Preview this Big Data Seminar, and request the complete audio and animated download featuring Agile Data Warehouse Design - a step-by-step method for data warehousing / business intelligence (DW/BI) professionals to better collect and translate business intelligence requirements into successful dimensional data warehouse designs. The method utilizes BEAM✲ (Business Event Analysis and Modeling) - an agile approach to dimensional data modeling that can be used throughout analysis and design to improve productivity and communication between DW designers and BI stakeholders. a2c's Practice Director of Information Services and Author Jim Stagnitto and CTO John DiPietro designed this presentation to provide an overview of Agile Warehouse Design that will facilitate communication between Data Modelers and Business Intelligence Stakeholders in a fun and informative one hour session. Demystify this process and find out what the 96 Data Scientists who attended November's Boston Big Data Meet-up are talking about.
“Excellent presentation. It is good to hear meaningful …information about new developments in how Agile methodologies can be applied to DW/BI work. Big Kudos to the presenters and organizers. Thanks, I found it very useful and enjoyable.”- Ramon Venegas
“Extremely useful to understand how to apply Agile approach to DWH; how create a framework where model changes are welcome, and bring users to the process of DWH modeling.” – Alfredo Gomez
This document discusses agile data warehouse design. It begins with an overview of data warehousing, including definitions of a data warehouse and common architectures. It then covers traditional waterfall and agile approaches to BI/WH development. The agile section focuses on an incremental lifecycle and agile dimensional modeling techniques like the 7Ws framework and BEAM methodology, which use natural language and collaboration to design models around business questions.
This document discusses Data Vault fundamentals and best practices. It introduces Data Vault modeling, which involves modeling hubs, links, and satellites to create an enterprise data warehouse that can integrate data sources, provide traceability and history, and adapt incrementally. The document recommends using data virtualization rather than physical data marts to distribute data from the Data Vault. It also provides recommendations for further reading on Data Vault, Ensemble modeling, data virtualization, and certification programs.
A strong relationship with the founder
of Data Vault for over 3 years now.
Supporting your business with 40+
certified consultants.
Incorporated as the preferred
Enterprise Data Warehouse modelling
paradigm in the Logica BI Framework.
Satisfied customers in many countries
and industry sectors
Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...DATAVERSITY
Join Basho technologies and Databricks, creators of Apache Spark, as we share lessons learned by both organizations in building scalable applications for IoT and time series use cases. We'll be discussing some of the data modeling considerations unique to time series data and some of the key factors developers and architects need to take into consideration as data moves through the pipeline. You'll learn:
Challenges in building apps to leverage data being generated by IoT devices
What you need to think about before you start modeling your IoT data
Shortcuts to success in building IoT apps
The webinar will also give a live demonstration of how to store and retrieve IoT data as well as a demonstration of integrated data store with analytics engine using a live Notebook as a guide.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data Lake, Virtual Database, or Data Hub - How to Choose?DATAVERSITY
Data integration is just plain hard and there is no magic bullet. That said, three new data integration techniques do ameliorate the misery, making silo-busting possible, if not trivial. The three approaches – data lakes, virtual databases (aka federated databases), and data hubs – are a boon to organizations big enough to have separate systems, separate lines of business, and redundant acquired or COTS data stores. Each approach has its place, but how do you make the right decision about which data silo integration approach to choose and when?
This webinar describes how you can use the key concepts of data Movement, Harmonization, and Indexing to determine what you are giving up or investing in, and make the best decision for your project.
Best Practices for Building a Warehouse QuicklyWhereScape
Key factors that influence a successful data warehouse task are:
+ Implementing the True Development Approach
+ Choosing a Rapid Development Product
+ Ensuring Data Availability
+ Involving Key Users throughout the whole project
+ Relying on a Pragmatic Governance Framework
+ Utilizing experienced Team Members
+ Selecting the right Hardware, Infrastructure Technology
Best Practices: Datawarehouse Automation Conference September 20, 2012 - Amst...Erik Fransen
The document discusses best practices for data warehouse automation. It covers challenges organizations face with business intelligence (BI), how data warehouse (DWH) automation can help address these challenges, and the Centennium BI Ability Model for DWH automation. Case studies of successful DWH automation projects at Rotterdam and KAS BANK are provided. The presentation also outlines the Centennium Methodology (CDM) for DWH automation best practices and concludes with information about Centennium as an independent BI expertise organization.
This document discusses agile data warehouse design. It begins with an overview of data warehousing, including definitions of a data warehouse and common architectures. It then covers traditional waterfall and agile approaches to BI/WH development. The agile section focuses on an incremental lifecycle and agile dimensional modeling techniques like the 7Ws framework and BEAM methodology, which use natural language and collaboration to design models around business questions.
This document discusses Data Vault fundamentals and best practices. It introduces Data Vault modeling, which involves modeling hubs, links, and satellites to create an enterprise data warehouse that can integrate data sources, provide traceability and history, and adapt incrementally. The document recommends using data virtualization rather than physical data marts to distribute data from the Data Vault. It also provides recommendations for further reading on Data Vault, Ensemble modeling, data virtualization, and certification programs.
A strong relationship with the founder
of Data Vault for over 3 years now.
Supporting your business with 40+
certified consultants.
Incorporated as the preferred
Enterprise Data Warehouse modelling
paradigm in the Logica BI Framework.
Satisfied customers in many countries
and industry sectors
Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...DATAVERSITY
Join Basho technologies and Databricks, creators of Apache Spark, as we share lessons learned by both organizations in building scalable applications for IoT and time series use cases. We'll be discussing some of the data modeling considerations unique to time series data and some of the key factors developers and architects need to take into consideration as data moves through the pipeline. You'll learn:
Challenges in building apps to leverage data being generated by IoT devices
What you need to think about before you start modeling your IoT data
Shortcuts to success in building IoT apps
The webinar will also give a live demonstration of how to store and retrieve IoT data as well as a demonstration of integrated data store with analytics engine using a live Notebook as a guide.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data Lake, Virtual Database, or Data Hub - How to Choose?DATAVERSITY
Data integration is just plain hard and there is no magic bullet. That said, three new data integration techniques do ameliorate the misery, making silo-busting possible, if not trivial. The three approaches – data lakes, virtual databases (aka federated databases), and data hubs – are a boon to organizations big enough to have separate systems, separate lines of business, and redundant acquired or COTS data stores. Each approach has its place, but how do you make the right decision about which data silo integration approach to choose and when?
This webinar describes how you can use the key concepts of data Movement, Harmonization, and Indexing to determine what you are giving up or investing in, and make the best decision for your project.
Best Practices for Building a Warehouse QuicklyWhereScape
Key factors that influence a successful data warehouse task are:
+ Implementing the True Development Approach
+ Choosing a Rapid Development Product
+ Ensuring Data Availability
+ Involving Key Users throughout the whole project
+ Relying on a Pragmatic Governance Framework
+ Utilizing experienced Team Members
+ Selecting the right Hardware, Infrastructure Technology
Best Practices: Datawarehouse Automation Conference September 20, 2012 - Amst...Erik Fransen
The document discusses best practices for data warehouse automation. It covers challenges organizations face with business intelligence (BI), how data warehouse (DWH) automation can help address these challenges, and the Centennium BI Ability Model for DWH automation. Case studies of successful DWH automation projects at Rotterdam and KAS BANK are provided. The presentation also outlines the Centennium Methodology (CDM) for DWH automation best practices and concludes with information about Centennium as an independent BI expertise organization.
Analyst Webinar: Best Practices In Enabling Data-Driven Decision MakingDenodo
Watch full webinar here: https://bit.ly/37YkgN4
This presentation looks at the trends that are emerging from companies on their journeys to becoming data-driven enterprises.
These trends are taken from a survey of 500 companies and highlight critical success factors, what companies are doing, their progress so far and their plans going forward. It also looks at the role that data virtualization has within the data driven enterprise.
During the session we'll address:
- What is a data-driven enterprise?
- What are the critical success factors?
- What are companies doing to create a data-driven enterprise and why?
- What progress are they making?
- What are the plans on people, process and technologies?
- Why is data virtualization central to provisioning and accessing data in a data-driven enterprise?
- How should you get started?
Slides: Proven Strategies for Hybrid Cloud Computing with Mainframes — From A...DATAVERSITY
Mainframes continue to perform mission-critical transaction processing and contain massive amounts of core business data. But digital transformation initiatives and cloud computing have created both opportunities and challenges for unlocking and utilizing this data. Qlik and AWS will share some of the proven strategies from successful customer deployments across a range of different mainframe to cloud use cases, including legacy application modernization, data analytics, and data migrations.
In this presentation, you will learn how to:
• Replicate very large volumes of mainframe data in real-time to the cloud
• Automate the creation of analytics-ready data lakes and data warehouses
• Achieve a 30% reduction in cost of compute
Agile BI via Data Vault and ModelstormingDaniel Upton
Audience: Business Intelligence Architects, Project Managers and Sponsors. This slideshow accompanies a video presentation of the same name, available at http://youtu.be/e0cHFdeGEeE.
Slides: Accelerating Queries on Cloud Data LakesDATAVERSITY
Using “zero-copy” hybrid bursting on remote data to solve data lake analytics capacity and performance problems.
