The document provides an overview of the China data management solutions market. It defines data management solutions as systems that effectively collect, store, analyze and apply massive amounts of data to extract valuable information and support enterprise decisions. Typical applications include data warehouses for structured data and analytics, and data lakes for storage of large volumes of raw data. The market is expected to continue expanding due to policies supporting data use and more data application scenarios. Key trends include increased cloud deployment, and integration of data lakes and warehouses.
Data Management Meets Human Management - Why Words MatterDATAVERSITY
This document discusses data governance at Fifth Third Bank and how the Vice President of Enterprise Data, Greg Swygart, is working to improve it. It notes that previously the bank did not have a strong data culture or data literacy. Greg is implementing a centralized data management program to develop these areas using best practices. He is focusing on adoption of the Alation data catalog to help formalize data stewardship and accountability. The document emphasizes that human management and changing behaviors and mindsets is key to successful data governance, and that words used are important to avoid making it feel like a burden.
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.
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
This document discusses how to create a data governance dashboard by connecting it to Trillium Software's data quality platform. It recommends including business rule metadata, the rules library, decision points, and time series analysis in the dashboard. It demonstrates how to use the OLE DB provider to abstract the platform's architecture and define tables to retrieve metrics, rules results, metadata, and more. Connecting the dashboard to the repository in this way allows efficient ongoing monitoring of data quality.
Agile BI: How to Deliver More Value in Less TimePerficient, Inc.
Learn how to:
Construct a BI and analytical environment that provides the critical functionality that enables your customers to provide timely answers, supporting modern agile business
Leverage agile delivery concepts to deliver value in days rather than in months
Build a support organization that enables your users to create increased value from your company’s information assets
This whitepaper outlines a reference architecture for real-time data governance using DataOps principles. The architecture defines logical units including lines of business, spaces, datasets, actors and identities to enable federated governance over heterogeneous systems. It allows each line of business to be aligned with the appropriate data, applications, rules and processes. The architecture specifies roles for access management and enforcement of governance policies across business units and technical environments.
Designed to address more mature programs, this tutorial covers the issues and approaches to sustaining Data Governance and value creation over time, amongst a changing business and personnel environment.
Part of the reason many companies launch a Data Governance program again and again is that over time, it is challenging to maintain the enthusiasm and excitement that accompanies a newly initiated program.
Learn about:
• Typical obstacles to sustainable Data Governance
• Re-energizing your program after a key player (or two) leave and other personnel challenges
• Staying relevant to the company as the business evolves over time
• Understanding the role of metrics and why they are critical
• Leveraging Communication and Stakeholder Management practices to maintain commitment
• Embedding Data Governance into the operations of the company
Data Management Meets Human Management - Why Words MatterDATAVERSITY
This document discusses data governance at Fifth Third Bank and how the Vice President of Enterprise Data, Greg Swygart, is working to improve it. It notes that previously the bank did not have a strong data culture or data literacy. Greg is implementing a centralized data management program to develop these areas using best practices. He is focusing on adoption of the Alation data catalog to help formalize data stewardship and accountability. The document emphasizes that human management and changing behaviors and mindsets is key to successful data governance, and that words used are important to avoid making it feel like a burden.
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.
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
This document discusses how to create a data governance dashboard by connecting it to Trillium Software's data quality platform. It recommends including business rule metadata, the rules library, decision points, and time series analysis in the dashboard. It demonstrates how to use the OLE DB provider to abstract the platform's architecture and define tables to retrieve metrics, rules results, metadata, and more. Connecting the dashboard to the repository in this way allows efficient ongoing monitoring of data quality.
Agile BI: How to Deliver More Value in Less TimePerficient, Inc.
Learn how to:
Construct a BI and analytical environment that provides the critical functionality that enables your customers to provide timely answers, supporting modern agile business
Leverage agile delivery concepts to deliver value in days rather than in months
Build a support organization that enables your users to create increased value from your company’s information assets
This whitepaper outlines a reference architecture for real-time data governance using DataOps principles. The architecture defines logical units including lines of business, spaces, datasets, actors and identities to enable federated governance over heterogeneous systems. It allows each line of business to be aligned with the appropriate data, applications, rules and processes. The architecture specifies roles for access management and enforcement of governance policies across business units and technical environments.
