Nestlé Leadership's Commitment to Analytics & Data Based Decision Making

Humbled to have got invited by Nestlé to address their South Asia Leadership team on the topic of Analytics and Data Based Decision Making in Business.

Shared insights from my 18 years humble journey of working on data and how it helped my employers, my clients and myself in Marketing, Sales and the facilitative role of HR.

Data in the form of Information has been a companion for human beings ever since we have existed and analysing the data / information is a capability which comes naturally to all of us.

The human mind receives information in the form of "data packets" all through our existence. Everything we see, hear & feel is absorbed, filtered & stored as data and then analysed & processed.

While this happens in a more natural / humane way, in the last few years we have started building tools & methodologies to generate more scientific & meaningful insights from data which empowers us to take more strategic decisions.

It is extremely impressive to notice the commitment & focus on Data & its analysis by a brand as big as Nestlé, which has now embarked upon the journey of embracing Analytics in all spheres of business and decision making.

While there are newer tools & methodologies which can be deployed to get deep insights, these tools have somehow started overwhelming organisations and employees because of their technical nature and the sheer capability of processing large chunk of data / information.

One of the ways of overcoming this challenge, of moving to "data based decision making", is to embrace data as a friend and the capability-to-analyse as our natural innate capability.

Organisations / Functions / Department / Employees can start by defining the required outcome and then moving backwards. Plucking the low hanging fruits and moving one step / one analysis / one inference at a time can ease the transition and also equip employees with a more practical understanding.

An organisation of the size of Nestlé generates data in every transaction / interaction it does with any of its internal or external stakeholder. And hence has been creating large amount of data, some of which is not even known or visible. By knowing the desired outcome, functions can identify which data to refer to and where to source it from.

A tangible example could be the "Dispatch Register" of a local manufacturing unit, analysing which can throw a lot of insights, however knowing the desired outcome will allow us to focus only on those "data entities" which has a direct relation with the outcome e.g. which Product Code is the fastest moving (in terms of demand) over a period of 1 year.

Starting with something as simple as selecting a few "data entities" of a "Dispatch Register" e.g. Product Code, Distributors, Geography and Quantity.

While there are complex analysis which can be applied on data, by using various analytical tools, starting small with a more focused approach is the key to bring the desired change in the mindset, habit & build the capability of the teams.

Nishant.