Data scientists want answers on demand. But in today’s enterprise architectures, the reality is that most data remains on-prem, despite the promise of cloud-based analytics. Moving all that data to the cloud has typically not been possible for many reasons including cost, latency, and technical difficulty. So, what if there was a technology that would connect these on-prem environments to any major cloud platform, enabling high-powered computing without the need to move massive amounts of data?
Join us for this webinar where Alex Ma of Alluxio, an open-source data orchestration platform, will discuss how a data orchestration approach offers a solution for connecting traditional on-prem data centers and cloud data lakes with other clouds and data centers. With Alluxio’s “zero-copy” burst solution, companies can bridge remote data centers and data lakes with computing frameworks in other locations, enabling them to offload, compute, and leverage the flexibility, scalability, and power of the cloud for their remote data.
IDERA Slides: Managing Complex Data EnvironmentsDATAVERSITY
Companies are expanding their information systems beyond relational databases to incorporate big data and cloud deployments, creating hybrid configurations. Database professionals have the challenges of managing multiple data sources and running queries for analytics against diverse databases in these complex environments.
IDERA’s Lisa Waugh will discuss how to deal with the growing challenges of having data residing on different database platforms by using a single IDE.
The document discusses data warehouse concepts and architecture. It defines a data warehouse as a structured repository of historic data that is subject oriented, integrated, time variant, and non-volatile. It contains business specified data to answer business questions. The document outlines why data warehouses are needed, how they are developed through operational data stores and data marts, and how they can be used for strategic reporting and trend analysis rather than replacing operational systems.
The Business Data Lake is a new approach to information management, analytics and reporting that better matches the culture of business and better enables organizations to truly leverage the value of their information.
ADV Slides: Data Pipelines in the Enterprise and ComparisonDATAVERSITY
Despite the many, varied, and legitimate data platforms that exist today, data seldom lands once in its perfect spot for the long haul of usage. Data is continually on the move in an enterprise into new platforms, new applications, new algorithms, and new users. The need for data integration in the enterprise is at an all-time high.
Solutions that meet these criteria are often called data pipelines. These are designed to be used by business users, in addition to technology specialists, for rapid turnaround and agile needs. The field is often referred to as self-service data integration.
Although the stepwise Extraction-Transformation-Loading (ETL) remains a valid approach to integration, ELT, which uses the power of the database processes for transformation, is usually the preferred approach. The approach can often be schema-less and is frequently supported by the fast Apache Spark back-end engine, or something similar.
In this session, we look at the major data pipeline platforms. Data pipelines are well worth exploring for any enterprise data integration need, especially where your source and target are supported, and transformations are not required in the pipeline.
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it.
https://www.brighttalk.com/webcast/18317/422499
ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...DATAVERSITY
This webinar will focus on the promise AI holds for organizations in every industry and every size, and how to overcome some of the challenges today of how to prepare for AI in the organization and how to plan AI applications.
The foundation for AI is data. You must have enough data to analyze to build models. Your data determines the depth of AI you can achieve – for example, statistical modeling, machine learning, or deep learning – and its accuracy. The increased availability of data is the single biggest contributor to the uptake in AI where it is thriving. Indeed, data’s highest use in the organization soon will be training algorithms. AI is providing a powerful foundation for impending competitive advantage and business disruption.
Business Intelligence Solution on Windows AzureInfosys
The document discusses a proposed cloud-based business intelligence (BI) solution on Microsoft Azure. It outlines challenges with traditional on-premise BI implementations and how a hybrid cloud solution addresses these issues through scalability, availability, cost efficiency and other benefits. The proposed solution features on-premise components that cleanse and transfer data to cloud components, which include an Azure table storage data warehouse, reporting and analytics tools, and delivery of reports to both internal and external users.
Core banking Closure bank day OSWA meetup 2018-Alexander Petrov OsloAlexander Petrov
Core Banking platform. Alexander Petrov demonstrates architecture based on In memory data grid solving the problem of closing the bank day , month, year.
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
- Accel proposes implementing a data warehouse and business intelligence solution using Business Objects software to provide consolidated access to organizational data and generate reports for improved decision making.
- The proposed solution includes building a data warehouse with an ETL process to integrate data from various sources, deploying Business Objects products for reporting, analysis and dashboards, and sample reports focused on retail business metrics.
- Benefits of the solution include increased access to required information, scalability, improved decision making through analysis, and protection of information access through security controls.
This document discusses big data analytics and analytic platforms. It provides an overview of why organizations are adopting big data analytics due to changing data types and advances in technology. It also discusses different analytic techniques like MPP and columnar storage used in analytic platforms. The document proposes a framework for organizations to succeed with big data analytics through their culture, people, organization structure, architecture, and use of analytic platforms. It also discusses different types of analytic platforms and considerations for purchasing them.
Slides: Moving from a Relational Model to NoSQLDATAVERSITY
Businesses are quickly moving to NoSQL databases to power their modern applications. However, a technology migration involves risk, especially if you have to change your data model. What if you could host a relatively unmodified RDBMS schema on your NoSQL database, then optimize it over time?
We’ll show you how Couchbase makes it easy to:
• Use SQL for JSON to query your data and create joins
• Optimize indexes and perform HashMap queries
• Build applications and analysis with NoSQL
Platforming the Major Analytic Use Cases for Modern EngineeringDATAVERSITY
We’ll describe some use cases as examples of a broad range of modern use cases that need a platform. We will describe some popular valid technology stacks that enterprises use in accomplishing these modern use cases of customer churn, predictive analytics, fraud detection, and supply chain management.
In many industries, to achieve top-line growth, it is imperative that companies get the most out of existing customer relationships. Customer churn use cases are about generating high levels of profitable customer satisfaction through the use of knowledge generated from corporate and external data to help drive a more positive customer experience (CX).
Many organizations are turning to predictive analytics to increase their bottom line and efficiency and, therefore, competitive advantage. It can make the difference between business success or failure.
Fraudulent activity detection is exponentially more effective when risk actions are taken immediately (i.e., stop the fraudulent transaction), instead of after the fact. Fast digestion of a wide network of risk exposures across the network is required in order to minimize adverse outcomes.
Supply chain leaders are under constant pressure to reduce overall supply chain management (SCM) costs while maintaining a flexible and diverse supplier ecosystem. They will leverage IoT, sensors, cameras, and blockchain. Major investments in advanced analytics, warehouse relocation, and automation, both in distribution centers and stores, will be essential for survival.
Synopsis: Modern enterprises anticipate business requirements and work proactively to optimise the outcomes. If they don’t renovate or reinvent their data architectures, they lose customers, and market share. So my talk will be in detailing the importance of data architecture, architectural challenges if is not addressed and a case study - the learnings and success story by fixing the issues at the root - at the data storage & access.
Target Audience: Principal Software engineers & Architects
Key Takeaways: Importance of Modern Data Architecture, PostgreSQL & JSONB
I have given a talk @ https://hasgeek.com/rootconf/elasticsearch-users-meetup-hyderabad/
Retour d'expérience sur un projet de Business Intelligence réalisé à l'EVAM selon une méthodologie Agile et avec un modèle de données Data Vault. Présentation faite lors du Swiss Data Forum du 24 novembre 2015 à Lausanne
Agile Data Warehouse Design for Big Data PresentationVishal Kumar
Synopsis:
[Video link: http://www.youtube.com/watch?v=ZNrTxSU5IQ0 ]
Jim Stagnitto and John DiPietro of consulting firm a2c) will discuss Agile Data Warehouse Design - a step-by-step method for data warehousing / business intelligence (DW/BI) professionals to better collect and translate business intelligence requirements into successful dimensional data warehouse designs.
The method utilizes BEAM✲ (Business Event Analysis and Modeling) - an agile approach to dimensional data modeling that can be used throughout analysis and design to improve productivity and communication between DW designers and BI stakeholders. BEAM✲ builds upon the body of mature "best practice" dimensional DW design techniques, and collects "just enough" non-technical business process information from BI stakeholders to allow the modeler to slot their business needs directly and simply into proven DW design patterns.
BEAM✲ encourages DW/BI designers to move away from the keyboard and their entity relationship modeling tools and begin "white board" modeling interactively with BI stakeholders. With the right guidance, BI stakeholders can and should model their own BI data requirements, so that they can fully understand and govern what they will be able to report on and analyze.
The BEAM✲ method is fully described in
Agile Data Warehouse Design - a text co-written by Lawrence Corr and Jim Stagnitto.
About the speaker:
Jim Stagnitto Director of a2c Data Services Practice
Data Warehouse Architect: specializing in powerful designs that extract the maximum business benefit from Intelligence and Insight investments.
Master Data Management (MDM) and Customer Data Integration (CDI) strategist and architect.
Data Warehousing, Data Quality, and Data Integration thought-leader: co-author with Lawrence Corr of "Agile Data Warehouse Design", guest author of Ralph Kimball’s “Data Warehouse Designer” column, and contributing author to Ralph and Joe Caserta's latest book: “The DW ETL Toolkit”.
John DiPietro Chief Technology Officer at A2C IT Consulting
John DiPietro is the Chief Technology Officer for a2c. Mr. DiPietro is responsible
for setting the vision, strategy, delivery, and methodologies for a2c’s Solution
Practice Offerings for all national accounts. The a2c CTO brings with him an
expansive depth and breadth of specialized skills in his field.
