise at retail prices and minimizes unsold merchandise moving to clearance. Execute business initiatives system-atically to achieve optimal decision making at all levels of the enterprise. Reduced costs by $90 million through productivity initiatives Wesco is an electrical product distributor for with over 130,000 customers, 24,000 suppliers, 370 branches and eight distribution centers. Their solution brings together information from purchasing and inventory management systems, sales and marketing, pricing and discount management, and key historic multi-year data to cut costs and deliver profit to shareholders. Quickly answer any question across the entire value chain by integrating information from all parts of the business. Expedite policy writing to close 50% faster and more accurately Nationwide’s vision was to create an “authority source” for information and reporting to provide clarity, accuracy, and flexibility. The opportunity was to gain a single view of policy information, pricing, claims, billing, marketing and customer information. Results: products re-priced and re-rated within seconds, immediate information to agents, enabling faster writing and increased accuracy.
126. Enterprise Agility Examples Ignite Ingenuity Apply unmatched capabilities and expertise to foster game-changing innovation. Utilize a proven set of uniquely powerful and superior technologies. Transformed the loyalty program model into a profit and revenue center The AAdvantage program from American Airlines invented and defined rewards programs. Still the most successful on record, AAdvantage became a profit center, retention program, and a brand image enhancement. Teradata enabled innovations like balancing reward travel with paid seating, or diversifying mileage awards with outside partnerships. AAdvantage continues to extend to revenue opportunities such as Flex Awards: cash+miles purchasing. Develop sustainable advantages by partnering with an organization focused solely on enterprise analytics. Saved $1.62 billion in lost deposits, grew market share from 13% to 20% National Australia Bank, a top 50 global banking entity, developed a multi-channel relationship marketing strategy linking customer profile and behavior to event triggers. NAB reached response rates as high as 75%, 98% retention rate, and 88% referral rate within targeted segments and reduced media advertising by 75% because of the improved understanding of their customers. Leverage the knowledge and practices of an elite network of customers and partners. The Teradata Community is riveted on driving business innovation and learning Joining the Teradata community offers companies a unique opportunity to learn from the worlds most successful and innovative companies. From Amazon to Dell to Wal Mart, Teradata users are changing the shape of their industries and delivering breakthrough performance. From social networks to peer communities to the annual PARTNERS Conference the Teradata community is designing the future of business.
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Editor's Notes
Viewpoint logins will map the user to the sessions and queries that I have submitted based on account ID. When they get added to Viewpoint, there is a place in the Viewpoint profile to insert their Teradata User ID and account number. This is used to connect Viewpoint users to MyQueries sessions. These portlets are likely to be popular with the ”power users” who submit a lot of queries and understand data warehousing well. They will search for their query and drill down to see how it is progressing. The MY QUERIES portlet provides information about queries currently running in a Teradata Database system. This tool allows you to define selection criteria to display queries that have the potential to impact overall system performance. Not all queries that exceed a threshold are bad. MY QUERIES helps you determine if a query is important and useful, or just poorly written. Use MY QUERIES to view information about queries in either the summary view or the detail view. The summary view contains a table with one row allocated to each of the sessions logged on under one or more user names. Select a row in the summary view to drill down to additional session and query information in the detail view. Use one of four tabs in the detail view to display query statistics, SQL, Explain, and blocking information. The PREFERENCES: MY QUERIES view allows you to select one or more Teradata Database systems, and then select one or more users per system to monitor. From this view, you can also configure the appearance of the summary and detail views.
The capacity map allows you to quickly scroll through days or even a month of performance statistics. The red periods are times of days when the system is heavily saturated with work. This visual display can be easier and more useful than tabular reports for finding patterns of system performance and saturation. The CAPACITY HEATMAP portlet provides a visual representation of system resource utilization based on userdefined metrics over an adjustable time period. The CAPACITY HEATMAP portlet uses historical data to help identify periods during the day or week when a system is either over- or under-utilized. You can use this information to determine when to schedule resource-intensive jobs with minimal impact to other users. The CAPACITY HEATMAP portlet displays a grid with days on the horizontal axis and hours on the vertical axis. Each color-coded square represents an hour and provides a visual indication of the metric value during a 1-hour period.
Here we see the scroll bar that lets the DBA scroll backwards in time to look at system saturation on days that may be similar to one another. The and buttons allow you to adjust the CAPACITY HEATMAP date range displayed on the toolbar. When clicked, the buttons modify the date range to display 1 month or 3 months of historical data, with the previous end date remaining as the last date displayed. As you adjust the date range, the toolbar refreshes automatically to display the new date range. The (threshold button) displays the current threshold value. The purpose of the threshold value is to highlight periods when the system is over- or under-utilized. Click to open the threshold slider.
