Decision Generalists - The missing link between data and value

Most big organizations have gathered massive amounts of data and have all the tools and technologies to manage and process this data. They also have a variety of personnel specializing in various aspects of the data-to-value chain.

Yet, if you try finding success stories around data monetization, data driven efficiency, new business model discovery etc. (barring the internet/data companies), you will see yourself finding needle in a haystack.

In this age of extreme and rapid commotization of data related tools and technologies, why is it so difficult for big traditional organizations to harness the power of data?

Mysterious it may seem at the outset, if you look closely at these cases of failure you will find that there is something common and fundamentally wrong that these organizations are doing in the way they have built their data-to-value organizational capabilities.

They have broken down their data-to-value chain into too many sub-parts and have employed specialists for each part. A typical setup looks like

While this would have worked in a setting where the value requirements are simple and stable, in the current world of constant disruptions and ever changing value propositions, (where future problems and opportunities can never be fathomed in the present) this setting requires too many handshakes resulting in the overall value proposition lost in translation. Furthermore, each specialist brings deep and narrow perspective which often ignores the larger overlying objective.

What we need in this new age are people who know technology (system architecture, data wrangling, machine learning) and have the ability to shift gears quickly to not lose a sight of the larger objective and constantly place the overall value at the center stage.

These people would have experience in data wrangling (in the traditional settings), business analysis (to appreciate the ultimate value that the business is seeking) and have acquired reasonable and horizontal knowledge about the modern disciplines like machine learning, predictive modeling, data mining and artificial intelligence. They would also have management and leadership skills to navigate the team to the goalpost.

I call them "Decision Generalists" . They should be made a part of every interaction that happens in the data-to-value life cycle. They are like the guardrails that prevents the individual efforts from going haywire.

As far as the data-to-value capability structure is concerned, the same methodology needs to be applied. Here is what I propose

The million dollar question then is: Where and how to get such people? There are two ways. First groom your data scientists to acquire the missing skills and capabilities. Alternately, look at organizations whose business model has been revolving around up-skilling and cross-skilling people and have people with exposure to multiple environments, roles, technologies and domains. Yes you guessed it right! IT and ITES service providers.

We have been seeing pundits and experts write off the IT service providers but I believe that they will play a crucial role in providing the missing link between data and value and will become the de facto pool for Decision Generalists!

(The ideas/opinions expressed here are my own and is not endorsed by any organization/person

Images and concept courtesy : Russel Jurney's blog

https://blog.datasyndrome.com/generalists-dominate-data-science-f01882f25347)