As organizations strive to become more data-driven, they increasingly recognize the importance of Data Governance (DG), a business program supporting harmonized data activities. However, business leaders, colleagues, and workers often express confusion about DG policies and need clarity around its value. This article tackles this issue by exploring the top 10 ways for effectively articulating the value […]
Women in Data: Meet Classification Guru Susan Walsh
To celebrate International Women’s Day, we’re kicking off a new Q&A series with women leaders in data. Our first installment features Susan Walsh, aka the Classification Guru. Susan Walsh didn’t always aspire to be a data classification expert. But after spending a decade honing her data skills – cleaning and classifying erroneous data – she […]
Metadata Governance: Crucial to Managing IoT
The Internet of Things (IoT), devices that produce and consume data through the internet, will likely comprise over 207 billion devices by the end of 2024. These widgets generate, consume, and send vast data over business networks. As a result, organizations must include IoT in their Data Governance programs to ensure better integration and legal compliance. Without effective governance, firms […]
The Impact of Data Silos (and How to Prevent Them)
Data silos often develop unintentionally within businesses, catching leaders by surprise. They hinder cross-departmental collaboration while giving rise to inconsistent data quality, communication gaps, reduced visibility, and increased expenses. The gravity of impact can be gauged from a report by Forrester research, which finds that knowledge workers spend an average of 12 hours a week “chasing data.” […]
What Is Data Completeness and Why Is It Important?
Data completeness is an important aspect of Data Quality. Data Quality is a reference to how accurate and reliable the data is overall. Data completeness specifically focuses on missing data or how complete the data is, rather than concerns of inaccurate or duplicated data. A lack of data completeness is normally the result of information […]
Common Master Data Management (MDM) Pitfalls
Leaders need to trust data within the organization to make sound business decisions. So, many turn to master data management (MDM), a solution to get and keep uniform and accurate data that increases business value. Yet, according to Gartner, 75% of all MDM programs across organizations fail to meet business objectives. Moreover, this trend has worsened since 2015, […]
AI Governance Best Practices
AI governance is meant to promote the responsible use of artificial intelligence for the betterment of humankind. Artificial intelligence has proven itself quite useful in completing a large variety of tasks quickly and efficiently. Unfortunately, it can also be used to support criminal behavior or to create and distribute misinformation. AI governance is an effort […]
What Is Data Mesh and Why Is It Important?
A data mesh challenges the traditional centralized Data Architecture by advocating a distributed and domain-oriented architecture. Data mesh promotes the idea of treating “data as a product,” where each domain or business unit becomes responsible for its own data products. By doing so, individual domains gain autonomy over their data needs and can make faster […]
Data Privacy vs. Data Security
Data privacy refers to a framework of laws, protocols, and controls designed to protect personal data from unauthorized access and use. It encompasses a range of information, including but not limited to names, addresses, financial details, social security numbers, and online activities. Data security refers to the controls, protocols, and industry standards designed to protect digital […]
Creating a Data Quality Framework
An organization can define its Data Quality goals and standards, and the steps needed to accomplish those goals, by creating a Data Quality framework. Creating it includes an assessment of the organization’s current Data Quality. A Data Quality framework can be described as an instruction manual for improving the quality of the data. With a […]