Sponsor Note:
Thanks to:
Microsoft NERD for providing awesome venue for the event.
http://A2C.com IT Consulting for providing the food/drinks.
http://Cognizeus.com for providing book to give away as raffle.
The document discusses databases versus data warehousing. It notes that databases are for operational purposes like storage and retrieval for applications, while data warehouses are used for informational purposes like business reporting and analysis. A data warehouse contains integrated, subject-oriented data from multiple sources that is used to support management decisions.
SOA with Data Virtualization (session 4 from Packed Lunch Webinar Series)Denodo
A robust SOA infrastructure is the lifeblood to application and process integration within the organization. However, the SOA stack (ESB, BPM, CEP, and so on) has often not mixed well with the traditional data integration stack – in most cases, data integration has been the ‘poor cousin’ in this relationship. Data Virtualization allows you to easily and quickly create a virtual data services layer to integrate cleanly into your SOA infrastructure – and also support new initiatives, such as mobile and cloud applications
More information and FREE registrations for this webinar: http://goo.gl/apGLPt
Landing page for the entire Packed Lunch webinar series: http://goo.gl/NATMHw .
Attend & get unique insights into:
- How Data Virtualization enables a more agile data architecture that better aligns with your SOA infrastructure.
- How to easily and quickly create data services to expose your data sources in a SOA-friendly way.
- Denodo’s unique linked RESTful data services that simplify building mobile and web applications.
- Case studies that demonstrate how Data Virtualization has enhanced existing SOA and BPM systems.
Analyst Webinar: Best Practices In Enabling Data-Driven Decision MakingDenodo
Watch full webinar here: https://bit.ly/37YkgN4
This presentation looks at the trends that are emerging from companies on their journeys to becoming data-driven enterprises.
These trends are taken from a survey of 500 companies and highlight critical success factors, what companies are doing, their progress so far and their plans going forward. It also looks at the role that data virtualization has within the data driven enterprise.
During the session we'll address:
- What is a data-driven enterprise?
- What are the critical success factors?
- What are companies doing to create a data-driven enterprise and why?
- What progress are they making?
- What are the plans on people, process and technologies?
- Why is data virtualization central to provisioning and accessing data in a data-driven enterprise?
- How should you get started?
Slides: Proven Strategies for Hybrid Cloud Computing with Mainframes — From A...DATAVERSITY
Mainframes continue to perform mission-critical transaction processing and contain massive amounts of core business data. But digital transformation initiatives and cloud computing have created both opportunities and challenges for unlocking and utilizing this data. Qlik and AWS will share some of the proven strategies from successful customer deployments across a range of different mainframe to cloud use cases, including legacy application modernization, data analytics, and data migrations.
In this presentation, you will learn how to:
• Replicate very large volumes of mainframe data in real-time to the cloud
• Automate the creation of analytics-ready data lakes and data warehouses
• Achieve a 30% reduction in cost of compute
Agile BI via Data Vault and ModelstormingDaniel Upton
Audience: Business Intelligence Architects, Project Managers and Sponsors. This slideshow accompanies a video presentation of the same name, available at http://youtu.be/e0cHFdeGEeE.
Slides: Accelerating Queries on Cloud Data LakesDATAVERSITY
Using “zero-copy” hybrid bursting on remote data to solve data lake analytics capacity and performance problems.
Data scientists want answers on demand. But in today’s enterprise architectures, the reality is that most data remains on-prem, despite the promise of cloud-based analytics. Moving all that data to the cloud has typically not been possible for many reasons including cost, latency, and technical difficulty. So, what if there was a technology that would connect these on-prem environments to any major cloud platform, enabling high-powered computing without the need to move massive amounts of data?
Join us for this webinar where Alex Ma of Alluxio, an open-source data orchestration platform, will discuss how a data orchestration approach offers a solution for connecting traditional on-prem data centers and cloud data lakes with other clouds and data centers. With Alluxio’s “zero-copy” burst solution, companies can bridge remote data centers and data lakes with computing frameworks in other locations, enabling them to offload, compute, and leverage the flexibility, scalability, and power of the cloud for their remote data.
IDERA Slides: Managing Complex Data EnvironmentsDATAVERSITY
Companies are expanding their information systems beyond relational databases to incorporate big data and cloud deployments, creating hybrid configurations. Database professionals have the challenges of managing multiple data sources and running queries for analytics against diverse databases in these complex environments.
IDERA’s Lisa Waugh will discuss how to deal with the growing challenges of having data residing on different database platforms by using a single IDE.
The document discusses data warehouse concepts and architecture. It defines a data warehouse as a structured repository of historic data that is subject oriented, integrated, time variant, and non-volatile. It contains business specified data to answer business questions. The document outlines why data warehouses are needed, how they are developed through operational data stores and data marts, and how they can be used for strategic reporting and trend analysis rather than replacing operational systems.
The Business Data Lake is a new approach to information management, analytics and reporting that better matches the culture of business and better enables organizations to truly leverage the value of their information.
ADV Slides: Data Pipelines in the Enterprise and ComparisonDATAVERSITY
Despite the many, varied, and legitimate data platforms that exist today, data seldom lands once in its perfect spot for the long haul of usage. Data is continually on the move in an enterprise into new platforms, new applications, new algorithms, and new users. The need for data integration in the enterprise is at an all-time high.
Solutions that meet these criteria are often called data pipelines. These are designed to be used by business users, in addition to technology specialists, for rapid turnaround and agile needs. The field is often referred to as self-service data integration.
Although the stepwise Extraction-Transformation-Loading (ETL) remains a valid approach to integration, ELT, which uses the power of the database processes for transformation, is usually the preferred approach. The approach can often be schema-less and is frequently supported by the fast Apache Spark back-end engine, or something similar.
In this session, we look at the major data pipeline platforms. Data pipelines are well worth exploring for any enterprise data integration need, especially where your source and target are supported, and transformations are not required in the pipeline.
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it.
https://www.brighttalk.com/webcast/18317/422499
ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...DATAVERSITY
This webinar will focus on the promise AI holds for organizations in every industry and every size, and how to overcome some of the challenges today of how to prepare for AI in the organization and how to plan AI applications.
The foundation for AI is data. You must have enough data to analyze to build models. Your data determines the depth of AI you can achieve – for example, statistical modeling, machine learning, or deep learning – and its accuracy. The increased availability of data is the single biggest contributor to the uptake in AI where it is thriving. Indeed, data’s highest use in the organization soon will be training algorithms. AI is providing a powerful foundation for impending competitive advantage and business disruption.
Business Intelligence Solution on Windows AzureInfosys
The document discusses a proposed cloud-based business intelligence (BI) solution on Microsoft Azure. It outlines challenges with traditional on-premise BI implementations and how a hybrid cloud solution addresses these issues through scalability, availability, cost efficiency and other benefits. The proposed solution features on-premise components that cleanse and transfer data to cloud components, which include an Azure table storage data warehouse, reporting and analytics tools, and delivery of reports to both internal and external users.
Core banking Closure bank day OSWA meetup 2018-Alexander Petrov OsloAlexander Petrov
Core Banking platform. Alexander Petrov demonstrates architecture based on In memory data grid solving the problem of closing the bank day , month, year.
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
- Accel proposes implementing a data warehouse and business intelligence solution using Business Objects software to provide consolidated access to organizational data and generate reports for improved decision making.
- The proposed solution includes building a data warehouse with an ETL process to integrate data from various sources, deploying Business Objects products for reporting, analysis and dashboards, and sample reports focused on retail business metrics.
- Benefits of the solution include increased access to required information, scalability, improved decision making through analysis, and protection of information access through security controls.
This document discusses big data analytics and analytic platforms. It provides an overview of why organizations are adopting big data analytics due to changing data types and advances in technology. It also discusses different analytic techniques like MPP and columnar storage used in analytic platforms. The document proposes a framework for organizations to succeed with big data analytics through their culture, people, organization structure, architecture, and use of analytic platforms. It also discusses different types of analytic platforms and considerations for purchasing them.
Slides: Moving from a Relational Model to NoSQLDATAVERSITY
Businesses are quickly moving to NoSQL databases to power their modern applications. However, a technology migration involves risk, especially if you have to change your data model. What if you could host a relatively unmodified RDBMS schema on your NoSQL database, then optimize it over time?
We’ll show you how Couchbase makes it easy to:
• Use SQL for JSON to query your data and create joins
• Optimize indexes and perform HashMap queries
• Build applications and analysis with NoSQL
Platforming the Major Analytic Use Cases for Modern EngineeringDATAVERSITY
We’ll describe some use cases as examples of a broad range of modern use cases that need a platform. We will describe some popular valid technology stacks that enterprises use in accomplishing these modern use cases of customer churn, predictive analytics, fraud detection, and supply chain management.
In many industries, to achieve top-line growth, it is imperative that companies get the most out of existing customer relationships. Customer churn use cases are about generating high levels of profitable customer satisfaction through the use of knowledge generated from corporate and external data to help drive a more positive customer experience (CX).
Many organizations are turning to predictive analytics to increase their bottom line and efficiency and, therefore, competitive advantage. It can make the difference between business success or failure.
Fraudulent activity detection is exponentially more effective when risk actions are taken immediately (i.e., stop the fraudulent transaction), instead of after the fact. Fast digestion of a wide network of risk exposures across the network is required in order to minimize adverse outcomes.
