White Papers, Research Papers, and eBooks

12 Guidelines for Success in Data Quality Projects

From years of data quality initiatives, hundreds of case studies, and research by industry experts, a number of common data quality success factors have emerged. View Now

Transforming Data Management Using Machine Learning

Machine learning is a powerful tool for the transformation of data management, and with increasing amounts of data in an organization’s possession, it is more vital than ever. View Now

Discover the Value of Data Quality for Data Governance Success

With data lakes offering the means to capture, store and utilize a broad array of internal, third party and external data from a variety of sources, organizations of all types are ready to gain greater and better insights. Download this white paper with Information Management, Discover the Value of Data Quality for Data Governance Success, to learn more about empowering your data governance program with quality data. View Now

Data Virtualization for Dummies: Learn How to put Data Virtualization to Work in your Organization

With the advent of big data and the proliferation of multiple information channels, organizations must store, discover, access, and share massive volumes of traditional and new data sources. View Now

Native Parallel Graphs: The Next Stage in the Graph Database Evolution

As the world’s first and only Native Parallel Graph (NPG) system, TigerGraph is a complete, distributed, graph analytics platform supporting web-scale data analytics in real time. View Now

Knowledge Graphs 101: How to Overcome a Major Enterprise Liability and Unleash Massive Potential

Enterprises must transition to contextualizing their data instead of just collecting it in order to fully leverage their data as a strategic asset. Existing data management solutions such as databases and data lakes encourage data sprawl and duplication. View Now

It’s Time: The Hidden Value of Unstructured Data

Effectively processing natural language and, in a broader sense, being able to accurately “make sense” of unstructured data at scale can generate exponential business value, accelerating knowledge discovery, and ensuring delivery of the right information at the right time, as well as empowering process automation by adding intelligence into Robotic Process Automation.  View Now

AI-Powered Data Operations for Modern Data Applications

The Unravel technical whitepaper entitled “AI-Powered Data Operations for Modern Data Applications”  addresses the requirements for driving reliable performance and reliability in today’s complex analytics data pipelines and data applications. The paper provides an overview of these requirements at the application level, the cluster level, and the big data ecosystem level. Unravel also describes the solution they have developed that delivers the data-driven automation and insights that address these requirements. View Now

Why Connectivity Has Become a Strategic Business Priority

In today’s business environment, companies must rely on consistent, accurate and reliable data to manage their business. Demand for data access and data integration initiatives is skyrocketing – but with it comes confusion. Where are businesses falling short in their ability to access and connect to their data? In this Q&A, data industry veteran Tony Fisher offers his insight on the best options for data access available today. View Now

Seven Ways to Change Data Culture

Arrayo conducted a series of interviews with prominent CTOs, CIOs, CDOs, CFOs, Data SMEs and recognized leaders in the world of data, with the goal of gathering “real life” stories and feedback from experts and decision makers across finance, pharma, biotech and healthcare.  View Now

2019 Global Data Management Research

For many, the promise of data-driven business is more myth than matter. Despite sincere ambitions, many businesses fail to take advantage of the many benefits that data can provide, as efforts around analytics and business intelligence are hamstrung by poor-quality data. View Now

What is… ?

What is Data Management? What is Data Governance? What is Metadata? Get the short answers to these and nine more hot data topics. View Now

Trends in Data Governance and Data Stewardship

The foundation of this report is a survey conducted by DATAVERSITY® that included a range of different question types and topics on the current state of Data Governance and Data Stewardship. View Now

Trends in Data Architecture

The foundation of this report is a survey conducted by DATAVERSITY® that included a range of different question types and topics on the current state of Data Architecture. The report evaluates the topic through a discussion and analysis of each presented survey question, as well as a deeper examination of the present and future trends. View Now

Emerging Trends in Metadata Management

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. View Now

Navigating the Data Governance Landscape: Analysis of How to Start a Data Governance Program

This report analyzes many challenges faced when beginning a new Data Governance program, and outlines many crucial elements in successfully executing such a program. View Now

Cognitive Computing: An Emerging Hub in IT Ecosystems

Will the “programmable era” of computers be replaced by Cognitive Computing systems which can learn from interactions and reason through dynamic experience just like humans? View Now

Why Your Business Users Need to Love Metadata

No business likes to throw money out the window, or in the case of the modern day enterprise, down the electronic data stream. View Now

The Question of Database Transaction Processing: An ACID, BASE, NoSQL Primer

There are actually many elements of such a vision that are working together. ACID and NoSQL are not the antagonists they were once thought to be; NoSQL works well under a BASE model, but also some of the innovative NoSQL systems fully conform to ACID requirements. View Now

Three-Valued Logic

Much has been written and debated about the use of SQL NULLs to represent unknown values, and the possible use of three-valued logic. View Now

An Approach to Representing Non-Applicable Data in Relational Databases

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. View Now

NO E-R: Modeling for NoSQL Databases

Entity-relationship (E-R) modeling is a tried and true notation for use in designing Structured Query Language (SQL) databases, but the new data structures that Not-Only SQL (NOSQL) DBMSs make possible can’t be represented in E-R notation. View Now

Cardinality, Optionality, and Unknown-ness

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. View Now

A Systematic Solution to Handling Unknown Data in Databases

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. View Now

The Hybrid Data Model

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. View Now

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