These are a subset of the functions available in TDWM. They tend to be the high use functions in many sites. With object throttles you should include users in your list of objects. A common use of object throttles today is to put one throttle on the performance group with a high concurrency limit, and then a second, different throttle on specific users within that PG that tend to submit large numbers of queries at one time, but give that a low limit, like 2.
Although you can set priorities to certain relative weight percents, in a dynamic environment the actual consumption usually will be quite different from these values. The ad hoc reporting queries and data loading tend to consume a lot of resources whereas the tactical queries and rush work are much small consumers. So even though tactical work may have a high relative weight, the work cannot consume that level of resources, so they are immediately made available to lower-priority priorities. The purpose of Priority Scheduler is to give quicker access to resources for the high priority (R & H) categories of work, even if it means making other categories of work wait longer for access to resources. Notice that the low priority jobs actually use much more CPU, memory, and disk I/O than the small real time priority tasks. This illustrates that prioritization does not equate to resource use. It only affects access to the resources in priority order. Weights and CPU usage in the ADW Benchmark Tactical queries: Relative weight of 45%...consume 12% DS queries: Relative weight of 1%...consume 77%
04/30/10 The first purpose of these slides is to show how the complexity and sophistication of questions change/increase as you add more data sources. The types of questions that can be answered with more data integration are more valuable to a company. The slides also show how combining data from data marts helps eliminate duplicate data, and moves you to more data re-use. The second purpose of these slides is to show how the number of questions you can ask increase with more data integration. The numbers in the individual pyramids represent the number of questions that can be answered from this data source in a stand alone data mart. These are subject specific questions. The numbers in ‘orange’ are the incremental number of cross-functional questions that can now be answered in addition to those specific to the data mart subject area. While the data mart questions can continue to be answered when integrated into a data warehouse, the new orange questions represent the more valuable questions that cross data sources. Notes: The numbers in the pyramids represent the number of questions/insights that a customer can get from their data; either from stand alone non-integrated data marts or from an integrated data warehouse. The numbers are either generated from the EDWr (using the iLDM), or are estimated numbers which are meant to be representative Numbers from the EDWr?: Financial yes Insurance yes Healthcare no Retail no Travel yes Trans yes Comm yes Media no Ent no Mfg no
04/30/10 The first purpose of these slides is to show how the complexity and sophistication of questions change/increase as you add more data sources. The types of questions that can be answered with more data integration are more valuable to a company. The slides also show how combining data from data marts helps eliminate duplicate data, and moves you to more data re-use. The second purpose of these slides is to show how the number of questions you can ask increase with more data integration. The numbers in the individual pyramids represent the number of questions that can be answered from this data source in a stand alone data mart. These are subject specific questions. The numbers in ‘orange’ are the incremental number of cross-functional questions that can now be answered in addition to those specific to the data mart subject area. While the data mart questions can continue to be answered when integrated into a data warehouse, the new orange questions represent the more valuable questions that cross data sources. Notes: The numbers in the pyramids represent the number of questions/insights that a customer can get from their data; either from stand alone non-integrated data marts or from an integrated data warehouse. The numbers are either generated from the EDWr (using the iLDM), or are estimated numbers which are meant to be representative Numbers from the EDWr?: Financial yes Insurance yes Healthcare no Retail no Travel yes Trans yes Comm yes Media no Ent no Mfg no
04/30/10 The first purpose of these slides is to show how the complexity and sophistication of questions change/increase as you add more data sources. The types of questions that can be answered with more data integration are more valuable to a company. The slides also show how combining data from data marts helps eliminate duplicate data, and moves you to more data re-use. The second purpose of these slides is to show how the number of questions you can ask increase with more data integration. The numbers in the individual pyramids represent the number of questions that can be answered from this data source in a stand alone data mart. These are subject specific questions. The numbers in ‘orange’ are the incremental number of cross-functional questions that can now be answered in addition to those specific to the data mart subject area. While the data mart questions can continue to be answered when integrated into a data warehouse, the new orange questions represent the more valuable questions that cross data sources. Notes: The numbers in the pyramids represent the number of questions/insights that a customer can get from their data; either from stand alone non-integrated data marts or from an integrated data warehouse. The numbers are either generated from the EDWr (using the iLDM), or are estimated numbers which are meant to be representative Numbers from the EDWr?: Financial yes Insurance yes Healthcare no Retail no Travel yes Trans yes Comm yes Media no Ent no Mfg no
