That time of year when BI vendors see how they are positioned against competitors and marketing departments weave their magic to patch over the cons identified.

The Gartner BI Magic Quadrant is unfairly criticised by many who see it as a payback to technology vendors who spend the most with them. In fact, if they bothered to look at the detail in the individual vendor reviews they would see even the leaders get some shocking "showstopper" cons revealed.

The truth is that buyers need to be very clear what they plan, why and who will do what with the new insights. That will help pick the appropriate vendor.

And very important:-

  1. Don't  just look at the MQ Visualisation and leave it at that- that is useless
  2. Read the individual vendor reviews which are revealing
  3. Then read the just published Gartner Critical Capabilities for Business Intelligence and Analytics Platforms

Then for good measure read on.

There are too many disparate BI & Analytics apps in most organisations.

Line of Business leaders, frustrated that central IT couldn't give them the analytics they needed, often licensed tools like Tableau and Qlik to provide quick and insightful visualisations. Gradually these spread often resulting in a degree of anarchy and "less than rigorous" data management.

 "Cloud structure storage for Big Data, hybrid hosting for data processing tools and applications, centralized control points for data-mining sources; these are all already available to analysts and BI professionals, but we will be seeing a lot more of them in 2017. This is very good news, because, without this unification, BI simply becomes unwieldy and cumbersome." Michael Brenner Visual Matters Feb 2017.

One of the questions BI practitioners need to ask is-

"Should we continue to encourage BI as standalone projects or should we prioritise embedding world-class analytics in our core apps and processes?"

The former can be faster and simpler in the short term but good decision-making is a cross organisational requirement not a departmental issue. And unless all the data in the organisation is accessible and analysed the value of insights gained will be limited. In fact, follow the link below to expose how many BI & Analytics projects disappoint. 

"Damning analysis of analytics".

There is typically too much data for users to analyse and make decisions so where applicable AI and machine learning will deliver decisions in repetitive and predictable environments.

Where future events are less predictable decision makers across the organisation need the insights and visualisations to help them make better decisions.

e.g. be able to test hunches across the disparate and multiple data sources to identify potential problems and opportunities. 

Yes- BI has to mature from the somewhat laissez-faire approach that characterises many approaches. Bringing together and bridging disciplines like:-

  • Central IT
  • Data Scientists
  • Analysts
  • Business users
  • Operational users

Data is at the centre of success and excellence and is one of the reasons the role of CDO is increasingly important. In fact, that's another key question reviewed in the linked article.

"Does effective analytics require a Chief Data Officer?"

The glamour of BI is at the presentation layer but the it's in the guts of BI- the data- where competitive advantage and true differentiation will be won.