The document introduces an intelligent data lake solution that enables organizations to more effectively harness big data. It allows users to (1) find any relevant data through automated discovery and metadata cataloging, (2) quickly prepare and share needed data through self-service preparation tools, and (3) establish repeatable data preparation workflows to derive insights from big data in a scalable and sustainable way. This solution aims to help organizations overcome the challenges of extracting value from large, complex datasets and gain competitive advantages from big data analytics.
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
Modern Integrated Data Environment - Whitepaper | QuboleVasu S
A whit-paper is about building a modern data platform for data driven organisations with using cloud data warehouse with modern data platform architecture
https://www.qubole.com/resources/white-papers/modern-integrated-data-environment
The New Enterprise Blueprint featuring the Gartner Magic QuadrantLindaWatson19
Read how Solix Big Data Suite manages the entire data lifecycle without sacrificing governance, compliance, or performance. This newsletter can help you start the enterprise Hadoop journey without having to choose between operational efficiency and BI.
You Need a Data Catalog. Do You Know Why?Precisely
Data catalog has become a more popular discussion topic within data management and data governance circles. “What is it?” and “Do I need one?” are two common questions; along with “How does a catalog relate to and support the data governance program?”
The data catalog plays a key role in the governance process; How well information can be managed, aligned to business objectives and monetized depends in great part to what you know about your data.
In this webinar you will learn about:
- The role of the data catalog
- What kinds of information should be in your data catalog
- Those catalog items that can be harvested systemically versus those that require stewardship involvement
- The role of the catalog in your data quality program
We hope you’ll join this on-demand webinar and learn how a data catalog should be part of your governance and data quality program!
Advanced Analytics and Machine Learning with Data Virtualization (India)Denodo
Watch full webinar here: https://bit.ly/3dMN503
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, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Watch this session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
Modern Integrated Data Environment - Whitepaper | QuboleVasu S
A whit-paper is about building a modern data platform for data driven organisations with using cloud data warehouse with modern data platform architecture
https://www.qubole.com/resources/white-papers/modern-integrated-data-environment
The New Enterprise Blueprint featuring the Gartner Magic QuadrantLindaWatson19
Read how Solix Big Data Suite manages the entire data lifecycle without sacrificing governance, compliance, or performance. This newsletter can help you start the enterprise Hadoop journey without having to choose between operational efficiency and BI.
You Need a Data Catalog. Do You Know Why?Precisely
Data catalog has become a more popular discussion topic within data management and data governance circles. “What is it?” and “Do I need one?” are two common questions; along with “How does a catalog relate to and support the data governance program?”
The data catalog plays a key role in the governance process; How well information can be managed, aligned to business objectives and monetized depends in great part to what you know about your data.
In this webinar you will learn about:
- The role of the data catalog
- What kinds of information should be in your data catalog
- Those catalog items that can be harvested systemically versus those that require stewardship involvement
- The role of the catalog in your data quality program
We hope you’ll join this on-demand webinar and learn how a data catalog should be part of your governance and data quality program!
Advanced Analytics and Machine Learning with Data Virtualization (India)Denodo
Watch full webinar here: https://bit.ly/3dMN503
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, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Watch this session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc
Analyst Keynote: Forrester: Data Fabric Strategy is Vital for Business Innova...Denodo
Watch full webinar here: https://bit.ly/36GEuJO
Traditional data integration is falling short to meet new business requirements - real-time connected data, self-service, automation, speed, and intelligence. Forrester analyst will explain how data fabric is emerging as a hot new market for an intelligent and unified platform.
Data Democratization for Faster Decision-making and Business Agility (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3ogsO7F
Presented at 3rd Chief Digital Officer Asia Summit
The idea behind Data democratization is to enable every type of user in a company to have access to data and to ensure that there is no dependency on any single party that might create a bottleneck to data access. But this is easier said than done especially given the complex data management landscape that most organizations have today. Data virtualization is a modern data integration technique that not only delivers data in real time without replication but also simplifies data discovery, data exploration and navigating between related data sets.
In this on-demand session, you will understand how data virtualization enables enterprises to:
- Reduce up to 80% the time required to deliver data to the business adapted to the needs of each user
- Apply consistent security and governance policies across the self-service data delivery process
- Seamlessly implement the concept of 'Data Marketplace'
THE FUTURE OF DATA: PROVISIONING ANALYTICS-READY DATA AT SPEEDwebwinkelvakdag
Data lakes & data warehouses, whether on-premises or in the cloud promise to provide a centralized, cost-effective and scalable foundation for modern analytics. However, organisations continue to struggle to deliver accurate, current and analytics-ready data sets in a timely fashion. Traditional ingestion tools weren’t designed to handle hundreds or even thousands of data sources and the lack of lineage forces data consumers to manually aggregate information from sources they trust. In this session, you’ll learn how to future-proof your modern data environment to meet the needs of the business for the long term. We'll examine how to overcome common challenges, the related must-have technology solutions in the data lake/ data warehousing world, using real-world success stories and even a few architecture tips from industry experts.
