“The simple things you see are all complicated” sang Roger Daltrey on the Who’s classic “Substitute”. 

If you haven’t heard it for a while (or ever!), you can listen here . . 

But I digress . . . .

I read a fascinating article by Professor Sjoerd van den Heuvel that really resonated with me. 

Stimulated in part by a McKinsey recommendation, it addresses the question “Why Data Driven Businesses need Analytics Translators”.

Today’s organizations increasingly depend on the speed and flexibility with which they can respond to changing business demands.

Insights from data are key to this ability to respond.

We need smart data insight into markets, customers, suppliers, competitors, consumer experience and to optimize our own internal business processes.

For operational efficiency and effectiveness in business, we need better data insights to streamline processes and operations, manage risk, and drive most effective decisions and “next best actions”. 

This means we need data to facilitate better engagement with stakeholders and participants right across globally integrated processes, to drive better communication, understanding, collaboration, alignment, shared vision and coordinated execution across the organization. 

The promises of artificial intelligence and data analytics have propelled large organizations to invest in hiring data scientists. This trend is accelerating.

But it isn’t that simple.

Research indicates that in many global organizations, hiring data scientists does not result in substantial business impact.

Why?

The root cause lies in the vacuum that exists between data expertise and business expertise. 

The authors of the article reflect on a Harvard Business Review article, where McKinsey introduce the role of “Analytics Translator” to bridge this gap. 

It makes sense in the unique domains of each business such as customer insight, product usage, competitive analysis et al, that we want to build our own unique, differentiated analytics, and thus require “Analytics Translators” in those areas.

But, for the majority of core business process or risk scenarios, we should be able to source knowledge enabled business process analytics and insights, pre-configured with embedded business knowledge. 

This dramatically shortens the time-to-value on data insights for business processes such as Purchase to pay, Order to Cash and Record to Report.

Sjoerd’s article, written in collaboration with the Data Driven Business team at University of Applied Sciences, Utrecht, Netherlands (a 4 minute read) can be found here . . 

And the original source article by McKinsey in Harvard Business Review (HBR) can be read here . . . 

It turns out, as the Dunning-Kruger effect suggests, that the simple things you see really are a bit more complicated than we anticipate.

Food for thought?

Thanks for reading . . .