We are halfway through 2019 and with the third release of the year, our momentum remains strong as we continue to deliver significant product innovations for our customers across the Qlik portfolio.

Many SAP customers have been running SAP on premise for decades and have struggled to harness the full potential of their business processes data running inside of SAP along with other enterprise and external data to gain augmented insight and become more agile in this digital era where everything keeps on moving at an exponential pace with no sign of slowing down.

I have had the privilege of playing with and following the progress of Pipeline Designer for a while now. I am really excited about this new tool. If you haven’t seen it yet, then don’t delay and get your free trial now…..actually, maybe read this blog first ;-) Pipeline Designer is an incredibly intuitive, web-based, batch and stream processing integration tool.

Before going to the world of integration, machine learning, etc., I would like to discuss with all of you about a scenario many of you might experience when you live in a mega city. I lived in the London suburbs for almost 2 years (and it's a city quite close to my heart too), so let me use London as this story's background. When I moved to London, one question which came to my mind was whether I should buy a car or not. The public transport system in London is quite dense and amazing (Oh!!!

As an innovative company, Yellowfin is focused on delivering new products to market, not just incrementally improving what we have. If you're a software company looking to build your own innovation strategy, there are some things that you need to do, and a few you shouldn’t, to make sure you're successful. The first thing you shouldn’t do is look at your competitors. Many software companies have one great idea and then they stop.

The main purpose of machine learning is to perform learning tasks on unseen data sets, having previously built up experience using training and testing data. Often those tasks can include looking for patterns and relationships between variables within the data.

We all know that data is important and that becoming a data-driven enterprise is critical to future enterprise success. But recent events threw into sharp relief just how critical data is to business. Google announced its intention to buy Looker for $2.6 billion dollars. Several days later, Salesforce announced that it would be purchasing Tableau for $15 billion dollars. What can we make of these acquisitions?