Muting the death knell of the data scientist

Two interesting, similarly-themed pieces from Cliff Cate at GoodData and Andy Cotgreave on the Huffington Post (nice to see Andy use the data animation metaphor!) have grabbed my attention over the past couple of days. Both Cliff and Andy suggest that the role of the data scientist is (or should be) on the way out; because evolving tools, technologies and techniques are placing large-scale, intuitive, interactive data analysis in the hands of business users everywhere. Consider these arguments:

“The new generation of solutions, on the other hand, is making it easy for business users to engage big data.” – Cliff Cate

“Rather than being dependent on a highly qualified analyst to view data relevant to every single department, interpret it and share the findings, businesses are instead able to free the data.” – Andy Cotgreave

Noble sentiments, and worthy aspirations, but only a tiny proportion of businesses agree at present; three enormous blockages, backed by staggering inertia, stand in the way of democratic use of insightful, interactive, intuitive information as Cliff and Andy propose:

1. Access to data: The Cathedral and the Bazaar

Both writers argue that businesses have a wealth of data at their disposal, and are crying out for ways to utilise it. That’s certainly true of some parts of some organisations, but is far from universal.

There are numerous business-prevention departments (in corporate IT, goveranance, regulatory, information security etc.) who wield fearsome blocking power, and their cardinals work hard every day to prevent democratic access to data; even the business-critical data required by hundreds and thousands of employees for maximum effect and productivity. Big Data brings Big Headaches for these high priests, and the simplest answer is to decree broad data access as unacceptable; lock-down means control, simplicity and compliance.  I have some sympathy with the challenges that these clerics face – organisations need controls, checks and balances – but in far too many cases they are throttling the lifeblood of innovation, enterprise and creativity; the essence of a vibrant merchant bazaar.

2. Data quality: The Data Asset

In those businesses where the priesthood is weak, it should be possible for effective analytics to flourish – with the full power of the bazaar brought to bear on all nuggets of organisational information. All too often, however, the precious river of data is muddy and stagnant, murky and stinking, borderline toxic; analysing data like this is fruitless – few fisherman can make a living in a bog.

In order to collect, manage and disseminate fresh crystal-clear data, businesses need to treat it as an asset. Not say “Oh yes, our data is an asset”, but actually treat it as an asset. This deserves serious consideration: business data should be a highly-valued, slowly-appreciating (not rapidly-depreciating) intangible asset. Does anyone believe that a large organisation can function effectively today without an accurate set of data about its products, services, customers, suppliers, employees, offices etc? Really? Yet how many businesses can measure their data asset depreciation? OK, so it’s difficult to value it (actually not that difficult, see How to Measure Anything), so how about a ‘half-life’, the rate at which data quality decays such that it impacts on business process efficiency.

Remember, analysis of filthy, foetid data is a waste of time and, worse, is liable to point to the wrong answers, leading to poor decisions, thus giving analytics of any form a bad name and only adding cost with no benefit.

3. Unthinking employees: The Information Illiterati

Here’s the unpleasant and inconvenient final truth: too many businesses, and their employees, have never learned to think for themselves. When faced with a problem, or a question, or a challenge, or any form of change from the norm, they do everything in their power except stop and think; instead, they repeat learnt behaviours and wonder why they don’t get new results.

Harsh? Judgemental? Patronising? Perhaps, but I would welcome a body of evidence to the contrary. On far too many occasions I have sat with well-paid senior managers who simply cannot think, or be bothered to think, about their organisation. In some cases it’s laziness; they really can’t be bothered to think objectively about potential root causes for problems, or explanations for outlying behaviours, or imaginative areas for expansion, so they sit dull-eyed waiting for the consultant – or even the computer – to do the thinking for them. In other cases, it’s raw inability; otherwise hard-working, numerate, keen employees simply don’t have the experience or innate ability to think logically and creatively.

From master politicians, to stagnating process repeaters, to cookie-cutter MBAs; a lack of thinking employees stultifies a great many businesses.

Of course there are exceptions. In some cases there are large tranches of them, desperate to apply a little scientific method: think, hypothesise, test, analyse, document, review. Their efforts go unnoticed, secreted in localised groups, unless the organisational culture – from CEO down – prizes, cultivates and rewards thinkers; until then, the Cathedral flourishes and the priesthood jealously guards its precious artefacts: data, information and knowledge.

So, whilst I admire the sentiment, applaud the motive and otherwise admire Cliff and Andy announcing the death of the data scientist I fear that, to paraphrase Mark Twain, “The report of [their] death was an exaggeration.”

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4 responses to “Muting the death knell of the data scientist

  1. I wasn’t really convinced the way big corps like IBM were saying data scientists were business orientated in the first place. Rarely do you see commercial businesses employ scientists except for research, I think I find it more appropriate that any scientist should be to seek knowledge through science and not really for business continuity.

    • Fair point Dan – I think the term “Data Scientist” is used and abused… but however we label them, it’s naiive of analytics vendors to suggest that their is no need for expert analysts in order to make sense of data (Big or small); there simply aren’t enough analytical skills in business today to make sense (rather than nonsense!) of data in a reliable, repeatable and robust manner.

  2. I think I found Cliff Cate’s article more of a ‘advert’ for their company and trying to convince others they don’t need analysts and data scientists if they get their solutions tbh. To some extent companies may be able to do with just dashboards and reports on performance but with technological and data driven business, there’s always going to be a need to innovate and have people know how the system works to come up with new ideas and ways to improve the data management process.

  3. Pingback: Why You Should Animate Your Data to Liberate It | Data Animator·

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