Data catalogs store information on all of the company’s data in one place. They collect data in a single repository and organize, analyze, and distribute the accompanying metadata. Without a data catalog, it will be harder for your organization to derive value from your business data and perform analyses necessary for the improvement and betterment of innovation, technological development, and business strategy.

Knowing how to easily identify, list, and access reference data has become a must-have skill for any company that seeks to be data-driven. So, what is reference data and what implications does it hold for your operation’s performance? Keep reading.

Data mesh is a sociotechnical approach to data architecture in which independent domain teams hold and maintain responsibility for managing their own data. With the transformation of raw data into highly relevant analytical models by local teams, data mesh eliminates large, centralized repositories of data and the complex pipelines connecting it to business intelligence users.

Three elements characterize big data: Volume, velocity, and variety. Of the three, volume is becoming a greater concern for companies – The amount of data collected is only growing! IT experts constantly have to adopt new terminology to describe the massiveness of data. It’s no longer surprising to hear about petabytes, exabytes, or even zettabytes!

If your organization doesn’t have a robust data governance plan in place, now is the time to reconsider. Not only does data governance improve your data quality, but it also has a significant impact on your company’s overall competitiveness and decision-making.

Managing corporate data is a complex challenge, but securing and controlling data access for each company’s employees is necessary. Both data governance and data management are incredibly vital to the company, but how do they depend on each other?

Simply put, master data management helps you manage the company’s master data. Find out everything you need to know about master data management, from its qualities to its limitations.

How do you ensure your data governance strategy has a real and lasting impact on your business? There is no magic formula, but these four key steps should help you create a foundation for data governance that fits your needs.

The Chief Data Officer (CDO) role is becoming an increasingly critical role. Previously, it was only assigned as an additional responsibility, but it has quickly become a full-time role in most organizations. So, what are the main challenges and responsibilities of Chief Data Officers?

Data quality is an essential element in your data governance strategy. This means taking the time to develop quality rules to use optimal data that teams will trust. Here are our top five tips for creating standard rules for reliable data.

Data governance is the process of managing the availability, usability, integrity, and security of the data in enterprise systems. Simply put, data governance covers all the rules and processes that ensure organizational data’s structure, protection, and management.

Have you ever wondered what a data catalog is or why it’s important for making smart business decisions? This blog post explains what information a data catalog holds and how it will help your team make faster, more informed decisions.

If, like me, you’ve heard this type of question more than often, chances are you might be considering building your data glossary! You might even asked yourself “I’m hearing everywhere people talk about the importance of data. If that’s so, why don’t I still have a referential to understand them all?”

Metadata has long been the poor relation of IT to data. Until then, there was little interest in exploiting these descriptions of information. Time has done its work. After an initial phase of euphoria generated by the business potential created by big data, enthusiasm is waning due to the difficulty of exploiting the data collected and existing data. According to recent Gartner studies, only 10% to 15% of the data owned by the company would be used; The rest consists of redundant, trivial, and other unknown data.

A lack of data governance, a major axis of data-driven business transformation, is the cause of many malfunctions and errors during data catalog transformation projects. The modern data governance approach is defined on two inseparable axes: Defining the data cultural maturity of its teams and implementing agile data governance techniques. Without collaboration and a common culture, any project is set to fail.