# New Thinking, Old Thinking and a Fairytale

Of course it can be argued that you can use statistics (and Google Trends in particular) to prove anything [1], but I found the above figures striking. The above chart compares monthly searches for Business Process Reengineering (including its arguable rebranding as Business Transformation) and monthly searches for Data Science between 2004 and 2019. The scope is worldwide.

Brunel’s Heirs

Business Process Reengineering (BPR) used to be a big deal. Optimising business processes was intended to deliver reduced costs, increased efficiency and to transform also-rans into World-class organisations. Work in this area was often entwined with the economic trend of Globalisation. Supply chains were reinvented, moving from in-country networks to globe-spanning ones. Many business functions mirrored this change, moving certain types of work from locations where staff command higher salaries to ones in other countries where they don’t (or at least didn’t at the time [2]). Often BPR work explicitly included a dimension of moving process elements offshore, maybe sometimes to people who were better qualified to carry them out, but always to ones who were cheaper. Arguments about certain types of work being better carried out by co-located staff were – in general – sacrificed on the altar of reduced costs. In practice, many a BPR programme morphed into the narrower task of downsizing an organisation.

In 1995, Thomas Davenport, an EY consultant who was one of the early BPR luminaries, had this to say on the subject:

“When I wrote about ‘business process redesign’ in 1990, I explicitly said that using it for cost reduction alone was not a sensible goal. And consultants Michael Hammer and James Champy, the two names most closely associated with reengineering, have insisted all along that layoffs shouldn’t be the point. But the fact is, once out of the bottle, the reengineering genie quickly turned ugly.”

Fast Company – Reengineering – The Fad That Forgot People, Thomas Davenport, November 1995 [3a]

A decade later, Gartner had some rather sobering thoughts to offer on the same subject:

Gartner predicted that through 2008, about 60% of organizations that outsource customer-facing functions will see client defections and hidden costs that outweigh any potential cost savings. And reduced costs aren’t guaranteed […]. Gartner found that companies that employ outsourcing firms for customer service processes pay 30% more than top global companies pay to do the same functions in-house.

Computerworld – Gartner: Customer-service outsourcing often fails, Scarlet Pruitt, March 2005

It is important here to bear in mind that neither of the above critiques comes from people implacably opposed to BPR, but rather either a proponent or a neutral observer. Clearly, somewhere along the line, things started to go wrong in the world of BPR.

Dilbert’s Dystopia

Even when organisations abjured moving functions to other countries and continents, they generally embraced another 1990s / 2000s trend, open plan offices, with more people crammed into available space, allowing some facilities to be sold and freed-up space to be sub-let. Of course such changes have a tangible payback, no one would do them otherwise. What was not generally accounted for were the associated intangible costs. Some of these are referenced by The Atlantic in an article (which, in turn, cites a study published by The Royal Society entitled The impact of the ‘open’ workspace on human collaboration):

“If you’re under 40, you might have never experienced the joy of walls at work. In the late 1990s, open offices started to catch on among influential employers—especially those in the booming tech industry. The pitch from designers was twofold: Physically separating employees wasted space (and therefore money), and keeping workers apart was bad for collaboration. Other companies emulated the early adopters. In 2017, a survey estimated that 68 percent of American offices had low or no separation between workers.

Now that open offices are the norm, their limitations have become clear. Research indicates that removing partitions is actually much worse for collaborative work and productivity than closed offices ever were.”

The Atlantic – Workers Love AirPods Because Employers Stole Their Walls, Amanda Mull, April 2019

When you consider each of lost productivity, the collateral damage caused when staff vote with their feet and the substantial cost of replacing them, incremental savings on your rental bills can seem somewhat less alluring.

