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


The Ultimate Guide to Data Lineage in 2022

Data-driven organizations are adopting augmented data management to deal with complexity, keep the ability to innovate, and iterate quickly. CTOs and CIOs of successful companies are facing one major challenge: the skyrocketing complexity of their data stack (data pipelines). Combined with a shortage of engineering talent, it limits their ability to cope with the fast pace of changes, negatively impacts innovation initiatives, increases the risk of data… View Now


Make AI & BI Work at Scale

Learn how top data leaders are leveraging Machine Learning and Big Data Analytics. Each chapter contains practical advice on how to help you accelerate business outcomes with AI & BI across your organization. Get collective advice from 15 thought leaders and industry experts… 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


Smart AI Combos: Hardware and infrastructure for top performance

According to Gartner, barely 50% of AI projects reach production. How can companies overcome the challenges of implementing and scaling an AI workflow, and reduce the time, cost, and risk? Learn about the six major obstacles to AI implementation… View Now


How On-Premises Deployment Can Overcome Six Critical AI Challenges

The decision whether to run AI environments in the cloud or on-premises requires an understanding of the application environments, scaling in the future, and other topics. The lack of skills and support and the performance and manageability of the servers will all influence the decision. An often overlooked issue is the future requirements and whether a cloud provider will be able to accommodate scaling for increased workloads… View Now


What is a Smart Data Catalog? And why it isn’t only about machine learning

The idea of a Smart Data Catalog has been around for a few years in metadata management-related literature. Although it has no official definition, the general consensus is that a modern data catalog must have machine learning and AI to unlock its potential. In this piece, we will attempt to define how Zeenea handles the idea of the Smart Data Catalog which, for us, cannot be limited to machine learning and AI… View Now


The Path to Data Modernization: Unlocking More Value from Your Data

With the proper preparation and tools, your business has the power to find meaning in data as you modernize in the cloud. Enterprise-wide access to your data will give business leadership insights to improve the quality of their business decisions. It can further increase productivity by giving every person in your organization fast and easy access to the data they need to perform their work… View Now


Making Sense of Information Overload with Modern Data Lakes

DataOps helps to improve processes throughout the data lifecycle – from initial collection and creation to delivery to the end user, but implementing the methodology requires effort. Download this special report to learn… 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


Data Fabric Architecture Delivers Instant Benefits

Learn about the instant benefits of a modern data architecture framework, data fabric, in this whitepaper. Dive deep into what exactly is a data fabric, how does it differ from previous architectures, what can it achieve for businesses, and what is IBM’s role in implementing it. View Now


A Practical Guide to BI Governance

Organizations continually fail to generate ROI on their governance initiatives because they are too narrow in scope. To be effective, Business Intelligence (BI) governance must cover both data and visualizations. This whitepaper will provide a practical step-by-step guide for implementing effective BI governance and a toolkit for addressing… 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.

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


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

We use technologies such as cookies to understand how you use our site and to provide a better user experience. This includes personalizing content, using analytics and improving site operations. We may share your information about your use of our site with third parties in accordance with our Privacy Policy. You can change your cookie settings as described here at any time, but parts of our site may not function correctly without them. By continuing to use our site, you agree that we can save cookies on your device, unless you have disabled cookies.
I Accept