Designed to address more mature programs, this tutorial covers the issues and approaches to sustaining Data Governance and value creation over time, amongst a changing business and personnel environment.
Part of the reason many companies launch a Data Governance program again and again is that over time, it is challenging to maintain the enthusiasm and excitement that accompanies a newly initiated program.
Learn about:
• Typical obstacles to sustainable Data Governance
• Re-energizing your program after a key player (or two) leave and other personnel challenges
• Staying relevant to the company as the business evolves over time
• Understanding the role of metrics and why they are critical
• Leveraging Communication and Stakeholder Management practices to maintain commitment
• Embedding Data Governance into the operations of the company
EY has a large and growing graph practice with over 200 consultants globally. They see widespread use of graph technologies across many sectors and have delivered graph solutions to help clients drive insight, efficiency, and value. The document discusses trends driving graph adoption, graph leaders in the market, and EY's point of view on building data fabrics and knowledge graphs to connect and mobilize enterprise data.
This document discusses an agile solution for enterprise data modeling and data management provided by A.I. Consultancy Limited and Pacific Rim Telecomm Datacomm Ltd. It outlines the benefits of enterprise data modeling, problems with traditional top-down approaches, and their hybrid agile solution using off-the-shelf modeling tools. Their solution aims to deliver initial data models quickly and support ongoing data governance through modular implementation and tailored training.
Using Data Platforms That Are Fit-For-PurposeDATAVERSITY
We must grow the data capabilities of our organization to fully deal with the many and varied forms of data. This cannot be accomplished without an intense focus on the many and growing technical bases that can be used to store, view, and manage data. There are many, now more than ever, that have merit in organizations today.
This session sorts out the valuable data stores, how they work, what workloads they are good for, and how to build the data foundation for a modern competitive enterprise.
Revolution In Data Governance - Transforming the customer experiencePaul Dyksterhouse
The foundation of managing data security and big data is implementing data governance. Data Owners, Metadata tagging, Customer feedback and Continuous Improvement are critical facets to provide the transparency and consistency so that customer's can trust the data, and make informed decisions.
ADV Slides: The Evolution of the Data Platform and What It Means to Enterpris...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here?
In this webinar, we look at this foundational technology for modern Data Management and show how it evolved to meet the workloads of today, as well as when other platforms make sense for enterprise data.
TDWI Spotlight: Enabling Data Self-Service with Security, Governance, and Reg...Denodo
Watch full webinar here: https://bit.ly/3xozd5W
Companies today want to realize the value of data and share it across the enterprise. While unlocking the full potential of data for business users, these companies must also ensure that they maintain security requirements. Learn how you can successfully implement self-service initiatives with data governance to enable both business and IT to realize the full potential of any data in the enterprise.
Watch Now On-Demand!
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
The document discusses Mainline's Storage Strategy Workshop service. The workshop helps storage and IT teams develop a strategic plan to guide infrastructure decisions. It involves identifying business challenges, current projects, team skills, objectives, and a vision and mission statement. This ensures the team is proactive in addressing issues rather than reactive. The strategy provides guidance that empowers both management and staff. It is meant to be fluid and change over time in response to evolving needs.
Learn about the three advances in database technologies that eliminate the need for star schemas and the resulting maintenance nightmare.
Relational databases in the 1980s were typically designed using the Codd-Date rules for data normalization. It was the most efficient way to store data used in operations. As BI and multi-dimensional analysis became popular, the relational databases began to have performance issues when multiple joins were requested. The development of the star schema was a clever way to get around performance issues and ensure that multi-dimensional queries could be resolved quickly. But this design came with its own set of problems.
Unfortunately, the analytic process is never simple. Business users always think up unimaginable ways to query the data. And the data itself often changes in unpredictable ways. These result in the need for new dimensions, new and mostly redundant star schemas and their indexes, maintenance difficulties in handling slowly changing dimensions, and other problems causing the analytical environment to become overly complex, very difficult to maintain, long delays in new capabilities, resulting in an unsatisfactory environment for both the users and those maintaining it.
There must be a better way!