Reengineering Redux

Nevertheless, some organisations did indeed reap benefits as a result of some or all of the activities listed above; it is worth noting however that these tended to be the organisations that were better run to start with. Others, maybe historically poor performers, spent years turning their organisations inside out with the anticipated payback receding ever further out of sight. In common with failure in many areas, issues with BPR have often been ascribed to a neglect of the human aspects of change. Indeed, one noted BPR consultant, the above-referenced Michael Hammer, said the following when interviewed by The Wall Street Journal:

“I wasn’t smart enough about that. I was reflecting my engineering background and was insufficiently appreciative of the human dimension. I’ve learned that’s critical.”

The Wall Street Journal – Reengineering Gurus Take Steps to Remodel Their Stalling Vehicles, Joseph White, November 1996 [3b]

As with most business trends, Business Transformation (to adopt the more current term) can add substantial value – if done well. An obvious parallel in my world is to consider another business activity that reached peak popularity in the 2000s, Data Warehouse programmes [4]. These could also add substantial value – if done well; but sadly many of them weren’t. Figures suggest that both BPR and Data Warehouse programmes have a failure rate of 60 – 70% [5]. As ever, the key is how you do these activities, but this is a topic I have covered before [6] and not part of my central thesis in this article.

My opinion is that the fall-off you see in searches for BPR / Business Transformation reflects two things: a) many organisations have gone through this process (or tried to) already and b) the results of such activities have been somewhat mixed.

“O Brave New World”

Many pundits opine that we are now in an era of constant change and also refer to the tectonic shift that technologies like Artificial Intelligence will lead to. They argue further that new approaches and new thinking will be needed to meet these new challenges. Take for example, Bernard Marr, writing in Forbes:

Since we’re in the midst of the transformative impact of the Fourth Industrial Revolution, the time is now to start preparing for the future of work. Even just five years from now, more than one-third of the skills we believe are essential for today’s workforce will have changed according to the Future of Jobs Report from the World Economic Forum. Fast-paced technological innovations mean that most of us will soon share our workplaces with artificial intelligences and bots, so how can you stay ahead of the curve?

Forbes – The 10 Vital Skills You Will Need For The Future Of Work, Bernard Marr, April 2019

However, neither these opinions, nor the somewhat chequered history of things like BPR and open plan office seem to stop many organisations seeking to apply 1990s approaches in the (soon to be) 2020s. As a result, the successors to BPR are still all too common. Indeed, to make a possibly contrarian point, in some cases this may be exactly what organisations should be doing. Where I agree with Bernard Marr and his ilk is that this is not all that they should be doing. The whole point of this article is to recommend that they do other things as well. As comforting as nostalgia can be, sometimes the other things are much more important than reliving the 1990s.

Here we come back to the upward trend in searches for Data Science. It could be argued of course that this is yet another business fad (indeed some are speaking about Big Data in just those terms already [7]), but I believe that there is more substance to the area than this. To try to illustrate this, let me start by telling you a fairytale [8]; yes your read that right, a fairytale.

