Leveraging Operational Knowledge with Analytics

Updated: Apr 3, 2020





by Tiago Abreu


Kaizen Institute has been supporting companies and leaders to implement successful transformation projects for years, leading to significant ever-growing performance results and continuous improvement cultures.


In an increasingly mature new digitalization era, the ability to turn data into a company’s asset arises as a key factor for a competitive and sustainable growth. Therefore, there is a whole new world of possibilities on how to leverage the operational knowledge and Kaizen principles with analytics, transforming information in improvement opportunities. So, how is it possible to build this bond between data and business?

The words “big data”, “data mining” or “business analytics” have been buzzing in the business world for years.


However, companies only analyze a small portion of all the data generated, from which an even smaller set of information is really leveraged. But why does this happen?


It all starts with the way data is organized. The concepts of “data architecture” – how data is structured - and “data governance” - how data is managed - are the first steps towards a robust and consistent database.


”It is critical to have accurate information, stored in a way that a KPI result is the same, regardless of the way it is calculated.”

This requires strong processes to keep relevant data always up-to-date, avoiding overlapping data and misleading information. The importance of each piece of info must be defined from the end user point of view. This is the same as to say that it needs to be defined by the “business”.

Every database works as a foundation for the day-to-day management of a business. On top of it exist several platforms as ERP - Enterprise resource planning/WMS - Warehouse management system/TMS - Transport management system that turn all the information in the way it can be used. Nevertheless, several implementations of these interfaces fail.


Often this happens due to a gap between the requirements fulfilled by the system and the real needs of the business. Frequently, the requirements’ definition fails to grasp what is required to make the daily decision process data-driven, highlighting the need for deep business understanding in these development processes.


“Data should be addressed in order to tackle specific problems and business opportunities, keeping the focus on the company’s goals.”

This explains the need for a holistic communication and a relationship of proximity between those who develop the system (software developers), the ones who analyze data (data scientists or business controllers) and the managers who ultimately rely on this output to make data-driven decisions.

Only with a consistent and clear strategy for structuring, governing, analyzing, and deploying data is it possible to fully take advantage of the generated insights.


The potential of business analytics is endless. In fact, for a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income (Forrester,2017).


The transition from intuition to data-driven decision making can be a sinuous path. The inflow of more data from different sources may become overwhelming and a lack of structured analytics processes translates into the inability to use accurate data for decision making.


On the other hand, it requires discipline and a clear vision to focus teams on analyzing data that can help the company.



In fact, from optimization models to data mining algorithms, Kaizen Institute have been using analytics to leverage operational knowledge, contributing to the development of solid solutions and fully integrated in the teams and in the business model.

Taking a holistic approach to all the supply chain, the possibilities are endless: data structure, operations analytics, resource planning pricing & sales, sourcing or customer analytics.


It all starts with a clear top-down definition of the company’s goals and needs.


“ So, are you ready to trigger this? ”

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