Supply chain leaders are under constant pressure to reduce overall supply chain management (SCM) costs while maintaining a flexible and diverse supplier ecosystem. They will leverage IoT, sensors, cameras, and blockchain. Major investments in advanced analytics, warehouse relocation, and automation, both in distribution centers and stores, will be essential for survival.
Synopsis: Modern enterprises anticipate business requirements and work proactively to optimise the outcomes. If they don’t renovate or reinvent their data architectures, they lose customers, and market share. So my talk will be in detailing the importance of data architecture, architectural challenges if is not addressed and a case study - the learnings and success story by fixing the issues at the root - at the data storage & access.
Target Audience: Principal Software engineers & Architects
Key Takeaways: Importance of Modern Data Architecture, PostgreSQL & JSONB
I have given a talk @ https://hasgeek.com/rootconf/elasticsearch-users-meetup-hyderabad/
Retour d'expérience sur un projet de Business Intelligence réalisé à l'EVAM selon une méthodologie Agile et avec un modèle de données Data Vault. Présentation faite lors du Swiss Data Forum du 24 novembre 2015 à Lausanne
Agile Data Warehouse Design for Big Data PresentationVishal Kumar
Synopsis:
[Video link: http://www.youtube.com/watch?v=ZNrTxSU5IQ0 ]
Jim Stagnitto and John DiPietro of consulting firm a2c) will discuss Agile Data Warehouse Design - a step-by-step method for data warehousing / business intelligence (DW/BI) professionals to better collect and translate business intelligence requirements into successful dimensional data warehouse designs.
The method utilizes BEAM✲ (Business Event Analysis and Modeling) - an agile approach to dimensional data modeling that can be used throughout analysis and design to improve productivity and communication between DW designers and BI stakeholders. BEAM✲ builds upon the body of mature "best practice" dimensional DW design techniques, and collects "just enough" non-technical business process information from BI stakeholders to allow the modeler to slot their business needs directly and simply into proven DW design patterns.
BEAM✲ encourages DW/BI designers to move away from the keyboard and their entity relationship modeling tools and begin "white board" modeling interactively with BI stakeholders. With the right guidance, BI stakeholders can and should model their own BI data requirements, so that they can fully understand and govern what they will be able to report on and analyze.
The BEAM✲ method is fully described in
Agile Data Warehouse Design - a text co-written by Lawrence Corr and Jim Stagnitto.
About the speaker:
Jim Stagnitto Director of a2c Data Services Practice
Data Warehouse Architect: specializing in powerful designs that extract the maximum business benefit from Intelligence and Insight investments.
Master Data Management (MDM) and Customer Data Integration (CDI) strategist and architect.
Data Warehousing, Data Quality, and Data Integration thought-leader: co-author with Lawrence Corr of "Agile Data Warehouse Design", guest author of Ralph Kimball’s “Data Warehouse Designer” column, and contributing author to Ralph and Joe Caserta's latest book: “The DW ETL Toolkit”.
John DiPietro Chief Technology Officer at A2C IT Consulting
John DiPietro is the Chief Technology Officer for a2c. Mr. DiPietro is responsible
for setting the vision, strategy, delivery, and methodologies for a2c’s Solution
Practice Offerings for all national accounts. The a2c CTO brings with him an
expansive depth and breadth of specialized skills in his field.
Sponsor Note:
Thanks to:
Microsoft NERD for providing awesome venue for the event.
http://A2C.com IT Consulting for providing the food/drinks.
http://Cognizeus.com for providing book to give away as raffle.
The document discusses databases versus data warehousing. It notes that databases are for operational purposes like storage and retrieval for applications, while data warehouses are used for informational purposes like business reporting and analysis. A data warehouse contains integrated, subject-oriented data from multiple sources that is used to support management decisions.
SOA with Data Virtualization (session 4 from Packed Lunch Webinar Series)Denodo
A robust SOA infrastructure is the lifeblood to application and process integration within the organization. However, the SOA stack (ESB, BPM, CEP, and so on) has often not mixed well with the traditional data integration stack – in most cases, data integration has been the ‘poor cousin’ in this relationship. Data Virtualization allows you to easily and quickly create a virtual data services layer to integrate cleanly into your SOA infrastructure – and also support new initiatives, such as mobile and cloud applications
More information and FREE registrations for this webinar: http://goo.gl/apGLPt
Landing page for the entire Packed Lunch webinar series: http://goo.gl/NATMHw .
Attend & get unique insights into:
- How Data Virtualization enables a more agile data architecture that better aligns with your SOA infrastructure.
- How to easily and quickly create data services to expose your data sources in a SOA-friendly way.
- Denodo’s unique linked RESTful data services that simplify building mobile and web applications.
- Case studies that demonstrate how Data Virtualization has enhanced existing SOA and BPM systems.
The document describes several projects worked on by Antonio Carlos Siqueira between 2002-2007 as a developer and analyst. The projects involved requirements analysis, design, development and debugging of systems using technologies like Visual Studio, SQL Server, and SSAS/SSIS/SSRS. Siqueira took a lead role on the projects and optimized queries, stored procedures and OLAP cubes. The projects were for clients in various industries and involved building applications, reporting solutions and business intelligence systems.
Microsoft for BI and DW: Using the Right Tool for the JobSenturus
Learn the capabilities and best use cases for Power BI, SQL Server, SharePoint, Azure and Office. View the webinar video recording and download this deck: http://www.senturus.com/resources/microsoft-for-bi-and-dw/.
You'll also want to check out a Microsoft tool matrix that guides you in choosing the right tool for the job: http://www.senturus.com/wp-content/uploads/2015/11/Microsoft-BI-DW-Tool-Matrix-Senturus.pdf.
Knowing how the tools work together allows you to build an efficient, integrated BI solution. Information includes a review of product features and benefits, discusses use cases and demonstrate product capabilities.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
The Hive Data Virtualization Introduction - Sanjay Krishnamurti, Chief Archit...The Hive
Informatica's Data Virtualization Solution addresses the problems organizations face in getting business data to users in a timely manner. It currently takes weeks or months on average to integrate new data sources, create reports, or change data hierarchies. Data Virtualization creates a common access layer across data sources so data can be accessed and analyzed without movement. It provides reusable data services, advanced transformations, and real-time data profiling and quality checks to help organizations more quickly and directly access clean trusted data. Data Virtualization is a key part of building an agile data platform that can leverage existing investments and infrastructure.
Logical Data Warehouse and Data Lakes can play a role in many different type of projects and, in this presentation, we will look at some of the most common patterns and use cases. Learn about analytical and big data patterns as well as performance considerations. Example implementations will be discussed for each pattern.
- Architectural patterns for logical data warehouse and data lakes.
- Performance considerations.
- Customer use cases and demo.
This presentation is part of the Denodo Educational Seminar, and you can watch the video here goo.gl/vycYmZ.
The document provides an overview of business intelligence, data warehousing, and ETL concepts. It defines business intelligence as using technologies to analyze data and support decision making. A data warehouse stores historical data from transaction systems and supports querying and analysis for insights. ETL is the process of extracting data from sources, transforming it, and loading it into the data warehouse for analysis. The document discusses components of BI systems like the data warehouse, data marts, and dimensional modeling and provides examples of how these concepts work together.
The document introduces concepts related to business intelligence (BI) and data warehousing (DW). It defines BI and DW, discusses their purposes, and describes common processes like dimensional modeling, extract-transform-load (ETL), online analytical processing (OLAP), and tools from IBM Cognos and Microsoft SQL Server used for BI and DW projects.
The ETL process in data warehousing involves extraction, transformation, and loading of data. Data is extracted from operational databases, transformed to match the data warehouse schema, and loaded into the data warehouse database. As source data and business needs change, the ETL process must also evolve to maintain the data warehouse's value as a business decision making tool. The ETL process consists of extracting data from sources, transforming it to resolve conflicts and quality issues, and loading it into the target data warehouse structures.
The document provides an overview of an organization's data integration strategy. It discusses the scope of integration, including defining IT enablement needs, standards for data definition and information flow, and infrastructure requirements. It also outlines key focus areas such as process integration, data integration, and data management. The document summarizes the organization's business requirements for integration and provides examples of system data flows and interfaces between systems. Finally, it compares different integration technologies such as EII, EAI, ETL, and CDC.
White Paper - Data Warehouse Documentation RoadmapDavid Walker
All projects need documentation and many companies provide templates as part of a methodology. This document describes the templates, tools and source documents used by Data Management & Warehousing. It serves two purposes:
• For projects using other methodologies or creating their own set of documents to use as a checklist. This allows the project to ensure that the documentation covers the essential areas for describing the data warehouse.
• To demonstrate our approach to our clients by describing the templates and deliverables that are produced.
Documentation, methodologies and templates are inherently both incomplete and flexible. Projects may wish to add, change, remove or ignore any part of any document. Some may also believe that aspects of one document would sit better in another. If this is the case then users of this document and these templates are encouraged to change them to fit their needs.
Data Management & Warehousing believes that the approach or methodology for building a data warehouse should be to use a series of guides and checklists. This ensures that small teams of relatively skilled resources developing the system can cover all aspects of the project whilst being free to deal with the specific issues of their environment to deliver exceptional solutions, rather than a rigid methodology that ensures that large teams of relatively unskilled staff can meet a minimum standard.
