White Papers, Research Papers, and eBooks

Solving the Data Incongruence Dilemma

The infrastructure for managing data across the financial industry is built on 50-year-old technology. Line of business and functional silos are everywhere. They are exacerbated by relational database management… View Now


The Forrester Wave™: Machine Learning Data Catalogs, Q4 2020

The report examines the 10 most significant data catalog entrants across 39 different criteria, backed by research and analysis. The report also delves into the importance of machine learning for the effectiveness of a data catalog. View Now


SAP Analytics on BigQuery with Qlik Replicate – How to extract SAP data into BigQuery

Review to learn more about: Performing analytics on SAP data directly in the transactional datastore is painful, and might impact SAP itself; BigQuery is part of an ecosystem of capabilities that enables Business Intelligence and Machine Learning; and how Qlik Replicate not only copies selected data from SAP into BigQuery, but keeps BigQuery synchronized as new transactions occur. View Now


Recipes for DataOps Success – The Complete Guide to an Enterprise DataOps Transformation

While most data leaders recognize they need DataOps to supercharge their company’s business agility, most don’t know where to begin. You are not alone!  Recipes for DataOps Success: The Complete Guide to an Enterprise DataOps Transformation illustrates how to lead DataOps change at your organization.  It answers important questions… View Now


Benchmark Report – Trillion Edge Knowledge Graph

Our latest benchmark report, Trillion Edge Knowledge Graph, is the first demonstration of a massive knowledge graph that consists of materialized data and Virtual Graphs spanning hybrid multicloud data sources. Key finding: Virtual Graphs deliver sub-second query times and 98% cost savings over traditional approaches. In this benchmark, we prove it is possible to have a 1 trillion-edge knowledge graph—for comparison purposes, this is twice as large as Google’s knowledge graph, which has 500 billion triples—and deliver sub-second query times while using a distributed infrastructure that provides a 98% cost savings over traditional approaches that store all the data in one location… View Now


5 Best Practices for Building Trusted Data Products

The shift has been made and data is now treated as a product, to discover new insights, automate business processes, and deliver extraordinary customer experiences. Data products are transforming the way every business operates however, the management of data products is becoming increasingly important. View Now


Graph-Powered Analytics And Machine Learning With TigerGraph

With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.  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


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 white paper will provide a practical step-by-step guide for implementing effective BI governance and a toolkit for addressing the three critical aspects of any program. View Now


Data Quality Assessment: A Methodology for Success

“The world’s most valuable resource is no long oil, but data,” stated The Economist. Many have noticed the increased value of data – from contact information to buying patterns – and how vital it is to so many aspects of business operations. Data can be used for marketing strategies, business intelligence, customer behavior patterns and so much more. But how can one be sure the data is reliable? Ultimately, bad data can lead to bad business decisions. View Now


The 2021 State of Cloud Data Governance

Organizations can manage and provide access to their data more efficiently when companies have reliable cloud data governance. However, while many organizations have started such programs, achieving the promise of cloud governance remains elusive. DataOps can help; it weaves cloud data governance activities together, supplying critical data effectively, while reinforcing necessary data governance policies and procedures to ensure regulatory and security requirements. 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


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

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