04/30/10 The first purpose of these slides is to show how the complexity and sophistication of questions change/increase as you add more data sources. The types of questions that can be answered with more data integration are more valuable to a company. The slides also show how combining data from data marts helps eliminate duplicate data, and moves you to more data re-use. The second purpose of these slides is to show how the number of questions you can ask increase with more data integration. The numbers in the individual pyramids represent the number of questions that can be answered from this data source in a stand alone data mart. These are subject specific questions. The numbers in ‘orange’ are the incremental number of cross-functional questions that can now be answered in addition to those specific to the data mart subject area. While the data mart questions can continue to be answered when integrated into a data warehouse, the new orange questions represent the more valuable questions that cross data sources. Notes: The numbers in the pyramids represent the number of questions/insights that a customer can get from their data; either from stand alone non-integrated data marts or from an integrated data warehouse. The numbers are either generated from the EDWr (using the iLDM), or are estimated numbers which are meant to be representative Numbers from the EDWr?: Financial yes Insurance yes Healthcare no Retail no Travel yes Trans yes Comm yes Media no Ent no Mfg no
04/30/10 The first purpose of these slides is to show how the complexity and sophistication of questions change/increase as you add more data sources. The types of questions that can be answered with more data integration are more valuable to a company. The slides also show how combining data from data marts helps eliminate duplicate data, and moves you to more data re-use. The second purpose of these slides is to show how the number of questions you can ask increase with more data integration. The numbers in the individual pyramids represent the number of questions that can be answered from this data source in a stand alone data mart. These are subject specific questions. The numbers in ‘orange’ are the incremental number of cross-functional questions that can now be answered in addition to those specific to the data mart subject area. While the data mart questions can continue to be answered when integrated into a data warehouse, the new orange questions represent the more valuable questions that cross data sources. Notes: The numbers in the pyramids represent the number of questions/insights that a customer can get from their data; either from stand alone non-integrated data marts or from an integrated data warehouse. The numbers are either generated from the EDWr (using the iLDM), or are estimated numbers which are meant to be representative Numbers from the EDWr?: Financial yes Insurance yes Healthcare no Retail no Travel yes Trans yes Comm yes Media no Ent no Mfg no
This is a Data Overlap Analysis which is specific to this industry. It was developed using the data from our iEDWr. With information like this, you could analyze your upcoming projects; and all things equal, you can determine which projects need less data added to the warehouse which could mean faster time to implement and lower overall risk.
This is a Data Overlap Analysis which is specific to this industry. It was developed using the data from our iEDWr. With information like this, you could analyze your upcoming projects; and all things equal, you can determine which projects need less data added to the warehouse which could mean faster time to implement and lower overall risk.
04/30/10 Data warehouses usually follow a predictable evolution. After nearly 30 years, we’ve seen the stages companies go through on their path to enterprise data warehousing. Moving from Stage 1 (What Happened?) into Stage 2 (Why Did It Happen?) requires new capabilities for ad hoc analysis. Then as you evolve to Stage 3 (Predicting What Will Happen) you again grow in your platform and database requirements. As you cross the chasm into Stages 4 and 5, (Operational Intelligence) you require a platform capable of “active” analysis.
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
As you can see from the broad coverage of the data warehouse requirements here, the Teradata Platform Family has a platform family member to support a the diverse needs within all the stages of data warehousing
In the next few releases of Teradata software and hardware we are moving to an architecture that is both cheaper for our customers and faster because of multi-temperature data. Its cheaper because we allow clients to mix disk drives attached to a server node. Some are faster with less data in them so that the ratio of I/Os per second to gigabytes favors faster response time per gigabyte. There will also be larger disk drives that cost less per gigabyte but have a less attractive I/Os per gigabyte. As we move forward, we will also include solid state disk units that will be able to feed data to the incredibly fast Intel microprocessors at speeds more in line with silicon than spinning disks. The key to this slide if the automated migration. Clients can do some of this today on non-Teradata systems, but it brings a host of problems. At minimum, its labor intensive requiring constant care and attention from the DBAs. But at its worst, clients can easily un-balance their configurations making query response times inconsistent, hard to troubleshoot, and horrible to do capacity planning.
In the next few releases of Teradata software and hardware we are moving to an architecture that is both cheaper for our customers and faster because of multi-temperature data. Its cheaper because we allow clients to mix disk drives attached to a server node. Some are faster with less data in them so that the ratio of I/Os per second to gigabytes favors faster response time per gigabyte. There will also be larger disk drives that cost less per gigabyte but have a less attractive I/Os per gigabyte. As we move forward, we will also include solid state disk units that will be able to feed data to the incredibly fast Intel microprocessors at speeds more in line with silicon than spinning disks. The key to this slide if the automated migration. Clients can do some of this today on non-Teradata systems, but it brings a host of problems. At minimum, its labor intensive requiring constant care and attention from the DBAs. But at its worst, clients can easily un-balance their configurations making query response times inconsistent, hard to troubleshoot, and horrible to do capacity planning.
