The 5 technological breakthroughs of a Next Generation Data Catalog
Guillaume Bodet - CEO - Zeenea
“Developing any technological product requires making a number of critical architectural and design choices early on. In this piece, I will expand on some of the choices we made at Zeenea which, we feel, set the ground for a next generation data catalog - automated, smart and simple.”
Data Quality usually refers to a company’s ability to ensure the longevity of its data. At Zeenea (a data catalog provider), we believe Data Quality is ensured through the 9 following dimensions - all essential to extract value to your company:
We will detail these dimensions with the help of a simple example in part one. We will then elaborate on how Data Quality management is an important challenge for organizations seeking to extract maximum value from their data.
We will also draw parallels between these different Data Quality dimensions and the different risk management phases to overcome - identification, analysis, evaluation, and processing. This will enable you to hone your risk management reflexes by tying in Data Quality improvement processing to a company objective (and evaluating the ROI on each quality dimension).
Once we have established the main features of an enterprise Data Quality management tool, we will detail how a Data Catalog - though not a Data Quality tool - can contribute towards Data Quality improvement (through the clarity, availability, and traceability dimensions mentioned above).
Developing any technological product requires making a number of critical architectural and design choices early on that will inevitably have an impact on its ability to meet market and user expectations. Software architecture is one of the main levers of execution for technology companies.
When we founded Zeenea, our goal was to build a world leading data catalog. It still is. To be fair, this is probably the goal for anyone looking to offer a new technology product.
We started this adventure with a common vision, a solid financial situation and an accumulated 50 years of experience in innovation and data - an ideal set of circumstances to start a great project.
In this piece, I will expand on some of the choices we made early on which, we feel, set the ground for a next generation data catalog - automated, smart, and simple.
Of course, these choices were not made arbitrarily. On the contrary, their purpose was to meet on one hand, what we see as the inherent objective of a data catalog (those that involve data exploration) and on the other hand, the challenges of enterprise-wide data catalog implementation.