Systems | Development | Analytics | API | Testing

The arrival of more and more data in all segments of the enterprise started out as an embarrassment of riches, but quickly transformed into something close to a nightmare of dark data. However, a raft of new technologies and the processes embodied in DataOps are charting a path forward in which a much higher percentage of data becomes useful. The challenge most companies face is how to manage and get access to all the data flooding in from all directions.

In this episode of Data+AI Battlescars, Sandeep Uttamchandani, Unravel Data’s CDO, speaks with Samir Boualla, CDO at ING Bank France, one of the largest banks in the world. They cover his battlescars in Driving Data Governance Across Business Teams and Building Data Products. At ING Bank France, Samir is the Chief Data Officer. He’s responsible for several teams that govern, develop, and manage data infrastructure and data assets to deliver value to the business.

We all know data is the new oil. Both data and oil are valuable resources and share a common quality; if unprocessed they cannot be used. Data and oil have to be broken down and built up again to create true value for the business. There is, however, one key difference. Whereas oil is tangible, data is not. This means that the flow of low-quality oil is traceable and will be noticed in the production process. But, what happens if there is a bad data flow in your organization?

Should your data team focus on business problems and use cases, or expand into more complex areas like predictive analytics?

Not sure whether SQL or GUI transformations are better for your business? Here are the pros and cons of each approach.

We are roughly a decade removed from the beginnings of the modern machine learning (ML) platform, inspired largely by the growing ecosystem of open-source Python-based technologies for data scientists. It’s a good time for us to reflect back upon the progress that has been made, highlight the major problems enterprises have with existing ML platforms, and discuss what the next generation of platforms will be like.