Building Your Enterprise Data Marketplace with DMX-hPrecisely
In the past few years third-party data marketplaces, often provided as Data as a Service, have taken off. But most organizations already own the data most relevant to their business – data pertaining to their own customers, transactions, products, etc.
That’s why the most successful organizations are applying the concepts of external data markets to create their own enterprise data marketplaces, where users can easily find and access data from across the company that is clean, trustworthy and auditable.
View this webinar on-demand to learn how to build an enterprise data marketplace of your own with DMX-h! We'll cover:
• Attributes of a successful enterprise data marketplace
• Potential roadblocks, and how to overcome them
• Examples of customers who have successfully built data marketplaces with DMX-h
Traditional BI vs. Business Data Lake – A ComparisonCapgemini
Traditional Business Intelligence (BI) systems provide various levels and kinds of analyses on structured data but they are not designed to handle unstructured data.
For these systems Big Data brings big problems because the data that flows in may be either structured or unstructured. That makes them hugely limited when it comes to delivering Big Data benefits.
The way forward is a complete rethink of the way we use BI - in terms of how the data is ingested, stored and analyzed.
More information: http://www.capgemini.com/big-data-analytics/pivotal
From Business Intelligence to Big Data - hack/reduce Dec 2014Adam Ferrari
Talk given on Dec. 3, 2014 at MIT, sponsored by Hack/Reduce. This talk looks at the history of Business Intelligence from first generation OLAP tools through modern Data Discovery and visualization tools. And looking forward, what can we learn from that evolution as numerous new tools and architectures for analytics emerge in the Big Data era.
Moving from data to insights: How to effectively drive business decisions & g...Cloudera, Inc.
Firms have become obsessed with data. But the key to competitive advantage is not just more or bigger data or big data technology, it is finding actionable insights from all the data as well as embedding insight in processes and applications. This requires a change in your approach - modernized architecture and embedding insights and data in you business decisions It also requires a change in how your people work systematically to find, test and implement insights. In this webinar, Forrester Vice President and Principal Analyst Brian Hopkins will present results from two years of research into these ideas and recommend to attendees how they can get the most out of their data and analytics to drive effective business decisions and gain competitive advantage.
Analyst Keynote: Forrester: Data Fabric Strategy is Vital for Business Innova...Denodo
Watch full webinar here: https://bit.ly/36GEuJO
Traditional data integration is falling short to meet new business requirements - real-time connected data, self-service, automation, speed, and intelligence. Forrester analyst will explain how data fabric is emerging as a hot new market for an intelligent and unified platform.
Data Democratization for Faster Decision-making and Business Agility (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3ogsO7F
Presented at 3rd Chief Digital Officer Asia Summit
The idea behind Data democratization is to enable every type of user in a company to have access to data and to ensure that there is no dependency on any single party that might create a bottleneck to data access. But this is easier said than done especially given the complex data management landscape that most organizations have today. Data virtualization is a modern data integration technique that not only delivers data in real time without replication but also simplifies data discovery, data exploration and navigating between related data sets.
In this on-demand session, you will understand how data virtualization enables enterprises to:
- Reduce up to 80% the time required to deliver data to the business adapted to the needs of each user
- Apply consistent security and governance policies across the self-service data delivery process
- Seamlessly implement the concept of 'Data Marketplace'
THE FUTURE OF DATA: PROVISIONING ANALYTICS-READY DATA AT SPEEDwebwinkelvakdag
Data lakes & data warehouses, whether on-premises or in the cloud promise to provide a centralized, cost-effective and scalable foundation for modern analytics. However, organisations continue to struggle to deliver accurate, current and analytics-ready data sets in a timely fashion. Traditional ingestion tools weren’t designed to handle hundreds or even thousands of data sources and the lack of lineage forces data consumers to manually aggregate information from sources they trust. In this session, you’ll learn how to future-proof your modern data environment to meet the needs of the business for the long term. We'll examine how to overcome common challenges, the related must-have technology solutions in the data lake/ data warehousing world, using real-world success stories and even a few architecture tips from industry experts.