Microsoft Data Warehouse Business Intelligence Lifecycle - The Kimball ApproachMark Ginnebaugh
Data Warehouse - Business Intelligence Lifecycle Overview by Warren Thronthwaite
This slide deck describes the Kimball approach from the best-selling Data Warehouse Toolkit, 2nd Edition. It was presented to the Bay Area Microsoft Business Intelligence User Group in October 2012.
Starting with business requirements and project definition, the lifecycle branches out into three tracks: Technical, Data and Applications. You will learn:
* The major steps in the Lifecycle and what needs to happen in each one.
* Why business requirements are so important and how they influence all major decisions across the entire DW/BI system.
* Key tools for prioritizing business requirements and creating an enterprise information framework.
* How to break up a DW/BI system into doable increments that add real business value and can be completed in a reasonable time frame.
Hadoop and Data Virtualization - A Case Study by VHAHortonworks
This document discusses a case study of VHA implementing Hadoop and data virtualization technologies with Denodo and Hortonworks. It describes VHA's goals of moving to a modern data architecture by loading all types of data into a single data lake for flexible analysis. Challenges included training business users on new tools like Pig and Hive for accessing Hadoop. The solution involved utilizing data virtualization with Denodo to allow applications to access data without technical details, improving adoption.
This is the presentation for the talk I gave at JavaDay Kiev 2015. This is about an evolution of data processing systems from simple ones with single DWH to the complex approaches like Data Lake, Lambda Architecture and Pipeline architecture
How Starbucks Forecasts Demand at Scale with Facebook Prophet and DatabricksNavin Albert
Performing fine-grained forecasts on day-store-SKU is beyond the ability of legacy, data warehousing based forecasting tools. Demand for products varies by product, store and day, and yet traditional demand forecasting solutions perform their forecasts at the aggregate market, week and promo group levels.
With the introduction of the Databricks Unified Data Analytics Platform, retailers are able to see double-digit improvements in their forecast accuracy. They can perform fine-grained forecasts at the SKU, store and day as well as include hundreds of additional features to improve the accuracy of models. They can further enhance their forecasts with localization and the easy inclusion of additional data sets. And they’re running these forecasts daily, providing their planners and retail operations team with timely data for better execution.
In this webinar, we reviewed:
How to perform fine-grained demand forecasts on a day/store/SKU level with Databricks
How to forecast time series data precisely using Facebook’s Prophet
Also, how Starbucks does custom forecasting with relative ease
How to train a large number of models using the defacto distributed data processing engine, Apache Spark™
Finally, we then presented this data to analysts and managers using BI tools to enable the decision making required to drive the required business outcomes
Trivadis TechEvent 2016 Customer Event Hub - the modern Customer 360° view by...Trivadis
Unternehmen kommunizieren heute über verschiedenste Kanäle mit ihren Kunden. Dabei entstehen viele Daten in unterschiedlichen Systemen, immer öfter auch ausserhalb des Unternehmens. Diese Daten haben oft keine einheitlichen Formate und werden kontinuierlich und mit grösser werdendem Volumen erzeugt. Mit IoT Anwendungen wird dies nur noch extremer. Um eine komplette und konsistente Sicht über den Kunden zu haben, müssen all diese kundenbezogenen Informationen in eine 360 Grad Sicht einbezogen werden und dies möglichst in Echtzeit. Der Customer Hub wird damit zum Customer Event Hub
This document discusses Recom Retail Solution, a retail management model centered around manufacturers. It provides customized business intelligence solutions that process and analyze critical business information quickly using tools like artificial intelligence. The solutions help optimize resources, sales, cash flow, and more through dimensional measurement and aggregation. It discusses applications of data mining techniques like market basket analysis, association rules, and clustering to gain insights from customer data.
Your Roadmap for An Enterprise Graph StrategyNeo4j
Speaker: Michael Moore, Ph.D., Executive Director, Knowledge Graphs + AI, EY National Advisory
Abstract: Knowledge graphs have enormous potential for delivering superior customer experiences, advanced analytics and efficient data management.
Learn valuable tips from a leading practitioner on how to position, organize and implement your first enterprise graph project.
Your Roadmap for An Enterprise Graph Strategy Neo4j
This document provides a roadmap for developing an enterprise graph strategy. It outlines key steps including building a proof of concept graph using a small dataset, designing the graph schema, and creating demo applications. The roadmap involves discussions with stakeholders to understand use cases and business needs. Example graph schemas are provided for customer 360, supply chain, and master data management. The goal is to solve a "graphy problem" and showcase the value of connected data through new insights and analytics.
Your Roadmap for An Enterprise Graph StrategyNeo4j
This document provides a roadmap for developing an enterprise graph strategy with the following key steps:
1. Design and build a proof-of-concept graph using a small local dataset to demonstrate graph capabilities.
2. Present use cases and example queries to business stakeholders to validate the graph model and gather feedback.
3. Design the production graph schema and build APIs/services to integrate data from multiple sources.
4. Deploy the graph in the cloud and develop applications and reports to mobilize enterprise data using the graph.
Turning Big Data into Better Business OutcomesCisco Canada
The big data era is upon us as organizations are awash in social, mobile and machine-generated data. Opportunity abounds. But competition threatens. Further this high volume, data-at-the-edge environment challenges centralized data warehouse approaches typical with BI and Analytics today. Data virtualization provides a more agile, leave-the-data-where-it-lies way to fulfill BI and Analytic needs and achieve key business outcomes.
Improve Store Expansion (Territory Management Featuring)Esri España
This document discusses SAP Spatial and its integration across SAP applications and with Esri ArcGIS. It highlights how SAP Spatial can provide maps and spatial analysis within SAP applications or custom apps. It also discusses how SAP HANA serves as a platform to seamlessly integrate spatial and enterprise data to enable spatial capabilities across organizations. Examples are provided of how SAP Spatial has been used in retail for tasks like in-store consumer behavioral analytics and demand forecasting. Competition and key trends in retail are also mentioned.
Knowledge Graphs for Supply Chain Operations.pdfVaticle
Agility in supply chain operations has never been so important, especially with today's nonlinear and complex world. That is why companies with supply chains need knowledge graphs.
So how do enterprises unleash the power of their own supply chain data to make smarter decisions? This is where bops comes into play. Bops activates supply chain data from existing operating systems (ERPs, Pos, OMS, etc) simplifying how operators optimize working capital in every decision.
In this session, bops will showcase a few use cases that portray the power of a knowledge graph to represent a supply chain network composed of an end to end product flow driven by actions among plants, customers and suppliers.
Supply chain operations visibility:
- Story of a Product and an SKU: from raw material to finished goods track trace & bill of material deviations
- Story of a Supplier – risk assessments – “the most influential supplier”
- Story of a Process – anomaly detection – “what went wrong?”
Join us for a lively discussion to learn how using knowledge graphs is already helping supply chain companies to better collect, unify, and activate their data.
Speaker: Jorge Risquez
Jorge is the Co-founder and CEO of bops, a headless supply chain intelligence platform helping manufacturers and distributors source, make, and deliver their products, and unlock working capital. Previously, Jorge spent a decade as a Supply Chain Consultant for Deloitte, where he worked with Fortune 500 companies such as Tyson and Cargill. In his spare time, he enjoys going for a run in Central Park and spending time with family and friends.
This document discusses building products with data at the core. It provides an overview of the data landscape, including the growth of data volumes and the big data market. The data landscape map shows the evolution of data tools from 1980s spreadsheets to today's AI-informed decisions. Categories of the data landscape include databases, data warehouses, ETL, data prep, data virtualization, business intelligence, visualization, master data management, governance, and data science tools. Examples are given for popular tools in each category.
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Denodo
Watch full webinar here: https://bit.ly/35FUn32
Presented at CDAO New Zealand
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists.
However, most architecture laid out to enable data scientists miss two key challenges:
- Data scientists spend most of their time looking for the right data and massaging it into a usable format
- Results and algorithms created by data scientists often stay out of the reach of regular data analysts and business users
Watch this session on-demand to understand how data virtualization offers an alternative to address these issues and can accelerate data acquisition and massaging. And a customer story on the use of Machine Learning with data virtualization.
1) The document discusses how graph databases can help with master data management by providing a 360-degree view of customers, products, suppliers, and other connected data.
2) It provides examples of how graphs show connections between different types of master data like customers, products, suppliers, and shows how Adidas uses Neo4j to deliver personalized content.
3) Building a graph-based master data repository allows for flexibility, agility, and relationships that help with recommendations, fraud detection, and supply chain management.
Seagate provides a customer portal called Seagate Direct that offers self-service business-to-business applications for direct customers. The portal provides secure access to applications for ordering, returns, pricing, marketing, billing, and support. It improves efficiency for customers and Seagate by reducing manual processes. Key applications on the portal include online ordering (SOLO), price lists, sales and marketing programs, billing status, and electronic returns. The portal has over 1,000 users that generate over 100,000 visits per quarter. Feedback from customers on usability has been positive.
Data Con LA 2022 - Practical Solutions to Complex Supply Chain ProblemsData Con LA
The document provides examples of how data science can be applied to solve complex supply chain problems for customers. It discusses forecasting demand at a granular level for a spirits company, using machine learning to predict late shipments for a CPG company, and optimizing a distribution network for a durable goods company. The goal is to show how data science tools and services can help businesses address challenges in forecasting, logistics planning, and operations optimization.