Watch this webinar to learn:
- The three technological advances in data storage that eliminate star schemas
- How these innovations benefit analytical environments
- The steps you will need to take to reap the benefits of being star schema-free
Chief Data Officer (CDO) Organization RolesDave Getty
If your Company wants to treat Data as an Asset, it needs a Chief Data Officer to initiate significant changes in the Roles and Responsibilities of the Data Governance, IT Data Management and Business Analyst Data Scientist organizations. This presentation describes how the resulting organizations might look and behave.
The opportunity of the business data lakeCapgemini
The document discusses how the Pivotal Business Data Lake provides a solution for digital transformation by addressing issues with traditional single enterprise data warehouse approaches. It does this through four key tenets: storing all information, encouraging local views of the data, governing only common data, and treating global views as local. This allows businesses to access and analyze data in ways that fit their needs and culture rather than being constrained by IT systems. The Business Data Lake is a collaboration between Pivotal and Capgemini to deliver a new approach combining supportive technology and business-centric governance of information.
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
Measuring Data Quality Return on InvestmentDATAVERSITY
Data Quality is an elusive subject that can defy measurement and yet be critical enough to derail any project, strategic initiative, or even a company. The data layer of an organization is a critical component because it is so easy to ignore the quality of that data or to make overly optimistic assumptions about its efficacy. Having Data Quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. It is a competitive strategy.
The Shifting Landscape of Data IntegrationDATAVERSITY
This document discusses the shifting landscape of data integration. It begins with an introduction by William McKnight, who is described as the "#1 Global Influencer in Data Warehousing". The document then discusses how challenges in data integration are shifting from dealing with volume, velocity and variety to dealing with dynamic, distributed and diverse data in the cloud. It also discusses IDC's view that this shift is occurring from the traditional 3Vs to the 3Ds. The rest of the document discusses Matillion, a vendor that provides a modern solution for cloud data integration challenges.
Mainframe users are continuously challenged to keep pace with rising data volumes from distributed applications that depend on mainframe transaction processing power. The pressure to squeeze more performance and value out of existing mainframes, while avoiding or deferring major upgrades, never stops.
There are ways to improve the efficiency of core workloads, like sorting, that help you uncover additional capacity, save money, and increase the ROI for mainframe expenditures. In addition, you can deliver more value to your business by integrating mainframe data into next-generation cloud and data platforms like Databricks, Snowflake, Splunk, ServiceNow, and more.
To ensure that Decision Management Systems are analytic and adaptive you must embed the results of data mining and predictive analytics in them. In this webinar you will learn what can be discovered using data mining and predictive analytic techniques and how this can be applied to the decision-making embedded in Decision Management Systems. The role of analytics in predicting risk, fraud and opportunity and the importance of continuous improvement and learning is also be covered.
Linking Data Governance to Business GoalsPrecisely
This document discusses linking data governance to business goals. It begins with an example of a typical governance program that loses business support over time. It then advocates taking a business-first approach to accelerate programs and increase ROI. Successful programs link governance to business goals, outcomes, stakeholders and capabilities. The document provides examples of how different business goals map to governance objectives and capabilities. It emphasizes quantifying value at strategic, operational and tactical levels. Finally, it discusses Jean-Paulotte Group's Chief Data Officer implementing a working approach driven by business value through an iterative process between a Data Management Committee and Working Groups.
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-relationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...DATAVERSITY
Operational Data Governance is more than a stewardship process for critical Business Assets. As organizations build structure around KPI’s and other critical data, a workflow develops that revolves around the sources and supply chain for that critical data. There can be many aspects to changes and inconsistencies affecting the final results of the supply chain. Inaccurate usage of data can result in audit penalties as well as erroneous report summaries and conclusions.
Is it coming from the correct authoritative source? Has the data been profiled? Has it met it’s threshold?
Gaps in the supply chain from incorrect pathways may lead dead ends or lost sources.
The value of understanding the entire supply chain cannot be overstated. When changes occur at and point, end users can validate that correct business standards, rules and policies have been applied to the critical data within the supply chain. Your organization can rest easy that you are not at risk for exposure due to improper usage, security, and compliance.