 $\mathfrak{Once}$ upon a time, there was a Kingdom, the once great Kingdom of Suzerain. Of late it had fallen from its former glory and, accordingly, the King’s Chief Minister, one who saw deeper and further than most, devised a scheme which she prophesied would arrest the realm’s decline. This would entail a grand alliance with Elven artisans from beyond the Altitudinous Mountains and a tribe of journeyman Dwarves [9] from the furthermost shore of the Benthic Sea. Metalworking that had kept many a Suzerain smithy busy would now be done many leagues from the borders of the Kingdom. The artefacts produced by the Elves and Dwarves were of the finest quality, but their craftsmen and women demanded fewer golden coins than the Suzerain smiths. $\mathfrak{In}$ a vision the Chief Minister saw the Kingdom’s treasury swelling. Once all was in place, the new alliances would see a fifth more gold being locked in Suzerain treasure chests before each winter solstice. Yet the King’s Chief Minister also foresaw that reaching an agreement with the Elves and Dwarves would cost much gold; there were also Suzerain smiths to be requited. Further she predicted that the Kingdom would be in turmoil for many Moons; all told three winters would come and go before the Elves and Dwarves would be working with due celerity. $\mathfrak{Before}$ the Moon had changed, a Wizard appeared at court, from where none knew. He bore a leather bag, overspilling gold coins, in his long, delicate fingers. When the King demanded to know whence this bounty came, the Wizard stated that for five days and five nights he had surveyed Suzerain with his all-seeing-eye. This led him to discover that gold coins were being dispatched to the Goblins of the Great Arboreal Forest, gold which was not their rightful weregild [10]. The bag held those coins that had been put aside for the Goblins over the next four seasons. Just this bag contained a tenth of the gold that was customarily deposited in the King’s treasure chests by winter time. The Wizard declared his determination to deploy his discerning divination daily [11], should the King confer on him the high office of Chief Wizard of Suzerain [12]. $\mathfrak{The}$ King was a wise King, but now he was gripped with uncertainty. The office of Chief Wizard commanded a stipend that was not inconsiderable. He doubted that he could both meet this and fulfil the Chief Minister’s vision. On one hand, the Wizard had shown in less than a Moon’s quarter that his thaumaturgy could yield gold from the aether. On the other, the Chief Minister’s scheme would reap dividends twofold the mage’s bounty every four seasons; but only after three winters had come and gone. The King saw that he must ponder deeply on these weighty matters and perhaps even dare to seek the counsel of his ancestors’ spirits. This would take time. $\mathfrak{As}$ it happens, the King never consulted the augurs and never decided as the Kingdom of Suzerain was totally obliterated by a marauding dragon the very next day, but the moral of the story is still crystal clear…

I will leave readers to infer the actual moral of the story, save to say that while few BPR practitioners self-describe as Wizards, Data Scientist have been known to do this rather too frequently.

It is hard to compare ad hoc Data Science projects, which can have a very major payback sometimes and a more middling one on other occasions, with a longer term transformation. On one side you have an immediate stream of one off and somewhat variable benefits, on the other deferred, but ongoing and steady, annual benefits. One thing that favours a Data Science approach is that this is seldom dependent on root and branch change to the organisation, just creative use of internal and external datasets that already exist. Another is that you can often start right away.

Perhaps the King in our story should have put his faith in both his Chief Minister and the Wizard (as well as maybe purchasing a dragon early warning system [13]); maybe a simple tax on the peasantry was all that was required to allow investment in both areas. However, if his supply of gold was truly limited, my commercial judgement is that new thinking is very often a much better bet than old. I’m on team Wizard.

Notes

[1]

There are many caveats around these figures. Just one obvious point is that people searching for a term on Google is not the same as what organisations are actually doing. However, I think it is hard to argue that that they are not at least indicative.

[2]

“Aye, there’s the rub”

[3a/b]

The Davenport and Hammer quotes were initially sourced from the Wikipedia page on BPR.

[4]

Feel free to substitute Data Lake for Data Warehouse if you want a more modern vibe, sadly it won’t change the failure statistics.

[5]

In Ideas for avoiding Big Data failures and for dealing with them if they happen I argued that a 60% failure rate for most human endeavours represents a fundamental Physical Constant, like the speed of light in a vacuum or the mass of an electron:

 “Data warehouses play a crucial role in the success of an information program. However more than 50% of data warehouse projects will have limited acceptance, or will be outright failures” – Gartner 2007 “60-70% of the time Enterprise Resource Planning projects fail to deliver benefits, or are cancelled” – CIO.com 2010 “61% of acquisition programs fail” – McKinsey 2009

[6]

For example in 20 Risks that Beset Data Programmes.

[7]

See Sic Transit Gloria Magnorum Datorum.

[8]

The scenario is an entirely real one, but details have been changed ever so slightly to protect the innocent.

[9]

Of course the plural of Dwarf is Dwarves (or Dwarrows), not Dwarfs, what is wrong with you?

[10]

Goblins are not renowned for their honesty it has to be said.

[11]

Wizards love alliteration.

[12]

CWO?

[13]

And a more competent Chief Risk Officer.

Another article from peterjamesthomas.com. The home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases.

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