Predictions about Prediction

2017 the Road Ahead [Borrowed from Eckerson Group]

“Prediction and explanation are exactly symmetrical. Explanations are, in effect, predictions about what has happened; predictions are explanations about what’s going to happen.”

– John Rogers Searle


The above image is from Eckerson Group‘s article Predictions for 2017. Eckerson Group’s Founder and Principal Consultant, Wayne Eckerson (@weckerson), is someone whose ideas I have followed on-line for several years; indeed I’m rather surprised I have not posted about his work here before today.

As was possibly said by a variety of people, “prediction is very difficult, especially about the future” [1]. I did turn my hand to crystal ball gazing back in 2009 [2], but the Eckerson Group’s attempt at futurology is obviously much more up-to-date. As per my review of Bruno Aziza’s thoughts on the AtScale blog, I’m not going to cut and paste the text that Wayne and his associates have penned wholesale, instead I’d recommend reading the original article.

Here though are a number of points that caught my eye, together with some commentary of my own (the latter appears in italics below). I’ll split these into the same groups that Wayne & Co. use and also stick to their indexing, hence the occasional gaps in numbering. Where I have elided text, I trust that I have not changed the intended meaning:
Data Management

Data Management

1. The enterprise data marketplace becomes a priority. As companies begin to recognize the undesirable side effects of self-service they are looking for ways to reap self-service benefits without suffering the downside. […] The enterprise data marketplace returns us to the single-source vision that was once touted as the real benefit of Enterprise Data Warehouses.
  I’ve always thought of self-service as something of a cop-out. It tends to avoid data teams doing anything as arduous (and in some cases out of their comfort zone) as understanding what makes a business tick and getting to grips with the key questions that an organisation needs to answer in order to be successful [3]. With this messy and human-centric stuff out of the way, the data team can retreat into the comfort of nice orderly technological matters or friendly statistical models.

However, what Eckerson Group describe here is “an Amazon-like data marketplace”, which it seems to me has more of a chance of being successful. However, such a marketplace will only function if it embodies the same focus on key business questions and how they are answered. The paradigm within which such questions are framed may be different, more community based and more federated for example, but the questions will still be of paramount importance.

New kinds of data governance organizations and practices emerge. Long-standing, command-and-control data governance practices fail to meet the challenges of big data and of data democratization. […]
  I think that this is overdue. To date Data Governance, where it is implemented at all, tends to be too police-like. I entirely agree that there are circumstances in which a Data Governance team or body needs to be able to put its foot down [4], but if all that Data Governance does is police-work, then it will ultimately fail. Instead good Data Governance needs to recognise that it is part of a much more fluid set of processes [5], whose aim is to add business value; to facilitate things being done as well as sometimes to stop the wrong path being taken.

Data Science

Data Science

1. Self-service and automated predictive analytics tools will cause some embarrassing mistakes. Business users now have the opportunity to use predictive models but they may not recognize the limits of the models themselves. […]
  I think this is a very valid point. As well as not understanding the limitations of some models [6], there is not widespread understanding of statistics in many areas of business. The concept of a central prediction surrounded by different outcomes with different probabilities is seldom seen in commercial circles [7]. In addition there seems to be a lack of appreciation of how big an impact the statistical methodology employed can have on what a model tells you [8].

Business Analytics

Business Analytics

1. Modern analytic platforms dominate BI. Business intelligence (BI) has evolved from purpose-built tools in the 1990s to BI suites in the 2000s to self-service visualization tools in the 2010s. Going forward, organizations will replace tools and suites with modern analytics platforms that support all modes of BI and all types of users […]
  Again, if it comes to fruition, such consolidation is overdue. Ideally the tools and technologies will blend into the background, good data-centric work is never about the technology and always about the content and the efforts involved in ensuring that it is relevant, accurate, consistent and timely [9]. Also information is often of most use when it is made available to people taking decisions at the precise point that they need it. This observation highlights the need for data to be integrated into systems and digital estates instead of simply being bound to an analytical hub.

So some food for thought from Wayne and his associates. The points they make (including those which I haven’t featured in this article) are serious and well-thought-out ones. It will be interesting to see how things have moved on by the beginning of 2018.


According to WikiQuotes, this has most famously been attributed to Danish theoretical physicist and father of Quantum Mechanics, Niels Bohr (in Teaching and Learning Elementary Social Studies (1970) by Arthur K. Ellis, p. 431). However it has also been ascribed to various humourists, the Danish poet Piet Hein: “det er svært at spå – især om fremtiden” and Danish cartoonist Storm P (Robert Storm Petersen). Perhaps it is best to say that a Dane made the comment and leave it at that.

Of course similar words have also been said to have been originated by Yogi Berra, but then that goes for most malapropisms you could care to mention. As Mr Berra himself says “I really didn’t say everything I said”.

See Trends in Business Intelligence. I have to say that several of these have come to pass, albeit sometimes in different ways to the ones I envisaged back then.
For a brief review of what is necessary see What should companies consider before investing in a Business Intelligence solution?
I wrote about the unpleasant side effects of a Change Programmes unfettered by appropriate Data Governance in Bumps in the Road, for example.
I describe such a set of processes in Data Management as part of the Data to Action Journey.
I explore some simmilar territory to that presented by Eckerson Group in Data Visualisation – A Scientific Treatment.
My favourite counterexample is provided by The Bank of England.

The Old Lady of Threadneedle Street is clearly not a witch
An inflation prediction from The Bank of England
Illustrating the fairly obvious fact that uncertainty increases in proportion to time from now.
This is an area I cover in An Inconvenient Truth.
I cover this assertion more fully in A bad workman blames his [Business Intelligence] tools.



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