In the next few releases of Teradata software and hardware we are moving to an architecture that is both cheaper for our customers and faster because of multi-temperature data. Its cheaper because we allow clients to mix disk drives attached to a server node. Some are faster with less data in them so that the ratio of I/Os per second to gigabytes favors faster response time per gigabyte. There will also be larger disk drives that cost less per gigabyte but have a less attractive I/Os per gigabyte. As we move forward, we will also include solid state disk units that will be able to feed data to the incredibly fast Intel microprocessors at speeds more in line with silicon than spinning disks. The key to this slide if the automated migration. Clients can do some of this today on non-Teradata systems, but it brings a host of problems. At minimum, its labor intensive requiring constant care and attention from the DBAs. But at its worst, clients can easily un-balance their configurations making query response times inconsistent, hard to troubleshoot, and horrible to do capacity planning.
In the next few releases of Teradata software and hardware we are moving to an architecture that is both cheaper for our customers and faster because of multi-temperature data. Its cheaper because we allow clients to mix disk drives attached to a server node. Some are faster with less data in them so that the ratio of I/Os per second to gigabytes favors faster response time per gigabyte. There will also be larger disk drives that cost less per gigabyte but have a less attractive I/Os per gigabyte. As we move forward, we will also include solid state disk units that will be able to feed data to the incredibly fast Intel microprocessors at speeds more in line with silicon than spinning disks. The key to this slide if the automated migration. Clients can do some of this today on non-Teradata systems, but it brings a host of problems. At minimum, its labor intensive requiring constant care and attention from the DBAs. But at its worst, clients can easily un-balance their configurations making query response times inconsistent, hard to troubleshoot, and horrible to do capacity planning.
04/30/10 This is a simplification of the Active Data Warehouse. The ADW begins with a Teradata data warehouse foundation. There are 6 elements that are used when activating your data warehouse: Active Access, Active Events, Active Load, Active Enterprise Integration, Active Workload Management, and Active Availability. Each of these are applied at different levels of investment depending ont eh maturity of the current data warehouse, the application being activated, and the users.
Teradata has a Virtualization Lab in San Diego Many Teradata products run on VMWare today We are working with VMWare R&D to optimize MPP and EDW workloads Reduce VMWare CPU tax Enhance VMWare support for EDW I/O configurations Enable MPP configurations Once VMWare is optimized for EDW workloads, it can be used in a single node configuration No release date for Teradata on top of VMWare in MPP configurations at this time
Why Teradata? Teradata has significant advantages over competitors in the Teradata 2500 space. We offer a single analytical database standard for all of your data warehousing needs. This means that you can leverage all of your existing resources and tools that have already been trained. It also mean one stop shopping and support for all of you data warehousing needs. No matter what your analytical system requirements are Teradata has you covered from entry level SMP to EDW/ADW implementations. All of this with simple ability to migrate applications into the Teradata EDW. [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{FFDDBF73-709E-4B8A-9605-1AF4E8AFFA6A}}
Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
OLAP (On-line Analytic Processing) is a tool that allows users to perform speed-of-thought analysis of data using business terminology. Speed of thought means responses are returned in a timely manner so it doesn’t disrupt the analytic thought process. Meaning OLAP tool users expect results in a matter of seconds, at most, minutes. OLAP tools also represents data using common business terms by defining dimensions in such time, location, product line, etc. And also allows users to drill down to different levels of the hierarchy in each dimension. For example for the time dimension, they can view at the higher levels such as year, quarter, month, or lower levels such as week and days. All OLAP technologies provide the user with an intuitive multi-dimensional view, allowing the user to analyze data based on multiple dimensions and at varying levels of detail, however, the underlying implementation can vary. The implementations typically vary based on two key factors: The type of data: the depth or levels of hierarchy within each dimension. Aggregated data vs. detailed data. Data store: Where the physical data resides. Is it in a physical cube on a server or leverage the data in a relational database. Let’s quickly examine the different implementation and how Teradata can optimize each. First we have the physical cube also referred to as MOLAP (Multi-dimensional OLAP). All dimensions and levels of your OLAP content are extracted, pre-calculated and stored in a cube - multi-dimensional database for analysis. In this case, Teradata is typically used as a data store and the data is extracted and moved into an OLAP server. The strengths of MOLAP is the quickness in response because the data is pre-calculated and available on the server, but that comes with a cost: maintenance and scalability Moving toward the right, is the ROLAP implementation. In this implementation relies heavily on the database engine. User request are submitted to the OLAP engine in the mid-tier which may house metadata on the server, however the processing is done in the database. The OLAP engine will convert the request into a SQL query that’s processed in the database. The benefits of the ROLAP implementation are lower maintenance cost and scalability in terms of breadth (in terms of more dimensions) and depth of analysis (more levels). An then there’s the hybrid implementation HOLAP. HOLAP leverages the strengths of MOLAP and ROLAP, where some dimensions and levels of your OLAP content are extracted and stored in a cube for analysis and drills-down into a relational database for detail analysis. Teradata optimizations that are done for MOLAP and ROLAP can be applied to HOLAP. For our partner technology, this is the best compromise solution. Our message is a complementary that we optimize all OLAP implementations. We’re in the process of developing technical training that will cover AJI implementation with a shorter segment for each of our key partners.
