Data Driven or Insight Led?

Mpg Data Driven Or Insight Led 1920
08 February 2022 by Martin Ewings
Digital-Workspace Experis UK business-transformation

​Samuel Taylor Coleridge said in his famous 1798 poem, The Rime of the Ancient Mariner, “Water, water, everywhere, / Nor any drop to drink.”

Who would have thought that in the 21stcentury that sentiment could be applied to modern business? Today we have data everywhere yet in many cases it is no help to organisations struggling to understand why things are happening around them or what to do next.

Phrases such as ‘being data driven’ abound, but what does that really mean?

We know it starts with data, but that needs to be turned into relevant information by adding context. Even then that information is still historical, and viewed in isolation it’s “a forward-facing view through the rear-view mirror”. Real value comes from using that information to generate actionable insights.

But what are actionable insights?

You’ll have no doubt heard the phrase, “what gets measured gets done”. In the world of Big Data and Analytics, they are not wrong.

At the most simplistic level, actionable insights highlight something that was previously unknown or unproven, which is so compelling as to make a person or business revisit any preconceptions and act differently.

A colleague told me a story how, many years ago, he was asked to look at forecasting production needs based off historical shipping data. Sure enough, over the three previous years there had been a marked drop in products shipped in July. Clearly evidence to support the recommendation to reduce production on the run up to July, or so he thought. When he proudly announced this recommendation as actionable insight to the new leadership team, he was told to increase production! Apparently, sales demand in July was always off the charts and for some strange reason his organisation regularly took a production line out in June creating stock shortages, hence the shipping was artificially low.

By looking at what they shipped rather than demand, a false understanding of what was happening in the market was created.

So where do we start to harness ‘Big Data’?

Collection and validation of data is the first step – you need to know what data you have, where it is stored and who has access to it. This ‘Data Discovery’ solves the problem of data visibility and accessibility for organisations that have disconnected or legacy data systems, applications, and datasets, all used by different people for different purposes.

It enables the organisation to start the journey towards joining up their disconnected data systems, people, and applications into a fully governed internal data service. This service can then feed the entire organisation, providing a focal point where data may be found, consumed, joined up and analysed more quickly and efficiently, and where both internal and external users can trust that the data is complete, secure, reliable, and accurate.

At Experis, our Data Science team can scope and then execute a Data Discovery activity, producing a detailed Data Map of an organisation’s existing data systems and datasets, including how these data systems and datasets are related to each other.

What is the business value of Data Discovery?

There are measurable benefits over and above significantly improved visibility of an organisation’s disconnected data systems and datasets. These include:

  • Reduced time and costs associated with data processing through improved identification and simplified extraction of data

  • Digitisation of tacit knowledge to reduce risk and improve resilience, retaining knowledge

  • Optimise the use of in-house resources through identification of skills gaps

Where do the Insights come from?

Following a Data Discovery engagement, all data-related challenges and opportunities would to be catalogued and used to build out a roadmap aligned to the organisation’s data and technology strategies. This would include identifying gaps in the information landscape and sources of relevant internal data surfaced through the discovery process and external data which can be incorporated to refine the analysis.

That external data could be historical economic data, such as that published by the UK Government’s open data programme or related industry bodies. Take the production demand scenario shared earlier. What if they added external data sources for an historical £/$ exchange rate, fuel cost or even weather? They may find that there is a firm correlation which allows a forecast to be made with a higher degree of accuracy.

Those correlations which come from analysing additional related data sets are what can identify areas for further investigation. The insights gained from the analysis may force a new way of viewing the world that causes us to re-examine existing conventions and challenge the status quo. Those next steps may be a Proof of Value exercise to validate a change in direction or a series of Agile sprints to further develop more granular data capture. Both of which have the potential to significantly deliver against your business goals and objectives.

In our next post on data and insights, we’ll focus on how and why Artificial Intelligence and Machine Learning are the logical partners of Data Analytics.

Until then, we’ll leave you with a question.

Would you rather be data driven or insight led?