The modern business landscape is characterized by an abundance of data, and those harnessing this data's power through machine learning stand to gain a competitive advantage. The ETL process (Extract, Transform, Load) is central to this effort and plays a crucial role in data management, analysis, business intelligence, and data processing. Raw data is ingested from multiple sources, like data lakes and databases, during the ETL process and reformatted for easier analysis.

After 30 years of working in tech across Asia, I’ve seen a lot of ups and downs in these markets. Not long ago, I wrote a blog about what US and European software companies can do to ensure their success when opening for business in the region.

By now, you would have read the headlines that Qlik's acquisition of Talend is complete, and we're excited to expand our best-in-class capabilities to help you access, transform, trust, analyze, and take action with your data. You might have seen Mike Capone's QlikWorld keynote or the recent "What's Next is Now" webinar and wondered how to leverage these new capabilities in your organization.

Poor-quality data costs the U.S. economy around $3 trillion a year and impacts the bottom line of 88% of American companies. That's why data reliability is so critical. It prevents inaccurate, inconsistent, or just plain bad data from moving through your pipelines.

It’s that time of the year again! I’m still buzzing about this year’s Showfloor Showdown at Gartner Data & Analytics Summit in London, where I had the opportunity to showcase ThoughtSpot's AI-Powered Analytics. In the spirit of facilitating a side-by-side comparison, we were all invited to look at global flooding and weather station data, analyze the variables affecting these natural disasters, and present key findings to the crowd.