Jumping to conclusions has always been considered a bad way to do business (and most things in life). It implies making a rash, poorly considered decision without the facts and without an understanding of the wider implications associated with that decision. But there’s a new wave of technology that is changing everything.

Recently, I read an article by Jill Dyché which was a wrap-up of TDWI Las Vegas. She talked about speaking to an analytics professional who works for a bank and was building analytics on top of their applications. This comment really struck me because it means the bank’s software vendor is missing out on a great opportunity to create an enormous amount of value for their customer and their own business.

In October 2018, we launched two new products into the Yellowfin Suite: Signals, an automated discovery product that discovers critical changes in your data as they happen, and Stories, a data storytelling product which enables users to provide better context to the numbers and create a common, consistent understanding across the organization. What did we do next? Drink our own champagne, of course.

Today during Microsoft Build 2019, we announced that Talend Cloud, our Integration Platform as a Service (iPaaS), would soon be available on Microsoft Azure starting in Q3 2019. For those who have selected Azure as their cloud platform of choice, Talend Cloud running natively on Azure will provide enhanced connectivity options for modern data integration needs as well as better performance.

It’s a big day here at Qlik – we officially welcome Attunity into the fold after the formal close of our acquisition. Over the past year we’ve been executing on a vision of what customers truly need from their analytics partner – an enterprise-level data management strategy that fuels enterprise-wide analytics.

Today’s ever-increasing competitive market is forcing organizations to become more data-driven. To support key business objectives such as growth, profitability and customer satisfaction, businesses must digitally transform and become reliant on more and more data to make laser sharp decisions faster.

Among Talend’s blog posts are many outstanding ones on data governance, such as David Talaga’s “Life Might Be Like a Box of Chocolates, But Your Data Strategy Shouldn’t Be” which encourages us to know our data, and his two-part post on “6 Dos and Don’ts of Data Governance,” in which David offers steps to take and pitfalls to avoid when starting out on data governance.

It dawned on me recently that I don’t actually use the Yellowfin mobile app. Like other BI apps, our app essentially replicated the dashboard experience on my phone. But I don’t like viewing a dashboard on my phone, I’d much prefer to look at it on my desktop because the screen is larger. We realized that there’s no point having an app if no one uses it. So we started to think about how people use their phones and set about reinventing our app.

In the last three parts of this four-part series, we have looked at: research on the state of analytics today and the lack of BI adoption; the history of BI and how we have arrived at the augmented era; and the four main blockers to BI adoption that is stunting the growth your business data culture. Today, let's take a look at how AI and machine learning (ML) can close that adoption gap.

Gartner has recently released its 2019 Market Guide for Data Preparation ([1]), its fourth edition of a guide that was first published in the early days of the market, back in 2015 when Data Preparation was mostly intended to support self-service uses cases.