Systems | Development | Analytics | API | Testing

Handling secrets has always been a challenging and critical task within organizations.

Data enablement doesn’t stop by delivering data in a data lake or data warehouse. Data must reach easily its point of consumption. Whether it is accessed as a self-service by a business user or integrated into an application, making trusted data available to all when it’s needed is of utmost importance.

To maximize the benefits of cloud, data & machine learning and make it available to innovative digital teams and customers, the data lake is the definite reference model. How can we accelerate its implementation and scope without losing control or jeopardizing its sustainability?

CCPA is the latest regulation which should be a concern for all companies with California residents in their databases. There are less than 90 days left to be ready to comply with CCPA, on top of the other regulations.

Not so long ago, our CFO was looking at changing our financial system and it reminded me of a similar situation that happened when I worked at National Australia Bank many years ago. We changed our entire ERP because the organization needed to slice and dice their data differently. What they could have done instead was use analytics differently to extend the life of the products they had, so that’s what we’ve done.

In today’s businesses, data should underpin every decision. It can be your most valuable enterprise asset if it is processed, stored, managed, and shared in ways that your employees can find, use, and interpret it. To do this, you need a data integration strategy.

This is part three of an occasional series on data governance (DG) and its enabling technologies. In parts one and two I gave an overview of what data governance is and how it might be undertaken; in this blog, I talk about business glossaries--one of the key DG-enabling technologies--and how to create one.