Expect an increase in business-driven and elegant Data Modeling – the plans, and activities around diagramming requirements for data architecture. These Data Modeling trends will gain traction as budgets for newer projects decrease and mandates to improve Data Quality increase, as resolution to each data incident has risen by 15 hours between 2022 and 2023. Stakeholders clamor for returns from AI after investing heavily, […]
Enterprise Data Governance Online – Free Conference
DATE: January 22, 2025 LOCATION: Online Conference PRICE: Free Join us for the next annual Enterprise Data Governance Online (EDGO) in January 2025. This full day of live online educational sessions is presented by industry experts and the program is designed to teach anyone working with data to execute and implement a successful Data Governance program. Registration […]
Building a Successful Data Quality Program
Creating a successful Data Quality program is essential for any organization seeking to use their data for improving efficiency and better decision-making. Data of poor quality can result in decisions that damage the business. Building a successful Data Quality program helps to ensure the data is of the highest quality, making it both useful and […]
Gaining Leadership Support for Data Governance
Among today’s data-driven businesses, there’s no lack of support for Data Governance. But that enthusiasm doesn’t always make its way to the top of the management chain. “If you don’t have senior leadership sponsorship and understanding of your Data Governance program, your program is going to be at risk,” said Robert S. Seiner, president and […]
Data Observability Use Cases
“Data observability” can be described as the practice of monitoring the “health and state” of data pipelines in your system. This practice encompasses some technologies and activities that enable business operators to identify, examine, and solve data-related problems in near real time. Though organizations rely heavily on accurate and reliable data to make informed decisions, […]
Master Data Management Tools
Master data management uses a combination of tools and business processes to ensure the organization’s master data is complete, accurate, and consistent. Master data describes all the “relatively stable” data that is critical for operating the business. This includes semi-permanent information about products, locations, employees, customers, etc. For example, if a specific customer purchases several products […]
Managing Missing Data in Analytics
Today, corporate boards and executives understand the importance of data and analytics for improved business performance. However, most of the data in enterprises is of poor quality, hence the majority of the data and analytics fail. To improve the quality of data, more than 80% of the work in data analytics projects is on data […]
Transforming Data Management with AI-Driven Data Catalogs
In today’s data-driven world, where every byte of information holds untapped potential, effective Data Management has become a central component of successful businesses. The ability to collect and analyze data to gain valuable insights is the basis of informed decision-making, innovation, and competitive advantage. According to recent research by Accenture, only 25% of organizations are […]
Elements of a Modern Data Warehouse
Businesses are generating vast amounts of information every second. Traditional data warehouses, which were once considered the gold standard for handling and analyzing large datasets, are struggling to keep up with the rapid pace of data growth and evolving analytics requirements. This has given rise to the concept of modern data warehouse, which provides a […]
The Cool Kids Corner: Data Quality Is Not a Fish You Can Catch
Hello! I’m Mark Horseman, and welcome to The Cool Kids Corner. This is my monthly check-in to share with you the people and ideas I encounter as a data evangelist with DATAVERSITY. (Read last month’s column here.) This month we’re talking about Data Quality (DQ). Data Quality, the phrase “garbage in, garbage out,” dirty data, data […]