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Published By: DATAVERSITY     Published Date: Oct 04, 2016
This report evaluates each question posed in a recent survey and provides subsequent analysis in a detailed format that includes the most noteworthy statistics, direct comments from survey respondents, and the influence on the industry as a whole. It seeks to present readers with a thorough review of the state of Metadata Management as it exists today.
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DATAVERSITY
Published By: TopQuadrant     Published Date: Jun 11, 2018
Data governance is a lifecycle-centric asset management activity. To understand and realize the value of data assets, it is necessary to capture information about them (their metadata) in the connected way. Capturing the meaning and context of diverse enterprise data in connection to all assets in the enterprise ecosystem is foundational to effective data governance. Therefore, a data governance environment must represent assets and their role in the enterprise using an open, extensible and “smart” approach. Knowledge graphs are the most viable and powerful way to do this. This short paper outlines how knowledge graphs are flexible, evolvable, semantic and intelligent. It is these characteristics that enable them to: • capture the description of data as an interconnected set of information that meaningfully bridges enterprise metadata silos. • deliver integrated data governance by addressing all three aspects of data governance — Executive Governance, Representative Governance, and App
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TopQuadrant
Published By: AnalytixDS     Published Date: May 04, 2018
The General Data Protection Regulation (GDPR) is a regulation by which the European Parliament, the Council of the European Union and the European Commission intend to strengthen and unify data protection for all individuals within the European Union (EU). It also addresses the export of personal data outside the EU. The General Data Protection Regulation (GDPR) will go into effect from May 25 2018, making organizations accountable for personal data protection including how and where data is stored and how it is processed within the organization. Get ready for the most comprehensive governance and automation platform in the industry.
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AnalytixDS
Published By: Ted Hills     Published Date: Mar 08, 2017
NoSQL database management systems give us the opportunity to store our data according to more than one data storage model, but our entity-relationship data modeling notations are stuck in SQL land. Is there any need to model schema-less databases, and is it even possible? In this short white paper, Ted Hills examines these questions in light of a recent paper from MarkLogic on the hybrid data model.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
This document provides a complete reference for the Concept and Object Modeling Notation (COMN, pronounced “common”), release 1.1. The book NoSQL and SQL Data Modeling (Technics Publications, 2016) reflects release 1.0 of COMN. This is a reference, not a tutorial. This document is designed to support a quick check of how to draw or notate something in COMN.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
This paper explores the differences between three situations that appear on the surface to be very similar: a data attribute that may occur zero or one times, a data attribute that is optional, and a data attribute whose value may be unknown. It shows how each of these different situations is represented in Concept and Object Modeling Notation (COMN, pronounced “common”). The theory behind the analysis is explained in greater detail by three papers: Three-Valued Logic, A Systematic Solution to Handling Unknown Data in Databases, and An Approach to Representing Non-Applicable Data in Relational Databases.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
Ever since Codd introduced so-called “null values” to the relational model, there have been debates about exactly what they mean and their proper handling in relational databases. In this paper I examine the meaning of tuples and relations containing “null values”. For the type of “null value” representing unknown data, I propose an interpretation and a solution that is more rigorously defined than the SQL NULL or other similar solutions, and which can be implemented in a systematic and application-independent manner in database management systems.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
Ever since Codd introduced so-called “null values” to the relational model, there have been debates about exactly what they mean and their proper handling in relational databases. In this paper I examine the meaning of tuples and relations containing “null values”. For the type of “null value” representing that data are not applicable, I propose an interpretation and a solution that is more rigorously defined than the SQL NULL or other similar solutions, and which can be implemented in a systematic and application-independent manner in database management systems.
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Ted Hills
Published By: Experian     Published Date: Jun 01, 2018
Better data insight is the key to becoming a more informed, profitable company. Download this white paper to understand: -How to overcome challenges you may have with your data management strategy -What improvements you should make that will make the biggest impact -The importance of being proactive rather than reactive to data quality issues
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Experian
Published By: Reltio     Published Date: May 21, 2018
Effective May 25, 2018, General Data Protection Regulation (GDPR) will represent the most rigorous data protection regulation ever. Complying is not optional, and the penalties are very high. As companies scurry for total compliance, it makes sense to pause, assess, and use this opportunity not only for compliance but for managing customer data efficiently and gainfully. Reltio proposes ten simple steps to ensure your data management strategy is ready for GDPR, not just for assured compliance but going beyond and enabling better customer experiences and building business competence.
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Reltio
Published By: Reltio     Published Date: May 22, 2018
"Forrester's research uncovered a market in which Reltio [and other companies] lead the pack,” the Forrester Wave Master Data Management states. "Leaders demonstrated extensive and MDM capabilities for sophisticated master data scenarios, large complex ecosystems, and data governance to deliver enterprise-scale business value.” Reltio executes the vision for next-generation MDM by converging trusted data management with business insight solutions at scale and in the cloud. Machine learning and graph technology capabilities enable a contextual data model while also maintaining temporal and lineage changes of the master data.
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Reltio
Published By: Alation     Published Date: Mar 26, 2018
When analysts and data scientists have access to data governance policies and best practices directly within the flow of their analysis, the result is both more consistent compliance and more broadly adopted best practices. With the combination of Tableau and Alation, organizations can not only balance the demands of agility and governance, they can actually optimize for both at the same time. We call this Governance for Insight. Learn more about how Tableau users can have the best of worlds - agility and governance. Read Enabling Governance for Insight: Trust in Data with Tableau and Alation’s Data Catalogs. Register on the right to access a complimentary copy of this joint white paper from Tableau and Alation.
