Five years: top ten speculations for the future of BI

This is a guest blog post by James Richardson, Business Analytics Strategist at Qlik. In it, he speculates on what the analytics future may look like by 2021 (just don’t hold him to it!)

At this time of year there are a lot of ‘BI trends for next year’ pieces around – I know as I’ve been asked to write enough of them.  Most of them look to the year ahead and offer little more than a series of assertions. Worse than that, they’re boring. So when I was asked to consider the future (always something I’m wary of – given predictors’ tendency to get things spectacularly wrong) I thought why not go big, and look five years ahead and make some educated guesses based on evidence we’ve gathered and partner projects Qlik is running today?

So here are ten speculations.  By 2021:

 1. Analytics of new data sources will have undermined some long-standing business models. Take drivers’ insurance, the widening use of telematics could mean the demise of the actuarial-table based shared risk model, as cohorts of drivers get removed from the population and charged based on analysis of actual driving behaviours.  Health insurance can’t be far behind. Perhaps this becomes true of public health systems too, which refocus on proactive health promotion rather than reactive illness treatment. Further, classic white-collar work roles like that of the auditor look increasingly ripe for analytic automation. This is a logical continuation of the mechanisation of intellectual work – we’ve already forgotten that ‘computer’ and ‘calculator’ were people’s job titles not too long ago.

 

2.Decision makers will be making wide use of shared, immersive analytic experiences. BI development has been focussed on small form-factor devices, but the locus will now shift to very large (think wall size) touch devices. This will enable teams of colleagues to work towards decisions through the side by side exploration of data in real thought time.  In 2015 the #3 reason for not making a decision is disagreements with peers in 39% of cases; these kind of collaborative data experiences will mean that by 2021 we’ll be working in the data together.

 

3. BI will support a wider gestalt and a fuller range of human learning styles. The visual representation of data is dominant in 2015. However, not all people that need to use data are equally visually oriented. Humans use an individual mixture of sensory inputs to learn – often defined as three learning styles – auditory/reading, visual or kinaesthetic. By 2021, business intelligence will be making use of information delivery mediums to use all these learning styles – for example, for auditory learners, auto generated narratives in written or spoken form that describe the shape of the data selected or the contents of a chart.  Similarly, 3D printing may play a role in creating charts for the kinaesthetic learners to feel who work best when they can physically get their hands on something. Of course, for the visually-led learners the options will grow, taking advantage of massive, higher resolution displays to enable the rendering of massive data sets, and perhaps virtual reality experiences.

 

4.The data literacy gap will have narrowed. Inevitably people will become familiar and comfortable with more forms of data visualization over the next five years and will learn to read and use the insights in charts more readily. (An analogy here is how people have become familiar with the ‘language’ of film over time, to the point where interpreting a movie is second nature).  Perhaps more importantly, the education system will have reacted by adding more on analytics to business and other courses. Leading organizations will be mandating training on data literacy, as they recognize that data literacy among staff is a driver of competitive advantage.  Of course, the implication of more data literate people is more demands for and of data.

 

5.Personal analytics become base line behaviour. The behaviour we see in the quantified-self movement maybe uber-geeky now, but as more data comes on stream from services and devices, this movement will rapidly become the norm as individuals analyse “data of me” for self-improvement. Not just that but they’ll use analytics more and more as part of family life and in their communities (whether geographic or of shared-interest). The interesting implication for software vendors here is that this is another consumerization trend, driven by personal preference with implications for an eventual BYOAT (bring your own analytic tool) mode.

 

6.More people will (finally) be making use of predictive analysis. This has been a long time coming – today although most organizations have a few people doing more sophisticated statistical forecasting it’s not widespread; data from industry analysts has shown for years that less than 20% use predictive analytics broadly (i.e., as part of their BI projects).   Two drivers will be critical for this barrier to be overcome.  The first is using technology to ‘nudge’ non-statisticians by automatically showing them likely trends. For example, line charts that use a best-fit model to forecast three periods out, telling users in narrative form that a KPI will fall out of the acceptable range by a certain date, or using Monte Carlo simulations in analytic apps. The second driver is simply the broad availability of tools to support predictive modelling.  In the past the technology and knowhow was asymmetrically distributed – in the hands of few mavens. By 2021 the fact that this has not been the case for more than 20 years (through notably the open source R stat language) will have done for statistical probabilistic analytics what Gutenberg did for writing.

 

7.There will be much easier analysis of the ‘long past.’ The dramatic fall in the cost of data storage will mean that by 2021 organizations will have data in accessible, readable form (i.e., not on tape back-ups) going back further in time.  This will enable the algorithmic recognition and analysis of deep patterns, analysing the ‘long’ past.  This could prove useful as the analytic period stretches beyond that of economic cycles.  This will help organizations to not repeat history. Take the example of the last recession, organizations couldn’t learn from what happened as the data was effectively gone; this will not be the case by 2021.

 

8.Intelligent Decision Automation (IDA) will take in more business decisions as machines get smarter.  In 2016 IDA is only handling simple tactical (i.e., single customer/situation) decisions, but as AI is applied more widely to model and learn, IDA will touch a wider range of choices and not just those can be expressed as a decision tree.  Initiatives like Google making its machine learning software (TensorFlow) open source can only lead to the acceleration of the use of AI in decision making. However, this has limits…

 

9.More organizations will be doing decision reviews. According to Qlik-gathered data, only 23% of organizations routinely check the outcome of business decisions in 2015.  Given that the oft cited main reason for investing in BI is to ‘improve decision making’ this is a problem. By 2021, more organizations will be modelling more decisions. ‘Decision’ will therefore become a BI metadata type and therefore be analysable, so we can start to see if our organizations are making good decisions, what the inputs and outputs were, and perhaps, which teams and people are making optimal choices.

 

10.Hybridised heuristic/algorithmic management and decision making will be emerging in some organizations. The ideal management team would be one that draws together the positive aspects of human experiential learning, as expressed through heuristic decision making, with the power of algorithmic computing. It gives each a voice at the meeting table. A hybrid of the subjective and objective – think Captain Kirk and Doctor Spock – to make decisions informed by both viewpoints of the data and the intangible. By 2021 this hybrid may just be in the form of auto-generated data stories to enlighten people and extend the range of their perspective, beyond that, who knows? A computer generated avatar representing the data and offering input verbally many not be too far-fetched.

 

So, remember what I said at the start. These are speculations! In all likelihood at least half will be wrong – either too optimistic or not ambitious enough. Or perhaps something will come along that changes everything – an outside context event (aka a ‘black swan’).  So if I get any right, remember where you read them first. If not, no matter.

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