Leveraging Operational Knowledge with Analytics

Kaizen Institute have been supporting companies and leaders to implement successful transformation projects since 1985, creating significant sustainable results in performance and continuous improvement cultures.

In an ever-developing digitalisation era, the ability to turn data into a company’s asset is a key factor for competitive and sustainable growth. As a result, there is a whole new world of possibilities on how to leverage operational knowledge and KAIZEN™principles with analytics, transforming information into improvement opportunities. So, how is it possible to build this bond between data and business?

The words ‘big data’, ‘data mining’ and ‘business analytics’ have been buzzing in the business world for years.

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

It all starts with the way data is organised. 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 robust processes to keep pertinent data up to date, avoiding overlapping data and misleading information. The importance of each piece of information must be specified from the end-user’s perspective. The end-user in this scenario is typically the ‘business’ as a whole.

Databases are used for the day-to-day management of businesses. Sitting on top of a database is a platform of which some examples include ERP - Enterprise Resource Planning, WMS - Warehouse Management System and TMS - Transport Management System. These platforms translate the information from databases into something meaningful.  However, recent studies have highlighted that 70% of digital platforms introduced by companies fail.

Often this happens due to a gap between the system’s functionality and the actual needs of the business. Often, the requirements’ definition fails to grasp what is required to make the daily decision process data-driven - which highlights the need for deep business understanding in these development processes.

Data should be addressed to tackle specific problems and business opportunities - keeping the focus on the company’s goals.

This justifies the need for a clear and transparent communication channel between those who develop the system (software developers), the ones who analyse data (data scientists or business controllers), and the managers who ultimately rely on this output to make data-driven decisions.

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

The potential for 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 several 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 analysing data that can help the company.

In fact, from optimisation models to data mining algorithms, Kaizen Institute has been using analytics to leverage operational knowledge, contributing to the development of solid solutions that can be fully integrated within teams and an organisations business model. Taking a holistic look throughout a supply chain, the possibilities are endless: data structure, operations analytics, resource planning pricing & sales and 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?