Systems | Development | Analytics | API | Testing

Imagine having self-service access to all business data, anywhere it may be, and being able to explore it all at once. Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested. Imagine independently discovering rich new business insights from both structured and unstructured data working together, without having to beg for data sets to be made available.

How can you ensure data quality and security across your data analytics pipeline? With data governance – the exercising of authority and control over your data assets. It includes tracking, maintaining and protecting data at every stage of the lifecycle.

It’s that time of year - back to school, back to books, and our annual must-read books for data and analytics leaders. Given the pace of change in our industry, continuous learning is a must, whether through networking, podcasting, or reading. To cull this year’s list, I focused mainly on books published in the last two years with the themes of data, analytics and AI. I scoured lists and reviews on Amazon, solicited ideas from social networks and got to reading.

The management of data assets in multiple clouds is introducing new data governance requirements, and it is both useful and instructive to have a view from the TM Forum to help navigate the changes.

For the third year in a row, we were recognized for our "ability to execute" and "completeness of vision.".

In part 1 of this blog series, we looked at how Snowflake supports the GEOGRAPHY geospatial data type, which works with the earth as an ellipsoid, measuring distances over a curvature and plotting objects using the latest World Geodetic System, WGS84.