Join this webinar to uncover how companies are using data lineage to accomplish data supply chain transparency. You’ll also see the direct value clear data lineage can give to your business and IT landscape today.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
The Comparison of Big Data Strategies in Corporate EnvironmentIRJET Journal
The document discusses and compares different big data strategies that corporations can use to handle large volumes of data. It analyzes traditional relational database management systems (RDBMS), MapReduce techniques, and a hybrid approach. While each strategy has benefits, the hybrid approach that combines traditional databases and MapReduce is identified as being most valuable for companies pursuing business analytics, as it allows for efficiently handling both structured and unstructured data at large scales. The document provides an overview of these strategies and their suitability based on different corporate needs and environments.
The Economic Value of Data: A New Revenue Stream for Global CustodiansCognizant
Global custodians' big data offers myriad opportunities for generating value from analytics solutions; we explore various paths and offer three use cases to illustrate. Data aggregation, risk management, digital experience, operational agility and cross-selling are all covered.
EY has a large and growing graph practice with over 200 consultants globally. They see widespread use of graph technologies across many sectors and have delivered graph solutions to help clients drive insight, efficiency, and value. The document discusses trends driving graph adoption, graph leaders in the market, and EY's point of view on building data fabrics and knowledge graphs to connect and mobilize enterprise data.
This document discusses an agile solution for enterprise data modeling and data management provided by A.I. Consultancy Limited and Pacific Rim Telecomm Datacomm Ltd. It outlines the benefits of enterprise data modeling, problems with traditional top-down approaches, and their hybrid agile solution using off-the-shelf modeling tools. Their solution aims to deliver initial data models quickly and support ongoing data governance through modular implementation and tailored training.
Using Data Platforms That Are Fit-For-PurposeDATAVERSITY
We must grow the data capabilities of our organization to fully deal with the many and varied forms of data. This cannot be accomplished without an intense focus on the many and growing technical bases that can be used to store, view, and manage data. There are many, now more than ever, that have merit in organizations today.
This session sorts out the valuable data stores, how they work, what workloads they are good for, and how to build the data foundation for a modern competitive enterprise.
Revolution In Data Governance - Transforming the customer experiencePaul Dyksterhouse
The foundation of managing data security and big data is implementing data governance. Data Owners, Metadata tagging, Customer feedback and Continuous Improvement are critical facets to provide the transparency and consistency so that customer's can trust the data, and make informed decisions.
ADV Slides: The Evolution of the Data Platform and What It Means to Enterpris...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here?
In this webinar, we look at this foundational technology for modern Data Management and show how it evolved to meet the workloads of today, as well as when other platforms make sense for enterprise data.
TDWI Spotlight: Enabling Data Self-Service with Security, Governance, and Reg...Denodo
Watch full webinar here: https://bit.ly/3xozd5W
Companies today want to realize the value of data and share it across the enterprise. While unlocking the full potential of data for business users, these companies must also ensure that they maintain security requirements. Learn how you can successfully implement self-service initiatives with data governance to enable both business and IT to realize the full potential of any data in the enterprise.
Watch Now On-Demand!
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
The document discusses Mainline's Storage Strategy Workshop service. The workshop helps storage and IT teams develop a strategic plan to guide infrastructure decisions. It involves identifying business challenges, current projects, team skills, objectives, and a vision and mission statement. This ensures the team is proactive in addressing issues rather than reactive. The strategy provides guidance that empowers both management and staff. It is meant to be fluid and change over time in response to evolving needs.
Learn about the three advances in database technologies that eliminate the need for star schemas and the resulting maintenance nightmare.
Relational databases in the 1980s were typically designed using the Codd-Date rules for data normalization. It was the most efficient way to store data used in operations. As BI and multi-dimensional analysis became popular, the relational databases began to have performance issues when multiple joins were requested. The development of the star schema was a clever way to get around performance issues and ensure that multi-dimensional queries could be resolved quickly. But this design came with its own set of problems.
Unfortunately, the analytic process is never simple. Business users always think up unimaginable ways to query the data. And the data itself often changes in unpredictable ways. These result in the need for new dimensions, new and mostly redundant star schemas and their indexes, maintenance difficulties in handling slowly changing dimensions, and other problems causing the analytical environment to become overly complex, very difficult to maintain, long delays in new capabilities, resulting in an unsatisfactory environment for both the users and those maintaining it.
There must be a better way!