In order to optimize the performance for BI solutions it is advisable to make sure the data architecture supporting the BI solution is designed to take advantage of the built-in Teradata features. For OLAP solutions Teradata provides a key optimization technology called Aggregate Join Index (AJI), building and deploying AJIs is a manual process and requires understanding of the BI solution and the data architecture. TBIO is designed to automate this process and to recommend and build the necessary AJIs. TBIO consists of the following: Schema Workbench is a desktop designer tool and is used to manually build the schema definition for BI tools that are not compatible with the Aggregate Designer. In the initial release Microsoft SQL Server Analysis Services will be the only BI tool to produce the compatible schema definitions but we are working with other BI vendors on automating this process. Aggregate Designer is a desktop tool used to read/consume the schema definitions and recommends/builds the required AJIs assuming they meet the AJI requirements. OLAP Connect is used by Excel or MDX-based tools to connect to Teradata
In order to optimize the performance for BI solutions it is advisable to make sure the data architecture supporting the BI solution is designed to take advantage of the built-in Teradata features. For OLAP solutions Teradata provides a key optimization technology called Aggregate Join Index (AJI), building and deploying AJIs is a manual process and requires understanding of the BI solution and the data architecture. TBIO is designed to automate this process and to recommend and build the necessary AJIs. TBIO consists of the following: Schema Workbench is a desktop designer tool and is used to manually build the schema definition for BI tools that are not compatible with the Aggregate Designer. In the initial release Microsoft SQL Server Analysis Services will be the only BI tool to produce the compatible schema definitions but we are working with other BI vendors on automating this process. Aggregate Designer is a desktop tool used to read/consume the schema definitions and recommends/builds the required AJIs assuming they meet the AJI requirements. OLAP Connect is used by Excel or MDX-based tools to connect to Teradata
We’ve discussed the what are AJIs and the benefits and now I’d like to share actual results of a customer implementation. This proof of concept was implemented to measure the ability and impact of usin gthe Teradata database to directly support OLAP analysis at one of our wireless customers. This customer originally started with a MOLAP implementation for their Performance Assurance Reporting suite. They implemented up to 38 dimensions and 24 measures which is one of the largest MOLAP implementations or cubes at this sight. The cube was over 22GB in size and monthly data transfer of 334 MB of data. It took approximately 13 hours to refresh the entire cube. And the lowest level of detailed available in this cube was at the monthly level. In addition to demonstrating the performance improvement, this customer also wanted to add another dimension to their OLAP cube however this exceeded architecture and hardware limitation and was able to deliver a partial solution with only 20 of the 4857 wire center values. This 4 day project including the time to capture and compare results, resulted in faster build and maintenance of the cube, ability to reduce data & redundant data mart, provided the scalability required to add the 39 th dimension, provided more detailed data and history and provided faster query response. As you can see from the query times, OLAP optimized with Teradata was significantly faster. We compared a set of 5 canned queries and 6 interactive queries with and without the 39 th dimension (Wire Center). The red indicates the times it took for using an OLAP server without optimization and the times in orange indicates time after pushing the processing into Teradata leveraging AJI. Of course we were unable to compare the query time w/wire centers because they were unable to add this dimension in the cube, but I’ve included this just to illustrate that the performance doesn’t vary much by adding another dimension. That’s the beauty of Teradata’s scalability and parallelism.
Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
Our technique is rather simple. Instead of bring the mountain of data to the data mining server as illustrated here, we bring the analytic functions to the mountain of data. The illustration on the top demonstrates the typically analytic environment where the data is extracted from the database on to the analytic server. Since data mining tasks, especially the data preparation tasks are data intensive and highly iterative, this architecture requires massive data movement which becomes the bottleneck in the data mining process. The processing is done on the server and once the results are available either as part of the data preparation or modeling phase, the results must then be written back into the warehouse for integration. With Teradata’s in-database technique, the data preparation, algorithms and the models are actually converted to SQL and pushed back into the database. The SQL is then processed in PARALLEL directly in the database and the results are written in the database. Our customers have found that in-database processing is significantly faster than the server based approach. On the average, performance improvements have been to the magnitude of 25, so tasks that took 25 minute now take less than 1 minute. Teradata takes an unique approach to analytic by pushing analytics into the database. Instead of bringing the data to the server, Teradata converts the ETL and analytic processing into optimized SQL then pushes the processing into Teradata.