SPS Chevy Chase - Build It and They Will Come: Sharepoint 2013 User AdoptionStacy Deere
Everyone has seen the movie Field of Dream where a farmer sacrifices part of his cash crops to builds baseball field. It just so happens after he built it numerous famous baseball players come back from the beyond to play baseball and people come from all around the world to watch and in turn the farmer became rich for charging admission, food, beverages, etc.
SharePoint 2013 and building user adoption share a lot of the same concepts and overall goals to ultimately come out with the same end result. Lots and lots of people from all around the company start utilizing SharePoint and all its features and functionality. In this session you will see first-hand information from a company that I have been working with since they had 2007, 2010 and now 2013. I will review the project and all the steps taken to increase user adoption.
• Discovery Sessions
• Governance
• Build & Implementation
• Migration
• Next Steps
To prove the success of the overall project I will show various statistics from the previous versions and SharePoint 2013. We will also take a look at some additional successes that have come from this upgrade and increase in user adoption.
SPFest DC Build It and They Will Come Share-Point 2013 User AdoptionStacy Deere
Everyone has seen the movie Field of Dream where a farmer sacrifices part of his cash crops to builds baseball field. It just so happens after he built it numerous famous baseball players come back from the beyond to play baseball and people come from all around the world to watch and in turn the farmer became rich for charging admission, food, beverages, etc.
SharePoint 2013 and building user adoption share a lot of the same concepts and overall goals to ultimately come out with the same end result. Lots and lots of people from all around the company start utilizing SharePoint and all its features and functionality. In this session you will see first-hand information from a company that I have been working with since they had 2007, 2010 and now 2013. I will review the project and all the steps taken to increase user adoption.
• Discovery Sessions
• Governance
• Build & Implementation
• Migration
• Next Steps
To prove the success of the overall project I will show various statistics from the previous versions and SharePoint 2013. We will also take a look at some additional successes that have come from this upgrade and increase in user adoption.
The document discusses knowledge graphs and provides examples of how Neo4j has been used by customers for knowledge graph and graph database applications. Specifically, it discusses how Neo4j has helped organizations like Itau Unibanco, UBS, Airbnb, Novartis, Columbia University, Telia, Scripps Networks, and Pitney Bowes with fraud detection, master data management, content management, smart home applications, investigative journalism, and other use cases by building knowledge graphs and connecting diverse data sources.
Similar to a2c Boston Big Data Meet-up: Agile Data Warehouse Design (20)
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdfTechgropse Pvt.Ltd.
In this blog post, we'll delve into the intersection of AI and app development in Saudi Arabia, focusing on the food delivery sector. We'll explore how AI is revolutionizing the way Saudi consumers order food, how restaurants manage their operations, and how delivery partners navigate the bustling streets of cities like Riyadh, Jeddah, and Dammam. Through real-world case studies, we'll showcase how leading Saudi food delivery apps are leveraging AI to redefine convenience, personalization, and efficiency.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
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2. AGENDA
•
INTRODUCTION / A2C OVERVIEW
•
MODELING FOR END USERS
•
ROLE OF DIMENSIONAL MODELS IN BIG DATA
•
EXAMPLE: E-COMMERCE
•
STRUCTURED DATA: SALES
•
SEMI-STRUCTURED DATA: CLICKSTREAM
•
AGILE DIMENSIONAL MODELING OVERVIEW
•
CASE STUDY REVIEW
•
Q&A
2
3. INTRODUCTION
A2C
•
•
BOUTIQUE EDM (ENTERPRISE DATA MANAGEMENT)
CONSULTANCY FIRM:
•
DATA WAREHOUSING
•
MASTER DATA MANAGEMENT
•
CLOSED LOOK ANALYTICS AND VISUALIZATION
•
DATA & APPLICATION ARCHITECTURE
JOHN DIPIETRO
•
•
PRINCIPAL, CHIEF TECHNOLOGY OFFICER
JIM STAGNITTO
•
•
DATA WAREHOUSE & MDM ARCHITECT
3
4. ON THURSDAY 11/14 A2C’S JIM STAGNITTO AND
JOHN DIPIETRO PRESENTED A WORKSHOP…
FEATURING AGILE DATA WAREHOUSE DESIGN - A STEP-BY-STEP
METHOD FOR DATA WAREHOUSING / BUSINESS INTELLIGENCE (DW/BI)
PROFESSIONALS TO BETTER COLLECT AND TRANSLATE BUSINESS
INTELLIGENCE REQUIREMENTS INTO SUCCESSFUL DIMENSIONAL DATA
WAREHOUSE DESIGNS.
BEAM✲
THE METHOD UTILIZES
(BUSINESS EVENT ANALYSIS AND
MODELING) - AN AGILE APPROACH TO DIMENSIONAL DATA MODELING
THAT CAN BE USED THROUGHOUT ANALYSIS AND DESIGN TO IMPROVE
PRODUCTIVITY AND COMMUNICATION BETWEEN DW DESIGNERS AND BI
STAKEHOLDERS.
SPONSORED BY MICROSOFT NERD (NEW ENGLAND RESEARCH AND
DEVELOPMENT CENTER) AND ATTENDED BY 93 DATA SCIENTISTS…
5. COMPETITIVE ADVANTAGE
CEO, Craig Spitzer
Pres., Scott King
CTO, John DiPietro
CRO, Brian Cassidy Managing Sales Dir., Joe Cattie
The founders of a2c were part of the fastest growing privately held IT
consulting and staff augmentation firm in the U.S. from 1994-2002. Our
Executive Management Team has over 100 years of collective
experience and has been responsible for delivering over a half billion
dollars of IT Consulting and staff augmentation revenue from 1994
through the present day.
a2c Top Twenty Most
Promising Data Analytics
November 2013
Alliance Consulting, Inc.
1999, 2000, 2001
CEO, Alliance Consulting
Group, Craig Spitzer 2001
7. MODELING FOR END USERS:
HOW TO DESIGN TO ANSWER BUSINESS
QUESTIONS?
•
•
THINK ABOUT HOW QUESTIONS ARE ARTICULATED
AND HOW THE ANSWERS SHOULD BE DELIVERED
•
IDENTIFY A COMMON QUESTION FRAMEWORK
•
DESIGN AN ARCHITECTURE THAT
EMBRACES AND LEVERAGES THIS
COMMON QUESTION FRAMEWORK
•
UTILIZE THE BEST DESIGNS AND
TECHNOLOGIES TO:
(A) DERIVE THE ANSWERS
(B) PRESENT THEM IN COMPELLING WAYS THAT LEAD
TO THE NEXT INTERESTING QUESTION!
7
8. HOW DO WE ASK QUESTIONS?
What
When
Who
“HOW DO THIS QUARTER‟S SALES BY SALES REP
OF ELECTRONIC PRODUCTS THAT WE PROMOTED
TO RETAIL CUSTOMERS IN THE EAST COMPARE
WITH LAST YEAR‟S?”
When
Who
Why
Where
What
8
9. HOW DO WE ASK QUESTIONS?
EVENTS / TRANSACTIONS
•
•
E.G. SALE
•
A IMMUTABLE "FACT" THAT OCCURS IN A TIME AND
(TYPICALLY A) PLACE
INTERROGATIVES:
•
•
WHO, WHAT, WHEN, WHERE, WHY
•
DESCRIPTIVE CONTEXT THAT FULLY DESCRIBES THE EVENT
•
A SET OF “DIMENSIONS" THAT DESCRIBE EVENTS
9
10. DIMENSIONAL VALUE PROPOSITION
•
IT MAKES SENSE TO PRESENT ANSWERS TO PEOPLE USING THE SAME
TAXONOMY OF EVENTS AND INTERROGATIVES (AKA: FACTS AND
DIMENSIONS - DIMENSIONAL STRUCTURE) THAT THEY USE WHEN
FORMING QUESTIONS;
•
EVENTS ARE INSTANCES OF PROCESSES ;
•
IT‟S BEST TO PRESENT INFORMATION TO PEOPLE WHO WILL ASK THE
SYSTEM QUESTIONS IN DIMENSIONAL FORM;
•
THIS IS TRUE REGARDLESS OF THE TYPE OF INFORMATION BEING
INTERROGATED, ITS SOURCE, OR IT STUFF (LIKE DATABASE
TECHNOLOGIES UTILIZED);
•
IT‟S BEST TO MODEL THIS PRESENTATION LAYER BASED ON THE
EVENTS (AKA: BUSINESS PROCESSES) THAT UNDERLIE THE
QUESTIONS.
