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.
April is an exciting time at Qlik as we build momentum towards Qonnections 2019 and later in the quarter, the global Qlik Analytics Tour. These great events will showcase Qlik Sense April 2019 – which is now available! This release sets us apart from the competition with significant advancements to our multi-cloud capabilities – including a new enterprise SaaS deployment option, as well as innovative advancements to our market leading AI capabilities.
Today’s connected experiences — such as controlling smart home accessories from a mobile app or ordering takeout via a voice assistant — involve a lot of software talking to other software. This means these digital experiences rely in large part on application programming interfaces (APIs). When someone uses their social media account to log into other websites, an API mediates the interaction.
There are numerous paid connectors for Data Studio but we offered ours for free, why?
65 SEO experts share their best tip for optimizing an entire website–ranging from content marketing to technical SEO–to help you reach the top spots in Google.
When the Application Program Interface (API) first came into existence, developers viewed it as a revolutionary approach to creating re-usable software fragments. Instead of creating new code from scratch for every new program, they could now use existing functionality to develop new features. Not only did this decrease the amount of time needed to deploy a program but also meant they could leverage existing code which was already tried and tested.