They built an enterprise ADS for risk analytics on Teradata. This immediately improved quality because of the consistency of data across all users. They also accelerated their development cycles. When hurricane Katrina hit, they were able to build a hurricane exposure analysis within 90 minutes providing their executives with information regarding their customers affected by the hurricane. They were able to extend credit and provide special offers to customers to help their customers with the recovery process. Previously, this same task would have taken over 3 weeks. Creating an Risk ADS and allowing their users to direct access to Teradata provided greater efficiency… greater than expected. Last year, the Risk team developed 40 models, and this year with the same resources, they’re on target to develop 500 analytic models.
Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
Teradata’s Geospatial implementation differs from other environments because most Geographic information systems are implemented as departmental data marts that are loosely, if at all integrated with their EDW. The data marts integrate geographic coordinates, geographic data such as Hazard data and a subset of the business data. Business intelligence environments are separated from the GIS enviornment
Next question is what can I do with these new types? We offer four classes of functions. Measurement queries: the distance between entities, the surface or the perimeter of an area, etc. These queries return a numeric value. Topological relations between two objects such as A intersects B, A contains B, A is within B, A is adjacent to B etc. These queries return a 0 for false or 1 for true. Examples of relevant query methods are: Single object spatial relations with other objects - retrieve all the objects located within the object A, touch object B etc. These queries return a list of objects. Examples of relevant query methods are: Object attribute queries - object type, number of points etc. These queries return attribute values. Examples of relevant query methods are: What are the real-world applications? Points can be represented on a map to indicate specific location such as stores, customers and competitors. You can measure the distance between the customer and your store. Click : You can also identify a 3 mile radius from your store and identify the relationship with the sales region of your competitor. Click : We can also retrieve objects that are contained within a region identifying the customers that are located in overlapping areas.
Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
This is an internal private cloud hosted inside the Teradata Server Provides self service, capacity on demand, and multi-tenancy Mixed workload management is a critical success factor The Viewpoint portlet source code is free This solution is good for Ad hoc and short term analysis Sand boxes, data miners, POC, developers Value discovery in the data and bring into EDW Controlling data mart proliferation You know where all the marts are Business users get what they need
This is an internal private cloud hosted inside the Teradata Server Provides self service, capacity on demand, and multi-tenancy Mixed workload management is a critical success factor The Viewpoint portlet source code is free This solution is good for Ad hoc and short term analysis Sand boxes, data miners, POC, developers Value discovery in the data and bring into EDW Controlling data mart proliferation You know where all the marts are Business users get what they need
This is simplified mockup of the workflow in the Elastic data mart life cycle. A GUI screen helps allocate perm space for a user and establishes security permissions for them. Once the perm space is built, the user can login and upload data sets into new tables. Since they are expected to use comma separated values format, the column names can be used to organize columns and to select data types and primary indexes. Once the data layout is accepted by the user, the data file can be loaded into the new table. Using a simple SQL interface, the user can also create, delete or modify tables. Based on their security permissions, they can also copy data from production data warehouse tables into the elastic mart. Creating a mart or sand box simply allocates a database to that UserID Here we upload a file, usually a spreadsheet of account numbers or products or whatever the user wants to analyze. 3) The portlet helps the user define the column names and data types of the CSV data, The data is then imported into the database created in step 1 Now the user can use joins or insert select statements to pull data from the production database into the mart or sand box. If the data needs to be modifiable, then the mart must have enough disk space to hold the tables that will be created from production data. If not, then the mart need only contain the control tables, that is not copy production data into the mart or sandbox.
Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
Teradata’s strategic intelligence portfolio includes a combination of Teradata and partner products to provide customer with the best in class analytic technology. Teradata has established R&D partnerships with all partners listed on this slide to ensure optimization of technologies. Many of these tools support other databases, however Teradata’s centralized environment and best practices optimize the project these tools try to do. Integration with Teradata may differ from product to product. Reporting and OLAP partners. These partners provide Teradata customers with the ability to create reports and analyze data via OLAP tools. Although many of these tools provide data mining, data visualization and ad hoc querying functionality, however the core focus of these tools is to provide reporting, OLAP and ad hoc querying capabilities. Teradata and Microsoft have entered into a partnership where Microsoft will develop a Teradata cartridge to push optimal SQL back into Teradata for processing. Microsoft’s product, SQL Server 2005’s Analysis Services and Reporting services are the two components that fit into the Reporting and OLAP space. Advanced Visualization Partners: Advanced visualization is beyond the simple graphs available in many of the tools listed on slide, but provides everything from real-time analytics to geospatial graphics. Data Mining partners and products provide Teradata customers with the ability to develop predictive and descriptive analytics to improve analytical applications. Text Analytics provides the capability of analyzing, decoding and loading unstructured text data into a relational format to merge with detailed data within the database.
