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 […]
Data Governance Trends in 2024
Companies are more determined than ever in 2024 to improve their Data Governance (DG) programs, the bedrock that supports harmonized data activities across organizations…
Master Data Management 101
The management of master data can be described as managing the data that is critical to your business’s operations. Master data management (MDM) deals with managing data that is relatively stable and critical to the business’s operations. The concept of master…
Data Strategy Trends in 2024
In 2024, organizations must embrace a good Data Strategy, a reliable touchstone created by an organization for businesspeople in their data-related endeavors and support its evolution. Executives face mounting pressure to swiftly adapt to a dynamic marketplace and demonstrate tangible impacts…