10
12. SCENARIOS:
A BRIEF DISCUSSION OF HOW AND
WHERE DIMENSIONAL MODELING
AND/OR DATABASES FIT WITHIN
COMMON AND EMERGING “BIG
DATA” DATA WAREHOUSING
ARCHITECTURES
12
14. KIMBALL WITH BIG DATA
Dimensional BI Semantic Layer
Dimensional Data Warehouse
Big Data Capture
(e.g. HDFS)
Big Data
Discovery
(e.g. MR)
Data Movement / Integration Tier
Data Movement / Integration Tier
Source Data Tier
Source Data Tier
(Un/Semi-Structured)
(Structured)
14
15. CORPORATE INFORMATION FACTORY (CIF)
Dimensional BI Semantic Layer
Dimensional Tier
(Virtual or Physical)
Corporate Information Factory 3NF DW
Data Movement / Integration
Source Data
(Structured)
15
16. CIF WITH BIG DATA
Dimensional BI Semantic Layer
Dimensional Tier
(Virtual or Physical)
Big Data Capture
(e.g. HDFS)
Big Data
Discovery
Corporate Information
Factory 3NF DW
(e.g. MR)
Data Movement / Integration Tier
Data Movement / Integration Tier
Source Data Tier
Source Data Tier
(Un/Semi-Structured)
(Structured)
16
17. DATA VAULT
Dimensional BI Semantic Layer
Dimensional Tier
(Virtual or Physical)
Data Vault
Data Movement / Integration
Source Data
(Structured)
17
18. DATA VAULT WITH BIG DATA
Dimensional BI Semantic Layer
Dimensional Tier
(Virtual or Physical)
Big Data Capture
(e.g. HDFS)
Big Data
Discovery
Data Vault
(e.g. MR)
Data Movement / Integration Tier
Data Movement / Integration Tier
Source Data Tier
Source Data Tier
(Un/Semi-Structured)
(Structured)
18
19. COMMON FRAMEWORK
Dimensional BI Semantic Layer
Dimensional Tier
[Physical (Kimball) or Virtual (CIF or Data Vault)
(Virtual or Physical)
Persistent
Un/SemiStructured
Staging Area
Unstructured ->
Structured Data
Discovery
Processing
Persistent Structured Data
Repository
(not needed for Kimball)
Un/Semi-Structured Data Movement
Structured Data Movement
Un/Semi-Structured Source Data
Structured Source Data
(Structured)
19
Insight
Generation /
Data Mining
20. COMMON FRAMEWORK
Dining Room
Readily Accessible to End Users
(and BI Developers)
Safe, Hospital Environment
Data Assets “Ready for Primetime”
Dimensionally Structured
Dimensional BI Semantic Layer
Dimensional Tier
[Physical (Kimball) or Virtual (CIF or Data Vault)
(Virtual or Physical)
Persistent
Un/SemiStructured Staging
Area
Unstructured ->
Structured Data
Discovery
Processing
Persistent Structured Data
Repository
Kitchen
(not needed for Kimball)
Un/Semi-Structured Data Movement
Structured Data Movement
Un/Semi-Structured Source Data
Structured Source Data
(Structured)
Clickstream Data
Off Limits to End Users
Data Professionals Only Please
Dangerous / Inhospitable Environment
Data Assets “Not Ready for Primetime”
Structured Variably For Data Processing
eCommerce Sale
eCommerce Example
20
23. E-COMMERCE EXAMPLE: WEB SALES
•
•
•
FULLY STRUCTURED
THE SALE TRANSACTION TYPICALLY CARRIES ALL FUNDAMENTAL
DIMENSIONS:
• TIME
• CUSTOMER
• REFERRING URL / SEARCH PHRASE
• PRODUCT
• PURCHASE AND/OR SHIPMENT (GEO OR URL) LOCATIONS
• PROMOTION / CAMPAIGN
• ETC.
AND “HOW MANY” MEASURES
• UNIT AND PRICE QUANTITIES / AMOUNTS
• DISCOUNT AMOUNTS
• ETC.
23
24. E-COMMERCE DIMENSIONALITY
Facts (below) &
Dimensions (right)
Time
(When)
Page Visit
View Start
View End
Session Start
Session End
Customer
(Who)
Web Page
(Where)
Visitor
Current Pre
vious
Next
Detailed Product View
View Start
View End
Session Start
Session End
Prospect
Current Pre
vious
Next
Shopping Cart Activity
Activity Start
Activity End
Sale (Checkout)
Shipment / Delivery
Product
(What)
Referring
URL
(Where)
Promotion /
Campaign
(Why)
Activity
Type
(How)
✔︎
✔︎
✔︎
Prospect
✔︎
✔︎
✔︎
✔︎
Sale Start
Sale End
Customer
✔︎
✔︎
✔︎
✔︎
Shipment
Delivery
Customer
Delivery
Recipient
✔︎
24
31. DW ARCHITECTURES: A BRIEF HISTORY
Corporate Information
Factory
Undisciplined
Dimensional
Dimensional Bus
Architecture
Data-Driven Analysis
Report-Driven Analysis
Process-Driven Analysis
32. 7WS DIMENSIONAL MODEL
When
Who
Time
Customer
Day
How – Facts:
Employee
Month
Much
Third Party
Fiscal Period
Many
Organization
Often
£$€
What
Where
Product
Location
Why
Causal
Geographic
Store
Ship To
Hospital
??
Service
Transactions
Promotion
Reason
Weather
Competition
34. TO DOWNLOAD WITH AUDIO WORKSHOP FILE:
PLEASE COMPLETE THE FOLLOWING REQUEST FORM
FOR FREE LINK TO AGILE DATA WAREHOUSE DESIGN
PRESENTATION.
REVIEWS:
“EXCELLENT PRESENTATION. IT IS GOOD TO HEAR MEANINGFUL
…INFORMATION ABOUT NEW DEVELOPMENTS IN HOW AGILE
METHODOLOGIES CAN BE APPLIED TO DW/BI WORK. BIG KUDOS TO
THE PRESENTERS AND ORGANIZERS. THANKS, I FOUND IT VERY
USEFUL AND ENJOYABLE.”- RAMON VENEGAS
“EXTREMELY USEFUL TO UNDERSTAND HOW TO APPLY AGILE
APPROACH TO DWH; HOW CREATE A FRAMEWORK WHERE MODEL
CHANGES ARE WELCOME, AND BRING USERS TO THE PROCESS OF
DWH MODELING.” – ALFREDO GOMEZ
34
39. WATERFALL BI/DW DEVELOPMENT
Limited Stakeholder Interaction
Analysis
Design
Development
This Year
Stakeholder
Input
BDUF
Requirements
Data
Model
Next Year
Test
Release
ETL
BI
DATA
VALUE?
40. AGILE DW/BI DEVELOPMENT
Stakeholder interaction
?
JEDUF
BI
Prototyping
ETL
Review
Release
This Year
Next Year
Iteration 1
VALUE?
Iteration 2
ETL
BI
Iteration 3Rev
ADM
VALUE
Iteration …
VALUE!
DATA
Iteration n
VALUE!
VALUE!
41. STATE OF THE DW FIELD
•
•
SOLID:
DIMENSIONAL DATA WAREHOUSE
DESIGN IS MATURE
•
PROVEN DESIGN PATTERNS EXIST FOR
COMMON REQUIREMENTS
•
•
HIT OR MISS:
COLLECTING UNAMBIGUOUS AND
THOROUGH REQUIREMENTS
•
SLOTTING REQUIREMENTS INTO
PROVEN DESIGN PATTERNS
•
END-USER OWNERSHIP AND
VALIDATION
•
TOO OFTEN: SNATCHING DEFEAT FROM
THE JAWS OF VICTORY
41
43. BEAM✲ METHODOLOGY
Structured, non-technical, collaborative
working conversation directly with BI Users
BEAM✲
• BI User’s Business
Process,
Organizational,
Hierarchical, and Data
Knowledge
• Focused Data Profiling
Data
Modeler
BI Stakeholders
• Logical and Physical
(Kimball-esque)
Dimensional Data
Models
• Example data
• Detailed and Testable
ETL Specification
• Instantiated DW
Prototype
46. AGILE DATA MODELING
REQUIREMENTS:
•
TECHNIQUES FOR ENCOURAGING INTERACTION
•
MUST USE SIMPLE, INCLUSIVE NOTATION AND TOOLS
•
MUST BE QUICK: HOURS RATHER THAN DAYS – MODELSTORMING
•
BALANCE „JUST IN TIME‟ (JIT) AND „JUST ENOUGH DESIGN UP FRONT‟
(JEDUF) TO REDUCE DESIGN REWORK
•
DW DESIGNERS MUST EMBRACE DATA MODEL CHANGE, ALLOW MODELS TO
EVOLVE, AVOID GENERIC DATA MODELS; NEED DESIGN PATTERNS THEY CAN
TRUST TO REPRESENT TOMORROW‟S BI REQUIREMENTS TOMORROW
•
ETL AND BI DEVELOPERS MUST EMBRACE DATABASE CHANGE; NEED TOOL
SUPPORT
46
49. CALENDAR
PRODUCT
Date Key
Product Key
Date
Day
Day in Week
Day in Month
Day in Qtr
Day in Year
Month
Qtr
Year
Weekday Flag
Holiday Flag
Product Code
Product Description
Product Type
Brand
Subcategory
Category
SALES FACT
Date Key
Product Key
Store Key
Promotion Key
Quantity Sold
Revenue
Cost
Basket Count
STORE
PROMOTION
Store Key
Promotion Key
Store Code
Store Name
URL
Store Manager
Region
Country
Promotion Code
Promotion Name
Promotion Type
Discount Type
Ad Type
54. COLLABORATIVE / CONVERSATIONAL DESIGN
Who does what?