Watch this webinar to learn:
- The three technological advances in data storage that eliminate star schemas
- How these innovations benefit analytical environments
- The steps you will need to take to reap the benefits of being star schema-free
Chief Data Officer (CDO) Organization RolesDave Getty
If your Company wants to treat Data as an Asset, it needs a Chief Data Officer to initiate significant changes in the Roles and Responsibilities of the Data Governance, IT Data Management and Business Analyst Data Scientist organizations. This presentation describes how the resulting organizations might look and behave.
The opportunity of the business data lakeCapgemini
The document discusses how the Pivotal Business Data Lake provides a solution for digital transformation by addressing issues with traditional single enterprise data warehouse approaches. It does this through four key tenets: storing all information, encouraging local views of the data, governing only common data, and treating global views as local. This allows businesses to access and analyze data in ways that fit their needs and culture rather than being constrained by IT systems. The Business Data Lake is a collaboration between Pivotal and Capgemini to deliver a new approach combining supportive technology and business-centric governance of information.
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
Measuring Data Quality Return on InvestmentDATAVERSITY
Data Quality is an elusive subject that can defy measurement and yet be critical enough to derail any project, strategic initiative, or even a company. The data layer of an organization is a critical component because it is so easy to ignore the quality of that data or to make overly optimistic assumptions about its efficacy. Having Data Quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. It is a competitive strategy.
The Shifting Landscape of Data IntegrationDATAVERSITY
This document discusses the shifting landscape of data integration. It begins with an introduction by William McKnight, who is described as the "#1 Global Influencer in Data Warehousing". The document then discusses how challenges in data integration are shifting from dealing with volume, velocity and variety to dealing with dynamic, distributed and diverse data in the cloud. It also discusses IDC's view that this shift is occurring from the traditional 3Vs to the 3Ds. The rest of the document discusses Matillion, a vendor that provides a modern solution for cloud data integration challenges.
Mainframe users are continuously challenged to keep pace with rising data volumes from distributed applications that depend on mainframe transaction processing power. The pressure to squeeze more performance and value out of existing mainframes, while avoiding or deferring major upgrades, never stops.
There are ways to improve the efficiency of core workloads, like sorting, that help you uncover additional capacity, save money, and increase the ROI for mainframe expenditures. In addition, you can deliver more value to your business by integrating mainframe data into next-generation cloud and data platforms like Databricks, Snowflake, Splunk, ServiceNow, and more.
To ensure that Decision Management Systems are analytic and adaptive you must embed the results of data mining and predictive analytics in them. In this webinar you will learn what can be discovered using data mining and predictive analytic techniques and how this can be applied to the decision-making embedded in Decision Management Systems. The role of analytics in predicting risk, fraud and opportunity and the importance of continuous improvement and learning is also be covered.
Linking Data Governance to Business GoalsPrecisely
This document discusses linking data governance to business goals. It begins with an example of a typical governance program that loses business support over time. It then advocates taking a business-first approach to accelerate programs and increase ROI. Successful programs link governance to business goals, outcomes, stakeholders and capabilities. The document provides examples of how different business goals map to governance objectives and capabilities. It emphasizes quantifying value at strategic, operational and tactical levels. Finally, it discusses Jean-Paulotte Group's Chief Data Officer implementing a working approach driven by business value through an iterative process between a Data Management Committee and Working Groups.
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-relationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...DATAVERSITY
Operational Data Governance is more than a stewardship process for critical Business Assets. As organizations build structure around KPI’s and other critical data, a workflow develops that revolves around the sources and supply chain for that critical data. There can be many aspects to changes and inconsistencies affecting the final results of the supply chain. Inaccurate usage of data can result in audit penalties as well as erroneous report summaries and conclusions.
Is it coming from the correct authoritative source? Has the data been profiled? Has it met it’s threshold?
Gaps in the supply chain from incorrect pathways may lead dead ends or lost sources.
The value of understanding the entire supply chain cannot be overstated. When changes occur at and point, end users can validate that correct business standards, rules and policies have been applied to the critical data within the supply chain. Your organization can rest easy that you are not at risk for exposure due to improper usage, security, and compliance.