Our vision for how companies can do this is by being capable of operating with Active Enterprise Intelligence
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[Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{B180C764-08E0-4878-85C4-706538E48745}}
Retail Fraud is a $16 B year problem in the USA alone. With web receipts and better copying capabilities, thieves can make multiple copies of a single receipt and make multiple returns for cash or other merchandise. Or they can bring back shoplifted items and try to exchange for cash. The problem is that often the associates in Returns department don’t have access to past sales information and can’t keep track easily of returned merchandise. This is especially problematic if the policy is to make returns without receipts. So the solution is straightforward: hook up the Point of Sale systems so within seconds, the Teradata data warehouse is updated with sales, return, exchange, and void data, and provide the Returns department with the entire history of purchases by that customer,, so they can ensure that a sold product can only be returned once. <Click> The impact? Huge, according to one Teradata customer who has already built this system. They stopped a crime ring in the first day of their rollout, a group that had defrauded the company of thousands of dollars. They saw a 100% payback on their investment in just 5 months, and continue to reap the benefits of this example use of Active Enterprise Intelligence. [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{70C11B6E-24EE-4FD8-BEBE-FBCB0DD1152C}}
[Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{105DCFB0-BB36-405F-9D32-9E1A3CF1E66B}}
Retail Fraud is a $16 B year problem in the USA alone. With web receipts and better copying capabilities, thieves can make multiple copies of a single receipt and make multiple returns for cash or other merchandise. Or they can bring back shoplifted items and try to exchange for cash. The problem is that often the associates in Returns department don’t have access to past sales information and can’t keep track easily of returned merchandise. This is especially problematic if the policy is to make returns without receipts. So the solution is straightforward: hook up the Point of Sale systems so within seconds, the Teradata data warehouse is updated with sales, return, exchange, and void data, and provide the Returns department with the entire history of purchases by that customer,, so they can ensure that a sold product can only be returned once. <Click> The impact? Huge, according to one Teradata customer who has already built this system. They stopped a crime ring in the first day of their rollout, a group that had defrauded the company of thousands of dollars. They saw a 100% payback on their investment in just 5 months, and continue to reap the benefits of this example use of Active Enterprise Intelligence. [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{70C11B6E-24EE-4FD8-BEBE-FBCB0DD1152C}}
Before process – very labor intensive, not scalable, error-prone
New process through multiple channels into a centralized customer database (using Teradata). The (realtime) scoring done via SPSS based on data feed from Teradata. Processing time cut from six working days to 30 sec for retail and to 2 minutes for commercial banking.
The credit decisioning challenge was around three key dimensions: Need to automate to improve efficiency Data consolidation and external sourcing to collect info about customers (in the Baltic, very few people have credit history) Realtime credit scoring ability due to the need to automate
This large brand-name consumer travel company uses both the web and email to provide vacation bundle offers. The situation was that the reaction rates for both of these were not meeting goals. The main problem was that the offers were too “generic”, e.g., not relevant for most consumers. The solution was to use information from Teradata about the customer web browsing behavior to better calibrate the offers – both on the web and via emails. <Click> The impact was outstanding – a 3-5 times improvement in the customer “take rates”. In addition, the compmany found that they were able to provide customers with more focused web offers using their existing system – 1.3M personalization queries per day created only a 3% increase in the workload for the system. More info: Solution: Leveraged the Teradata Active Data Warehouse capability to display customer specific ads during a shopping visit. Industry studies have shown that personalized offers which are compelling to the customer and presented at the keenest time of interest have response rates up to 12 times that of non-specific offers. Impact: Personalized offers are much more effective than generic offers. Should the warehouse not be available, the fall back position is a generic offer; which doesn’t stop business, just reduces the effectiveness of the ads to what they were before personalization. Leveraged existing data in the warehouse enabled rapid implementation. Tactical single amp queries consumed only a tiny portion of the system, even though the volume is high. [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{8B595D92-4D1A-4551-87D7-3B5AA0921C33}} Situation: E-mail campaigns, while well targeted, were not gaining in effectiveness in part due to becoming more and more of the marketing noise that consumers get hit with. Generic offers displayed during a shopping session on the web site were only achieving the low response rates typical in the industry. Problem: Generic offer displays did not leverage the detailed customer information contained in the data warehouse. It would take thinking outside of the box to connect the web site, a mission critical customer facing application, to the Teradata data warehouse in order to leverage the detailed customer information in real time.
