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.