Join this webinar to uncover how companies are using data lineage to accomplish data supply chain transparency. You’ll also see the direct value clear data lineage can give to your business and IT landscape today.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
The Comparison of Big Data Strategies in Corporate EnvironmentIRJET Journal
The document discusses and compares different big data strategies that corporations can use to handle large volumes of data. It analyzes traditional relational database management systems (RDBMS), MapReduce techniques, and a hybrid approach. While each strategy has benefits, the hybrid approach that combines traditional databases and MapReduce is identified as being most valuable for companies pursuing business analytics, as it allows for efficiently handling both structured and unstructured data at large scales. The document provides an overview of these strategies and their suitability based on different corporate needs and environments.
The Economic Value of Data: A New Revenue Stream for Global CustodiansCognizant
Global custodians' big data offers myriad opportunities for generating value from analytics solutions; we explore various paths and offer three use cases to illustrate. Data aggregation, risk management, digital experience, operational agility and cross-selling are all covered.
Bardess Moderated - Analytics and Business Intelligence - Society of Informat...bardessweb
Joe DeSiena, President of Bardess Group Ltd moderated a panel of Information Technology executives titled Analytics and Business Intelligence for the chapter meeting for the New Jersey Society of Information Management.
IMPACT OF BIG DATA ON BUSINESS DECISIONS THROUGH THE VIEW OF DATASCIENCE-BASE...IRJET Journal
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Le invitamos a participar en este webinar para aprender:
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the enormous volume of data in numerous environments; • the wide variety of data types regularly stored in big data systems, and
the velocity at which a significant part of the data is created, gathered and processed.
These characteristics were first recognized in 2001 by Doug Laney, then, at that point, an analyst at consulting firm Meta Group Inc.; Gartner further promoted them after it gained Meta Group in 2005. All the more as of late, several other V's have been added to various descriptions of big data, including veracity, value and variability.
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Watch full webinar here: https://bit.ly/3Ab9gYq
Imagina llegar a un parque de atracciones con tu familia y comenzar tu día sin el típico plano que te permitirá planificarte para saber qué espectáculos ver, a qué atracciones ir, donde pueden o no pueden montar los niños… Posiblemente, no podrás sacar el máximo partido a tu día y te habrás perdido muchas cosas. Hay personas que les gusta ir a la aventura e ir descubriendo poco a poco, pero cuando hablamos de negocios, ir a la aventura puede ser fatídico...
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China data-mngnt-solution-market-report
1. 1
2020
China Data Management Solutions Market Report
2020年中国数据管理解决方案市场报告
2020年中国ビッグデータ管理市場研究
Tags: Big Data, Data Management Solutions, Data Lake, Data Warehouse
2021/04
Any content provided in the report (including but not limited to data, text, charts, images, etc.) is the exclusive and highly confidential document of
LeadLeo Research Institute (unless the source is otherwise indicated in the report). Without the prior written permission of LeadLeo Research Institute, no
one is allowed to copy, reproduce, disseminate, publish, quote, adapt or compile the contents of this report in any way. If any behaviour violating the
above agreement occurs, LeadLeo Research Institute reserves the right to take legal measures and hold relevant personnel responsible. LeadLeo Research
Institute uses “LeadLeo Research Institute” or “LeadLeo” trade name or trademark in all business activities conducted by LeadLeo Research Institute.
LeadLeo Research Institute neither has other branches other than the aforementioned name nor does it authorize or employ any other third party to carry
out business activities on behalf of LeadLeo Research Institute.
LeadLeo Research Institute
Frost & Sullivan (China)
Sullivan Market Report| 2021/4
22. 22
Development Prospect of China
Data Management Solution Market
uKey Milestones
uCloud Deployment
uIntegration of Data Lake and Data Warehouse
uDeepening Application Scenarios of Data
Management Solution
04
31. 31
Sullivan Market Report | 2021/4 China：Data Management Series
Leader - Amazon Web Services
Amazon Web Services is the leader in DMS in China providing technological innovation, global
business practices, flexible data management, cloud security and strong business ecosystem
Amazon Athena is an
interactive query service that
makes it easy to analyze data in
Amazon S3 using standard SQL
Amazon Elasticsearch Service：Fully managed,
scalable, and secure Elasticsearch service
• provides support for open source Elasticsearch
APIs, managed Kibana, integration with
Logstash and other Amazon Web Services
services, and built-in alerting and SQL
querying.