This page came from the PARTNERS 2006 presentation Booking Services: this is a transaction to book this trip choice. Originally this was called “looking and booking” because many times people search the site but don’t buy, especially for people that they already know via cookies. All of this is captured in the operational systems that run the website. Capturing click stream information to determine propensities to purchase in the future. So, if a deal was served up, we also have to record the deal at that moment because the same deal may be offered to 100s of people – all at different prices. It is therefore necessary to capture the click stream information and save the summary of key values in the stream to be sure what was offered at run time. Merchandising services is a layer of processes to do the active intelligence. From consumer requests, I can bring back the correct deal. This is where we find the customer matching process. The streaming (preset times daily – different for the type of data being loaded) and batch (overnight). They do this to assure they don’t bombard the EDW during prime times with data loads. Prime time is “daytime in the US”. They do a lot of the batch work in the evening or night. [Enter any extra notes here; leave the item ID line at the bottom] Avitage Item ID: {{C8E81D4E-895F-4B85-9452-253B1674D205}}
Teradata’s goal is to help you transform Insight into Actions We do that by providing world-class database technology to create both Strategic and Operational intelligence. By strategic intelligence, we mean the insights that back-office workers create by using BI tools and analytic packages to build reports, write ad-hoc analytical queries, create predictive models, and then use these insights to create pre-planned customer dialogues and plans. Cycle times for developing these kinds of strategic insight activities take typically hours or days, sometimes weeks. By contrast, operational intelligence is the application of strategic intelligence at the front-line, at the point of customer contact. It is the application of historical context, current state, and predictive insights like the next best offer, specialized for one specific customer – the one standing in front of a bank teller, or at a check-in kiosk, or checking out of a retail store on the web or in person. In this case, the operational systems sense inputs (what does the customer want), assess and compare the inputs to the dialogue, and use that to drive a timely and relevant response. Cycle times for operational intelligence are fast – each step of the response may take under a second. For example, it could be a dialogue sequence of customized screen pages for a call center agent to use, a sequence of web pages to be rendered for that customer as she clicks, or a sequence of screens that a bank teller might see. You will go around this cycle numerous times on the right within one session with a customer. Then the customer walks away, and the results are captured and uploaded from the frontline system back into the database for further analysis. The insight cycle begins again, perhaps after hundreds or thousands of customers have interacted with the company. Changes are noted. By analyzing the data, refining the insights and dialogues, you can make them better for use the next time.
Lead: Businesses look to agility for differentiation, but it can be elusive. Study: The Economist (Intelligence Unit), Organizational agility: how businesses can survive and thrive in turbulent times, March 2009 (Conducted early 2009, 349 business executives around the world (UK, France, Germany, Singapore, US, Australia, New Zealand, Canada), 19 industries) Executives also said that Nearly 90% of executives surveyed believe that organizational agility is critical for business success. Organizational agility is a core differentiator in today’s rapidly changing business environment. Agility is essential to sustaining a strong competitive edge. It is and will be a core differentiator. One-half of all chief executive officers (CEOs) and chief information officers (CIOs) polled agree that rapid decision-making and execution are not only important, but essential to a company’s competitive standing. More than 80% of corporate executives have undertaken one or more change initiatives to improve agility over the past three years alone. More than a third, 34%, say the efforts have failed. On performance, the Massachussetts Institute of Technology (MIT) says: Agile organizations increase their revenue 37% faster than non-agile organizations. And, that they generate 30% higher profits from the revenue they pull in. Transition: The recent economic crisis has businesses rethinking the importance of agility.
It’s an organization that exhibits the agility to be able to: quickly gather intelligence creatively and nimbly act or react take advantage of changing conditions. move with strength or power And How Agile is your organization? Is their functional transparency throughout? Has consistency infiltrated every corner? Do you move faster? Can the organization handle More…always? Is there a means of readily seeing through the complexities? Are your strategies breakthrough? Is innovation part of the culture? Can you recall the last time resistance won out over change?
Why Teradata? Teradata enables organizations to be more intelligent, be more powerful, and create breakthrough strategies for their business . Intelligent It’s not just about making better decisions – it’s making the best decisions on the right issues to impact business performance. Intelligence that breeds the best decisions, breakthrough innovation, and decisive actions will deliver maximum value in the short and long term. Teradata enables a new visibility to your business by eliminating the black holes and delivering consistent insight across user functions and roles. Just look at JCPenney for example, during the worst stretch of retail downturn in our lifetime, was able to increase operating income by $63M+ eliminating black holes and tying sales trends to inventory. Powerful Today’s Businesses need power to outpace competition, handle more, and conquer the complexities of today’s environment. Teradata makes companies more powerful with: Our Unmatched speed in data mining from 100% parallel design An Architectural flexibility that adapts to the technical differences of your business A Storage capacity and accessibility that provides answers regardless of size or diversity of your business Optional – Look atFor example, download giant Real Networks replaced their massive royalty payment process by employing an existing Teradata data model to achieve better results faster and in the process created a view into lifetime customer value. Breakthrough Teradata creates new breakthrough strategies for your business, true category changing innovation, advantages sustainable over time. Teradata does this by allowing you to: Use intelligence and powerful solutions to feed a culture of innovation Partner with a professional support team steeped in industry expertise Network with other customers who lead their industries Optional - For example, in a tough industry Enterprise Rent-A-car gained competitive advantage by streamlining information delivery to the various parties involved in repairs and service… to provide an better experience for customers.