White Papers, Research Papers, and eBooks

Active Data Governance Methodology

Data governance can deliver high-quality, trusted, and compliant data, which is why data leaders are pursuing governance initiatives. They need an effective means to make it a part of their process. Alation offers a new, fresh approach. Active data governance builds a community of business experts committed to data literacy as a means to enhance their business’ effectiveness. It is based on an iterative process of continuous improvement — in contrast to data governance of old, which typically… View Now


Logical Data Fabric to the Rescue: Integrating Data Warehouses, Data Lakes, and Data Hubs

Data warehouses were introduced to offer one integrated view of all the enterprise data spread across numerous isolated transactional systems. Now, organizations are struggling with a myriad of new data architectures that also try to offer some…  View Now


How Confluent Completes Apache Kafka: Modernize your data infrastructure with Confluent

Apache Kafka is the foundation of modern data architectures, but the open-source technology alone doesn’t offer everything enterprises need to reach production quickly and implement data in motion use cases end-to-end. To remedy this, Confluent offers a complete and secure enterprise-grade distribution of Kafka and makes it available everywhere your apps and data reside. View Now


Measuring the Cost-Effectiveness of Confluent Platform

Setting your data in motion with Apache Kafka® is a valuable but costly endeavor for most organizations. Even for small projects, the time and resources required to deploy and manage Kafka yourself can overwhelm your people and budget. Confluent Platform completes Kafka with a set of enterprise-grade features and services to solve this challenge, reducing Kafka’s infrastructure footprint, day-to-day operational burden, and intangible costs stemming from downtime and security risks. View Now


The Top 7 Techniques for De-identifying Data

De-identifying or anonymizing data is one of the best ways to ensure the safety of your customers, patients, partners, and employees. De-identification replaces raw data values with safer alternatives in places where unauthorized people could use sensitive data in your datasets to identify these data subjects. Why and how you’ll choose to de-identify data varies by use case. It’s important… View Now


Data Governance Essentials: How to Maximize Value and Minimize Risk

This guide, co-written by EY and BigID, explores how well-designed data governance programs contribute to business value while protecting sensitive and personal information. From mitigating risk across an organization to knowing your data – all of it – find out how to gain a competitive advantage and build brand trust with a solid data governance program. View Now


IT and Business Communication Run Together – Making a Data Governance Program Successful

Maybe you recall the failure that took place on September 23, 1999? A $125-million satellite – the Mars Climate Orbiter – burned up in Mars’ atmosphere. This paper takes up how such disasters, the regrettable outcome of poor data quality, can be avoided through data governance. Not only does data governance prevent these catastrophes but through the improved data quality it creates it also supports digital transformation: solutions… View Now


Bank Realizes Continuous Value from Data

This national bank had data spread across 1,500 systems. The bank didn’t know the data’s quality, how it originated or flowed, who had access or how they used it. The bank made a goal: create a transactional MDM platform for all client data. For five years, Prolifics and the bank have carefully modernized and integrated system after system – with the bank already realizing major benefits. View Now


Guide: Creating a Warehouse–First Data Analytics Stack

Analytics on the warehouse unlock insights and analysis that aren’t possible in third party tools. Your organization can make better decisions, faster when you use your data warehouse as a central source of truth to fuel the downstream tools. In this guide, you’ll learn how to put together the infrastructure required to enable richer, more comprehensive analytics on top of your warehouse. View Now


5 Reasons to Modernize Your Data Warehouse with the Data Cloud

Are you extracting maximum insights from your data? Like crude oil, data is much more valuable when processed and used to drive a business forward. Conventional data warehouses can’t handle the volume, complexity, and variety of data that companies generate today, nor can they simultaneously satisfy various departments’ need to access and analyze that data in real time. In this ebook, we outline five compelling reasons to modernize your data warehouse with a data platform… View Now


RBAC vs. ABAC: Future-Proofing Access Control

Data is always evolving. The access control methods we use should be doing the same. Data collection and analysis are integral to the success of modern organizations. What’s even more important is making sure that this data, especially when sensitive, doesn’t fall into the wrong hands… 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

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