Amazon EMR：Cloud big data platform
Easily run and scale Apache Spark, Hive,
Presto, and other big data frameworks
• Scale your big data environments by
automating time-consuming tasks like
provisioning capacity and tuning clusters.
Amazon Aurora： Relational database built for the cloud
Performance and availability of commercial-grade databases
at 1/10th the cost.
A distributed, fault-tolerant, self-healing storage system
• a fully managed, multi-region, multi-
active, durable database with built-in
security, backup and restore, and in-
memory caching for internet-scale
applications.
Amazon SageMaker：Machine learning
for every data scientist and developer
• helps data scientists and developers to
prepare, build, train, and deploy high-
quality machine learning (ML) models
quickly by bringing together a broad
set of capabilities purpose-built for ML.
Amazon Redshift：Analyze all of your data
with the fastest and most widely used cloud
data warehouse
• Make query and combine exabytes of
structured and semi-structured data across
your data warehouse, operational database,
and data lake using standard SQL.
Lake House Architecture Overview
Amazon Web Services Glue is
a serverless data integration
service that makes it easy to
discover, prepare, and combine
data for analytics, machine
learning, and application
development.
Amazon Web Services Lake
Formation is a service that
makes it easy to set up a secure
data lake in days.
Amazon
S3
Amazon
Athena
Data Gravity Data Lake
Amazon DynamoDB：Fast and flexible
NoSQL database service for any scale
n Technological Innovation: Data lakehouse architecture
§ Integrating AI technology with data management functions: the high-availability
architecture of data management services, security authentication and fine-grained
monitoring, storage and computing separation, and automatic resource expansion.
§ Amazon QuickSight Q（A machine learning powered capability that uses natural language
processing to answer your business questions instantly）
n Global Business Practice: Customized data management service
§ Amazon Web Services combines the best of the global practices with the market conditions
in China, provides customized data management services to different industry
n Flexible data management: High Usability
§ Amazon Web Services is capable of node configuration, software configuration, automated
indexing and extraction, data isolation and security, industry compliance, cluster sizing,
automatic patching, alarm and detection, and hardware maintenance
n Cloud Security: Shared responsibility model
§ Amazon Web Services responsibility “Security of the Cloud”: hardware, software,
networking, and facilities that run Amazon Web Services Cloud services.
§ Customer responsibility “Security in the Cloud” : operating system, network, firewall
configuration, application, identity & access management and customer data
n Business Ecosystem: Customer Enablement
§ Migrate and build faster in the cloud with Amazon Web Services Customer Enablement
services. Augment your team’s cloud skills with deep Amazon Web Services expertise where,
when, and how you need it.
32. 32
Sullivan Market Report | 2021/4 China：Data Management Series
n Technology Innovation：MRS combines three types of Data Lake
§ Offline Data Lake: Open format storage engine, Diversity engine, Support multiple
analysis workloads, Data lakehouse realization
§ Real-time Data Lake: Real-time integration, batch stream fusion, real-time update
and delete, real-time analysis service, T+0 timeliness
§ Logic Data Lake: Data Virtualization implemented unified access and collaborative
analysis of data inside and outside the data lake
n Abundant Industry Practice: Business acumen in demand
§ Government affairs (digital transformed Shenzhen’s Longgang District; Enabling
access via one website), Operators (Smooth Migration), Finance (Collaborate across
data warehouses)
n Comprehensive Security: From infrastructure to application access
§ Network isolation (support for multi-section network security), Host security
(operating system kernel security reinforcement, etc.), Application security (user-
level access control, etc.), Data security (multi-copies backup for disaster recovery
guarantee) and security authentication (unified authentication system)
n Ultimate data management: fulfill the requirement in each functional scenarios
§ Abundant data management functions, data lake, data warehouse multi-generation
coexistence
§ Ultimate performance in data collection, collation, desensitization, analysis and
management, security
n Business Ecosystem: Customer Enablement
§ A cloud ecology that adhere to openness, cooperation and win-win benefits.