“Customers buy products”
BEAM✲
Modeler
Subjects Verb Objects
BI
Users
55. DESIGN USING NATURAL LANGUAGE
•
VERBS – EVENTS – RELATIONSHIPS – FACT TABLES
•
NOUNS – DETAILS – ENTITIES – DIMENSIONS
•
MAIN CLAUSE – SUBJECT-VERB-OBJECT
•
PREPOSITIONS – CONNECT ADDITIONAL DETAILS TO
THE MAIN CLAUSE
•
INTERROGATIVES – THE 7WS – DIMENSION TYPES
•
BUSINESS VOCABULARY - NO “IT-SPEAK”
55
56. “Spreadsheet”-like Models
Event Table Name (filled in later)
Subject Column Name
Verb
Object Column Name
Interrogative
Details
Example Data (4-6
rows)
58. CAPTURE EXAMPLE DATA:
verb
on/at/every
SUBJECT
OBJECT
EVENT
DATE
[who]
[what]
[when]
[where]
[how many]
[why]
[how]
Typical
Typical/Popular
Typical
Typical
Typical/Average
Typical/Normal
Typical/Normal
Different
Different
Different
Different
Different
Different
Different
Repeat
Repeat
Repeat
Repeat
Repeat
Repeat
Repeat
Missing
Missing
Missing
Missing
Missing
Missing
Missing
Group
Multiple/Bundle
Old, Low
Old, Low Value
Oldest needed
Near
Min, Negative, 0
New, High
New, High
Most Recent, Future
Far
Max, Precision
Multi-Level
ENGAGE
CLARIFY DEFINITIONS / CONFORM
DIMENSIONS
Multiple Values
Exceptional
Exceptional
ILLUSTRATE EXCEPTIONS
“DRIVE OUT UNIQUENESS”
“SHOW AND TELL”
66. MODEL HOW MANY MEASURES:
•
ADDITIVE – CAN BE SUMMED UP OVER ANY
COMBINATION OF DIMENSIONS. NO SPECIAL RULES
•
NON-ADDITIVE – CAN NOT BE SUMMED OVER ANY
DIMENSION E.G. UNIT PRICE OR TEMPERATURE
•
•
•
MUST BE AGGREGATED IN OTHER WAYS E.G. AVERAGE, MIN, MAX
DEGENERATE DIMENSIONS – TRANSACTION #, TIMESTAMPS, FLAGS
SEMI-ADDITIVE – CAN NOT BE SUMMED ACROSS AT
LEAST ONE DIMENSION E.G. BALANCES CAN NOT BE
SUMMED OVER TIME
66
76. RECAP:
COLLABORATIVE AND AGILE
•
•
DATA MODELING
•
DATA SOURCING
•
DATA CONFORMANCE
REQUIREMENTS = DESIGN
•
•
SLOTS DIRECTLY INTO PROVEN AND MATURE DIMENSIONAL DATA
WAREHOUSING DESIGN PATTERNS
VALIDATION THROUGH PROTOTYPING
•
•
SEMI-AUTOMATED BUILD OF DIMENSIONAL DATA WAREHOUSE
•
PERFECT COMPLIMENT TO AGILE BI TOOLS AND METHODS (E.G.
PENTAHO)
76
77. IF YOU HAVE BEEN AFFECTED BY
ANY OF THE ISSUES RAISED
IN THIS PRESENTATION…
78. AGILE DATA WAREHOUSE DESIGN
LAWRENCE CORR, JIM STAGNITTO,
DECISION PRESS, NOVEMBER 2011
81. COMPANY OVERVIEW
•
TECHNOLOGY SOLUTION CONSULTANCY
HEADQUARTERED IN PHILADELPHIA WITH REGIONAL
OFFICES IN NEW YORK AND BOSTON
•
SERVICING HEALTHCARE, LIFE SCIENCE, TEL-COM AND
FINANCIAL SERVICES INDUSTRIES WITH RECENT
OBTAINMENT OF OUR GSA SCHEDULE TO PURSUE
FEDERAL GOVERNMENT OPPORTUNITIES
•
CONSULTANT BASE OF OVER 2500 PROVEN IT
PROFESSIONALS THROUGHOUT THE NORTH EAST REGION
WITH A RECRUITING NETWORK WHICH PROVIDES
NATIONAL COVERAGE
8
1
82. COMPANY OVERVIEW
•
FLEXIBLE APPROACH TO HELPING OUR CLIENTS WITH
THEIR INITIATIVES
•
PROJECT-BASED SOLUTIONS
•
STAFF AUGMENTATION
•
MANAGED SERVICE OFFERINGS – “ON-SHORE QA ,
DEVELOPMENT & APPLICATION SUPPORT”
•
EXECUTIVE & PROFESSIONAL SEARCH
8
2
83. a2c’s Recruiting Engine and Methodology
is one of the Best in the Industry…
CAPABLE OF PRODUCING QUALITY RESULTS ON-DEMAND
FOR OUR CLIENTS. RESOURCE MANAGERS CONTINUALLY
“SILO” DISCIPLINES WITH AVAILABLE CANDIDATES WHO
HAVE PROVEN THEIR ABILITIES WITH
A2C OVER THE PAST DECADE. THE
A2C SOLUTIONS ORGANIZATION IS
INSTRUMENTAL IN THE SCREENING
AND SELECTION PROCESS TO ENSURE
THAT CANDIDATES SUBMITTED TO CLIENTS
ARE AN IDEAL MATCH.
84. THE A2C TEAM
A2C’S CULTURE
PROVIDES AN ABILITY TO
ATTRACT AND RETAIN
THE BEST TALENT IN THE
INDUSTRY AND FOSTERS
CREATIVITY, INTEGRITY,
GROWTH AND
TEAMWORK.
85. ALTERNATIVE SOLUTIONS…
A2C PROVIDES
CLIENTS WITH AN
ALTERNATIVE
SOLUTION TO A “BIG
4” CONSULTANCY AT
SUBSTANTIAL
SAVINGS FOR
PROJECTS THAT ARE
BETWEEN $500K AND
$5M DUE TO
FLEXIBILITY, AGILITY
AND FOCUS.
87. A2C SOLUTIONS CAPABILITIES
•
ENTERPRISE DATA MANAGEMENT PRACTICE HELPS CLIENTS MANAGE THEIR
COMPLETE INFORMATION LIFECYCLE FROM THEIR ON-LINE TRANSACTIONAL
SYSTEMS TO THEIR DATA WAREHOUSING, ENTERPRISE REPORTING, DATA
MIGRATION, BACK-UP AND RECOVERY STRATEGIES
•
BUSINESS ARCHITECTURE & OPTIMIZATION PRACTICE UTILIZES “SIX SIGMA LEAN”
METHODOLOGIES TO ANALYZE, RE-ENGINEER AND AUTOMATE OUR CLIENT‟S
BUSINESS PROCESSES TO LEVERAGE HUMAN WORKFLOW AND BUSINESS RULES
ENGINE TECHNOLOGIES TO CREATE EFFICIENCIES AND PROVIDE BUSINESS UNIT
OWNERS WITH THE NECESSARY METRICS TO CONTINUALLY IMPROVE
PERFORMANCE
•
PROGRAM MANAGEMENT OFFICE OVERSEES ALL ASPECTS OF SOLUTIONS
PLANNING AND DELIVERY ACROSS CLIENT ENGAGEMENT TEAMS AND PROVIDES
THE METHODOLOGY AND FRAMEWORKS WHICH ARE BASED ON PMI® INDUSTRY
STANDARDS
8
7
88. A2C SOLUTIONS CAPABILITIES
•
APPLICATION DEVELOPMENT & MANAGED SERVICES PRACTICE HELPS
CLIENTS ARCHITECT, IMPLEMENT AND DEPLOY THE LATEST MICROSOFT
AND ENTERPRISE JAVA BASED APPLICATIONS WHICH ARE BUILT ON
PROVEN FRAMEWORKS AND ARCHITECTURES FOR THE ENTERPRISE
•
A2C'S SDLC DELIVERY MODEL IS COMPRISED OF OVER 20 YEARS
COLLECTIVE BEST PRACTICES AND INDUSTRY PROVEN
METHODOLOGIES THAT ALLOW OUR DELIVERY TEAMS TO RAPIDLY
DESIGN, DEVELOP AND IMPLEMENT SOLUTIONS. OUR SDLC MODEL HAS
BEEN DESIGNED TO COMPLEMENT OUR PROJECT MANAGEMENT
METHODOLOGY, UTILIZING ITERATIVE DEVELOPMENT CYCLES THAT
ENABLE PROJECT TEAMS TO PROVIDE CONSISTENTLY HIGH QUALITY,
ON-TIME DELIVERABLES, REGARDLESS OF TECHNOLOGY PLATFORM
8
8
90. CONNECT TO A2C
For Further information on the Agile Data Warehouse Design please contact:
John DiPietro, CTO
or Jim Stagnitto, Practice Director of Information Services
a2c.com
a2c Philadelphia
1801 Market Street
Suite 2430
Philadelphia, PA 19103
215-789-4816
contact: Joe Cattie
JCattie@a2c.com
a2c Boston
100 Grandview Road
Suite 215
Braintree, MA 02184
781-848-0005
contact: Scott King
SKing@a2c.com
a2c New York
401 Greenwich Street
3rd Floor
New York, NY 10013
212-913-0933
contact: John DiPietro
JDiPietro@a2c.com