White Papers, Research Papers, and eBooks

6 Ways to Improve Your Data Quality

When your data doesn’t properly capture the reality of your business, it won’t serve as a reliable basis for any predictive model. Rather than driving smart decisions, data that’s not validated for quality and reliability can be worthless, or even damaging, to your company. Data quality and reliability checks help businesses detect data-related issues long before they… View Now


Building an Operational Data Warehouse

Have you ever tried driving real-time updates to customers from your data warehouse?  Or optimized a conversion funnel using real-time data? If so, you’ve probably suffered the pain of pushing your data warehouse past its limits. Analytical data warehouses are now ubiquitous in every company’s data workflow. But the data warehouse was designed to provide… View Now


Trends in Data Management: A 2023 DATAVERSITY Report

The Trends in Data Management 2023 Report provides a comprehensive overview of the evolving landscape in the field of Data Management, highlighting significant developments and emerging patterns that are shaping the industry around the world. The report delves into the continued importance of Data Governance and Data Quality while also emphasizing the increasing need for organizations to implement Data Literacy initiatives… View Now


Trends in Data Management: A 2022 DATAVERSITY Report

In today’s data-driven digital economy, organizations are increasingly looking for competitive advantages through reporting, analytics, and operational efficiencies. While this has been true for many years, there is an increasing maturity in the Data Management space as more organizations look to focus on Data Governance, Data Quality, and Data Security to ensure a solid data foundation for these efforts… View Now


Trends in Data Management: A 2021 DATAVERSITY Report

Digital transformation and the rise of the data-driven organization continue to drive Data Management across the globe. Increases in remote work and digital commerce, in part due to COVID-19 lockdowns, have only intensified these trends. Data stands at the center of digital transformation… View Now


Trends in Data Management: A 2020 DATAVERSITY Report

DATAVERSITY asked questions through the 2020 Trends in Data Management Survey. This paper details and analyzes the survey’s latest thoughts, trends, and activities indicated by study participants. View Now


What Happens When You Automate a Business Glossary?

Business glossaries are critical to an organization’s ability to speak the same data language across the entire company. Without trustworthy data, the enterprise may fail to realize … View Now


The 2020 State of Data Governance and Automation

The foundation of this report is a survey conducted by DATAVERSITY®. The 2020 State of Governance report explores where companies stand in automating the Data Governance processes that are so important to achieving Data Quality. View Now


Trends in Data Management: A 2019 DATAVERSITY Report

DATAVERSITY® asked what’s happening in Data Management through a 2019 Trends in Data Management survey. This paper details and analyzes the latest thoughts, trends, and activities indicated by those who participated in the study. 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


Business Intelligence versus Data Science

The competitive advantages realized from a dependable Business Intelligence and Analytics (BI/A) program are well documented. Everything from reduced business costs and increased customer retention to better decision making and the ability to forecast opportunities have been observed outcomes in response to such programs. View Now


Insights into Modeling NoSQL

The growth of NoSQL data storage solutions have revolutionized the way enterprises are dealing with their data. The older, relational platforms are still being utilized by most organizations, while the implementation of varying NoSQL platforms including Key-Value, Wide Column, Document, Graph, and Hybrid data stores are increasing at faster rates than ever seen before. Such implementations are causing enterprises to revise their Data Management procedures across the board from governance to analytics, metadata management to software development, data modeling to regulation and compliance. 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


Status of the Chief Data Officer: An Update on the CDO Role in Organizations Today

Ask any CEO if they want to better leverage their data assets to drive growth, revenues, and productivity, their answer will most likely be “yes, of course.” Ask many of them what that means or how they will do it and their answers will be as disparate as most enterprise’s data strategies. To successfully control, utilize, analyze, and store the vast amounts of data flowing through organization’s today, an enterprise-wide approach is necessary. 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.

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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


The Utilization of Information Architecture at the Enterprise Level

This report investigates the level of Information Architecture (IA) implementation and usage at the enterprise level. The primary support for the report is an analysis of a 2013 DATAVERSITY survey on Data and Information Architecture. View Now


Unstructured Data and the Enterprise

In its most basic definition, unstructured data simply means any form of data that does not easily fit into a relational model or a set of database tables. Unstructured data exists in a variety of formats: books, audio, video, or even a collection of documents. In fact, some of this data may very well contain a measure of structure, such as chapters within a novel or the markup on a HTML Web page, but not a full data model typical of relational databases. 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