Fertilize the partners quickly integrate into the local ecology as the “black soil” in
the “intelligent earth”
§ Open community contribution （Ranked 2nd
in Hadoop, 4th
in Spark)
Leader – Huawei Cloud
Real-time
Entry into
the Lake
Incremental
Update
DWS
数据仓库
FusionInsight
Data
Source
Transactional
System
Web/Mobile
3rd Party
Social
Media
IoT
…
DLC Unified Metadata | Unified Security
GES
Graph Engine
Service
Hetu
Engine
MRS Cloud Native
ModelArts
AI Platform
DGC
Data Lake Governance Center
Data
Catalog
Data Service
Data Governance
Data integration,
development, scheduling
Storage
（OBS）
Computing
（BMS、PM、VM、Container）
Huawei Cloud
MRS MapReduce Service
MRS combines three types of Data Lake（Offline
Data Lake, Real-time Data Lake and Logic Data Lake ）
GaussDB Cloud Data Warehouse
An fully-managed and out-of-the-box analytic
database service （ employed Shared-Nothing
Architecture, massively parallel processing (MPP)
engine and cross- AZ disaster recovery）
DGC Data Lake Governance Center
A one-stop data lake operations platform
(Functions with Data Integration and Data
Development etc.）Rapidly grow your enterprise's
big data Operations (Build industry knowledge
libraries with intelligence etc.)
ModelArts AI Platform
A one-stop AI development platform that
enables developers and data scientists (data pre-
processing, semi-automated data labelling,
distributed training, and automated model building
capabilities.)
GES Graph Engine Service
Facilitates querying and analysis of graph-
structure data. Specifically suited for scenarios
requiring analysis of rich relationship data.
FusionInsight Lake House Overview
Huawei Cloud is the leader in DMS in China providing technological innovation, abundant industry
practice, comprehensive security, ultimate data management and strong business ecosystem
GaussDB
Cloud Data
Warehouse
33. 33
Sullivan Market Report | 2021/4 China：Data Management Series
DB级
元数据透视
MaxCompute Built-in optimized storage
n Technology Innovation：Data Lakehouse Architecture
§ Own PrivateAccess network connectivity technology, ability to connect across the integrated
network, faster access
§ One-key database metadata mapping technology, unified metadata service
§ Provide unified development environment , highly compatible with Hive/Spark
§ Intelligent cache technology; identify cold data and hot data; Automatic data warehouse
n Cloud Native Practice ：Enable cloud native transformation for millions of enterprises
§ In 2009, Alibaba launched the core middleware system for the first time
§ In 2011, Taobao and Tmall began to use container scheduling technology, and then launched
self-developed cloud native hardware Shenlong server and cloud native database PolarDB
§ In 2019, Double 11 Festival, Ali e-commerce core system is 100% on the cloud, which is also the
largest cloud native practice in the world
n Ultimate data management: fulfil the requirement in each functional scenarios
§ Enterprise-class high-performance data warehouse, high flexibility and agility at a lower cost
§ Complement elasticity resources and EMR cluster resources
§ Based on PAI, encapsulated many algorithm services that are close to business scenarios
n Business Ecosystem：City Brain 3.0
§ All urban elements, such as farmland, buildings and public transports will be linked through the
urban space gene pool
§ Perform intelligent decision-making of all urban scenes, such as traffic, medical care, emergency
response, people's livelihood, elderly care and public services
Leader – Alibaba Cloud
Alibaba Cloud is the leader in DMS in China providing technological innovation, cloud native
practice, ultimate data management and strong business ecosystem
Alibaba Cloud Lake House Overview
IDE Task Scheduling Data Security
Asset
Management
Data Service
Offline Computing
Service
Interactive
Computing Service
Machine Learning
Service
Deep Learning
Service
Real-time
Computing Service
MC
SQL
MC
Spark
PAI TF PAI GNN
MaxCompute Meta Service
Cache
Hive Spark Flink Presto
Hive Meta Service
Structured
Semi-
Structured
Unstructured
MaxCompute Data Warehouse Cluster Data lake Cluster
No need to move data, cross-platform computing
Hot and Cold Cache Separation
Optimized Storage
and Performance
PrivateAccess
Exclusive Channel
Hot Data
The
Middle
Layer
The
Computing
Layer
The
Storage
Layer
The
Storage
Layer
HDFS/OSS Data Lake
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