Building Your Enterprise Data Marketplace with DMX-hPrecisely
In the past few years third-party data marketplaces, often provided as Data as a Service, have taken off. But most organizations already own the data most relevant to their business – data pertaining to their own customers, transactions, products, etc.
That’s why the most successful organizations are applying the concepts of external data markets to create their own enterprise data marketplaces, where users can easily find and access data from across the company that is clean, trustworthy and auditable.
View this webinar on-demand to learn how to build an enterprise data marketplace of your own with DMX-h! We'll cover:
• Attributes of a successful enterprise data marketplace
• Potential roadblocks, and how to overcome them
• Examples of customers who have successfully built data marketplaces with DMX-h
Traditional BI vs. Business Data Lake – A ComparisonCapgemini
Traditional Business Intelligence (BI) systems provide various levels and kinds of analyses on structured data but they are not designed to handle unstructured data.
For these systems Big Data brings big problems because the data that flows in may be either structured or unstructured. That makes them hugely limited when it comes to delivering Big Data benefits.
The way forward is a complete rethink of the way we use BI - in terms of how the data is ingested, stored and analyzed.
More information: http://www.capgemini.com/big-data-analytics/pivotal
From Business Intelligence to Big Data - hack/reduce Dec 2014Adam Ferrari
Talk given on Dec. 3, 2014 at MIT, sponsored by Hack/Reduce. This talk looks at the history of Business Intelligence from first generation OLAP tools through modern Data Discovery and visualization tools. And looking forward, what can we learn from that evolution as numerous new tools and architectures for analytics emerge in the Big Data era.
Moving from data to insights: How to effectively drive business decisions & g...Cloudera, Inc.
Firms have become obsessed with data. But the key to competitive advantage is not just more or bigger data or big data technology, it is finding actionable insights from all the data as well as embedding insight in processes and applications. This requires a change in your approach - modernized architecture and embedding insights and data in you business decisions It also requires a change in how your people work systematically to find, test and implement insights. In this webinar, Forrester Vice President and Principal Analyst Brian Hopkins will present results from two years of research into these ideas and recommend to attendees how they can get the most out of their data and analytics to drive effective business decisions and gain competitive advantage.
his webinar will introduce a positioning, messaging, and social product marketing and storytelling blueprint for B2B marketing teams to position, orchestrate and realize message authority. Specific focus on connecting target content strategy with social and web conversations. Includes a short demonstration of new digital marketing cloud technology to support capturing, testing, and orchestrating message authority and related content.
Presenter Bio:
David P. Butler
Founder and CEO, iPositioning Inc.
My social profiles:
https://twitter.com/david_p_butler
www.linkedin.com/in/davidpbutler1
Creative, hands-on technology marketing executive and visionary with:
- Extensive experience defining and implementing market and product positioning and strategy in leading and emerging B2B software technology companies
- Social marketing strategy and execution for increasing social awareness, engagement, conversions, and relationships
- Senior management and leading organizational roles in start-up and large companies
- Specialties: Product Positioning and Strategy, Technology Evangelism, Product Management, Product Marketing, Digital Marketing, and Marketing Communications.
- Broad software marketing industry experiences in cloud computing, enterprise applications and infrastructure, business intelligence and analytics, and software development. Including: Eucalytpus, HP Software, Systinet, Spotfire, Netscape, NeXT Software.
MSP Positioning & Messaging | How to differentiate your MSP business to win m...David Castro
MSP positioning and messaging best practices. How to differentiate your MSP business to win more customers. Presented by Kaseya and MSPSalesPros. Feb 2013.
Customer/ Partner Briefing Template for Executive AssistantsOfficepal
This template is a useful resource for executive assistants who need to brief their executives regularly about customer and partner meetings. This template was authored by Debbie Gross, Chief Executive Assistant to Chairman & CEO, Cisco Systems.
The Message Map is a visual aid. It allows you to prepare and to organize answers to the questions you are most likely to hear from the news media and from the public during a crisis. It is based on research that looked into how people process information when they are under stress.
To download the editable version of this document, go to www.slidebooks.com
Market & competitor analysis template in PPT created by former Deloitte & McKinsey management consultants and talented designers.
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sector’s leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoop’s free and open-source path.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Data Lakes are early in the Gartner hype cycle, but companies are getting value from their cloud-based data lake deployments. Break through the confusion between data lakes and data warehouses and seek out the most appropriate use cases for your big data lakes.