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Alation
Published By: Infogix     Published Date: May 04, 2018
Over the last few years, the term “data governance” has evolved to more prominence, concurrently with big data. While organizations understand the need for governance around big data, the implementation of a successful data governance solution continues to remain elusive as organizations grapple with what exactly data governance is. This whitepaper provides a concise definition of data governance and offers some key considerations for a successful data governance solution.
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Infogix
Published By: Ataccama     Published Date: Mar 16, 2018
Discover how a major healthcare provider and facility operator in the United States implemented Ataccama ONE data quality tools to scale their data quality across the enterprise.
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Ataccama
Published By: MariaDB     Published Date: Apr 02, 2018
Learn how to use JSON functions for semi-structured data This white paper explains step-by-step how to support semi-structured data with JSON functions, introduced in MariaDB Server 10.2, using a practical use case with sample data and queries for everything from creating, reading and querying JSON documents to enforcing data integrity with check constraints and functions. You will learn how to: • Create, read and update JSON documents • Index and query JSON documents • Enforce data integrity with JSON documents • Combine relational data and JSON documents • Return JSON documents as relational data • Return relational data as JSON documents
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MariaDB
Published By: Converseon     Published Date: Apr 02, 2018
Separating signals from noisy social listening data has long been a problem for data scientists. Poor precision due to slag, sarcasm and implicit meaning has often made it too challenging to effectively model. Today, however, new approaches that leverage active machine learning are rapidly over taking aging rules-based techniques and opening up use of this data in new and important ways. This paper provides some detail on the evolution of text analysis including current best practices and how AI can be used by data scientists to use this data for meaningful analysis.
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Converseon
Published By: Datawatch     Published Date: Apr 06, 2018
Enterprises are focusing on becoming ever more data-driven, meaning that it is simply unacceptable to allow data to go to waste. Yet, as the amount of data businesses collect and control continues to increase exponentially, many organizations are failing to derive enough business value from their data. Companies are feeling the pressure to extract maximum value from all of their data, both defensive and offensive. Defensive analytics are the “plumbing aspects” of data management that must be captured to mitigate risk and establish a basic understanding of business performance. Offensive analytics build on defensive analytics and support overarching business objectives, strategic initiatives and long-term goals using predictive models. In this whitepaper, you will learn how to address many challenges, including streamlining operational reporting, delivering insight and providing a single, unified platform for everyone.
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Datawatch
Published By: Zoomdata     Published Date: Apr 11, 2018
How do you imagine data? If you’re thinking about it in terms of uniform records and databases, it’s time to make a brain update. Back in the day, analytical engines were limited, so our perception of what could be considered data was, too. Today, the big data renaissance has begun, and actually, more of the data exists outside of databases than inside them, plus, EVERYTHING is data. We’re going to help you discover how business intelligence and data sources of today have changed, and as a result, so has our approach to analyzing data. An eBook on this very topic is waiting for you—all it takes is the click of a button.
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Zoomdata
Published By: BackOffice     Published Date: Apr 22, 2018
The success of a business is increasingly influenced by how effectively it utilizes data within strategic decision making & operations. But when a business views its critical data simply as a byproduct of business processes, and doesn’t value it as a business asset, it increases the risk of not being able to achieve its desired outcomes.
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BackOffice
Published By: Bitwise     Published Date: Apr 30, 2018
Organizations that adopt an enterprise data lake model for real-time, self-service and advanced analytics require a fresh approach and outlook to develop a Data Governance strategy as Hadoop changes the way that organizations ingest and store data, as well as how business partners access and use data. This paper outlines pillars for Hadoop Data Governance and Security that provide a framework that can be applied to any company.
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Bitwise
Published By: Deloitte Process Robotics     Published Date: May 04, 2018
Deloitte Process Robotics (DPR) solutions use a lightweight approach to train ‘bots’ that automate repetitive tasks of medium complexity without changes to existing process or IT infrastructure. With ever-growing repositories of unanalyzed and underutilized data, organizations require highly flexible and scalable enterprise data management (EDM) processes, DPR makes it possible for organizations to adopt comprehensive yet affordable EDM processes in the Big Data Era, helping organizations transform data into strategic assets through the automation of capabilities.
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Deloitte Process Robotics
Published By: Red Gate     Published Date: May 09, 2018
This whitepaper looks at what the changing data privacy and protection landscape means for organizations.
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Red Gate
Published By: IO-Tahoe     Published Date: May 14, 2018
The “silver lining” in GDPR compliance efforts is a creation of transparency and trust with customers, many of whom have layers of concern and trepidation over how all companies they do business with are handling their data. GDPR compliance efforts offer a chance to clear this air, between organizations and their customers, in a way that’s beneficial to both sides. In Io-Tahoe’s eBook, we explore the obligatory components of GDPR compliance, and the mindset most appropriate to achieving it. We also investigate opportunistic facets of establishing comprehensive protection policy around personal or sensitive data. We look at the prospect of GDPR compliance through the exploration of several “mandates”. Download the e-book to find out more on the eight mandates and the dividends beyond.
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IO-Tahoe
Published By: Erwin     Published Date: Jun 12, 2018
A strong data governance foundation underpins data security and privacy. Find out how to connect the dots across the data trinity – governance, security and privacy – and to act accordingly.
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Erwin
Published By: CA Technologies     Published Date: Oct 22, 2015
As the interest in managing information and enforcing corporate data management policies increases, data governance programs to manage data sets are becoming more and more vital to the business operation. However, in this rush for data governance programs, sometimes the true utility and importance of metadata can be missed. In this white paper, David Loshin of Knowledge Integrity, Inc. discusses the importance of data governance and the role of metadata management as a way to empower data governance and enforce data policies.
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white paper, metadata, data management, data modeling, david loshin, data governance, data governance strategy
    
CA Technologies
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