Master Data Management for the Data Governance Professional
Master Data Management (MDM) and Data Governance are both unique yet complementary disciplines. As defined by most industry experts, MDM is a technology-enabled discipline in which business and information technology work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and… View Now
Data Governance for Data Driven Organizations
Data is useful only when it is protected and its context, content, and quality can be trusted. Learn how IBM can help you. View Now
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Driving Change: Best Practices for Implementing and Sustaining Data Governance
As organizations increasingly rely on data to drive decision-making, effective data governance becomes increasingly important. Data governance is the framework for managing data quality, security, privacy, and compliance across an organization. It ensures that data is accurate, complete, trustworthy, and used appropriately… 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.
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