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATAijseajournal
Data analytics and Business Intelligence (BI) are essential components of decision support technologies that gather and analyze data for faster and better strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between data offering insights. The major difference between BI and analytics is that analytics has predictive competence which helps in making future predictions whereas Business Intelligence helps in informed decision-making built on the analysis of past data. Business Intelligence solutions are among the most valued data management tools whose main objective is to enable interactive access to real-time data, manipulation of data and provide business organizations with appropriate analysis. Business Intelligence solutions leverage software and services to collect and transform raw data into useful information that enable more informed and quality business decisions regarding customers, market competitors, internal operations and so on. Data needs to be integrated from disparate sources in order to derive valuable insights. Extract-Transform-Load (ETL), which are traditionally employed by organizations help in extracting data from different sources, transforming and aggregating and finally loading large volume of data into warehouses. Recently Data virtualization has been used to speed up the data integration process. Data virtualization and ETL often serve unique and complementary purposes in performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.
Go from data to decision in one unified platform.pdfwebmaster553228
According to IDC’s January 2022 Worldwide CEO Survey, 65% of organizations are using at least 10 different data engineering and intelligence tools to integrate data.
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Real time responses to events will be feasible when enterprises are designed to be maneuverable and their flow of activity is not disrupted by a breakdown in any one component in the chain of business processes that enable the completion of an activity.
Semantic 'Radar' Steers Users to Insights in the Data LakeThomas Kelly, PMP
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Data has become one of the most valuable commodities in the world, and it can make or break a business in no time. The DataOps approach to data management is the newest and most advanced. Technology and processes in an organization can be merged with business processes through DataOps
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
Slow Data Kills Business eBook - Improve the Customer ExperienceInterSystems
We live in an era where customer experience trumps product features and functions. How do you exceed customer’s expectations every time they interact with your organization? By leveraging more information and applying insights you have learned over time. Turning data-driven power into delightful experiences will give you the advantages required to succeed in today’s climate of one-click shopping and crowd-sourced feedback. Whether you are a retailer, a banker, a care provider, or a policy maker, your organization must harness the power of growing data volumes, data types, and data sources to foster experiences that matter.
Similar to intelligent-data-lake_executive-brief (20)
Slow Data Kills Business eBook - Improve the Customer Experience
intelligent-data-lake_executive-brief
1. Executive Brief
Intelligent Data Lake
Find, Prepare, and Govern Data for Analysis in a
Uniquely Collaborative Way That Enables Businesses to
Make Decisions Even Faster
Data has undoubtedly become the fuel for competitive advantage in the 21st century.
Organizations are looking to harness new data processing platforms such as Apache Hadoop
to derive previously unattainable—if not inconceivable—insights. The emergence of Apache
Hadoop and the data lake concept now gives organizations the luxury of pooling all data so that
it is accessible for users at any time for any type of analysis.
Organizations are collecting customer and market data for its potential to improve experiences
and drive business growth. Financial institutions are saving and monitoring transactional data
and other related signals in order to enrich fraud detection techniques, keep up with changing
global regulations, and boost consumer trust in the security of their services. Healthcare
organizations are preserving electronic medical record data and claims data in order to drive
more personalized healthcare. The opportunity to harness data has never been greater with big
data technologies.
The challenge
The sheer volume of data being ingested into Hadoop systems is overwhelming IT. Business
analysts eagerly await quality data from Hadoop. Meanwhile, IT is burdened with manual, time-
intensive processes to curate raw data into fit-for-purpose data assets. Big data cannot deliver
on its promise if it brings progress to a grinding halt because of complex technologies and
additional resources required to extract value.
Without scalable, repeatable, and intelligent mechanisms for curating data, all the opportunity
that data lakes promise risks stagnation. The capability to turn big data into valuable business
insights with the right data delivered at the right time is, ultimately, what will separate
organizational forerunners from laggards.
The solution
Data lakes on their own are merely means to an end. To achieve the end goal of delivering
business insights, you need machine intelligence driven by universal metadata services. Universal
metadata services catalog the metadata attached to data, both inside and outside Hadoop, as
well as capture user-provided tags about the business context of the data.
Business insights flow from an otherwise inert data lake through the added value derived
from the cataloging of both the quality and the state of the data inside the data lake as
well as the collaborative self-service data preparation capabilities applied to that data.
Thus, the Intelligent Data Lake enables raw big data to be systematically transformed into
fit-for-purpose data sets for a variety of data consumers. With such an implementation,
organizations can quickly and repeatably turn big data into trusted information assets that
deliver sustainable business value.
Solution Benefits
Informatica Big Data
Management provides
the gold standard in data
management solutions
• Find any data and
relationships that matter
• Quickly prepare and
share the data you need
• Get more trusted insights
from more data without
more risk