White Papers, Research Papers, and eBooks

Which Comes First: Data Quality or Data Governance?

This guide explores the debate of data quality versus data governance. It unpacks the relationship between these two critical aspects of data management and explores which should ideally come first and why it’s often not that straightforward. Learn why many organizations find themselves addressing data quality issues before establishing a data… View Now

Mastering Your Data: The Ultimate Guide to Data Strategy

Wavicle’s Ultimate Guide to Data Strategy provides insights into the fundamentals of data strategy, the impact of a robust approach, and how to start crafting an effective data strategy for your organization. Your data strategy is the foundation that makes everything from data collection and basic reporting to advanced analytics and AI possible… View Now

Data Preparation: Don’t Try to Be Data-Driven Without It

Discover the path to transforming into a data-driven organization without the expense of data science-driven analytics platforms. Explore the significance of data preparation in achieving accurate, high-value analysis throughout your organization. Learn to break free from reliance on costly business intelligence vendors and… View Now

The State of Data Intelligence Analyst Report

Dive into the complexities of data intelligence with this detailed report. Uncover key pain points faced by businesses today and learn expert strategies to overcome them. From data governance to analytics, discover actionable insights to enhance decision-making and drive innovation. Stay ahead of the competition by… View Now

Data Modeling: Drive Business Value and Underpin Governance with an Enterprise Data Model

Unlock business potential and enhance governance with a comprehensive enterprise data model. Learn how to address data management challenges and drive value in this insightful white paper. View Now

Data Governance for Data-Driven Organizations

A strong data governance framework is the cornerstone of a successful data-driven enterprise. Not only does it provide the necessary guard rails to ensure data is used in a compliant manner, but it can also bolster innovation by providing data users the confidence and trust to accelerate data usage for insights generation… View Now

Enabling Bespoke Embedded Business Insights Through Semantic Layers

Donald Farmer, an expert in data analytics and the author of the O’Reilly book “Embedded Analytics,” guides readers through a comprehensive 14-page report focused on using a semantic layer in embedded analytics. Within this report, he explores… View Now

The Future of Data-Driven Decision Making: Integrating Machine Learning and Data Governance

“The Future of Data-Driven Decision Making: Integrating Machine Learning and Data Governance” delves into the role of integrating data governance with machine learning in modern business technology. It emphasizes the critical importance of data governance frameworks that ensure data accuracy, accessibility, and security, which are fundamental for effective data-driven decision-making. This structured approach to managing data quality allows machine learning algorithms to generate reliable and actionable insights, thereby enhancing strategic decision-making across various sectors such… 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