What is Causality and Why is it so important

If you collect data, you need to conduct Causal Analysis

Causal analysis is the study of causality with non linear variables. This is considered the “Holy Grail” of data analytics.  Causality goes beyond linear correlation and looks at cause and effect, what causes what. Not simply when two parameters rise and fall at the same time. Like ice cream purchases and sun cream usage. 

The vast majority of organisations have simple, diagnostic and descriptive standard data analytics that are still largely using traditional statistical methods that create pie charts, bar graphs and tell you statistics like mean, mode and correlation. A business leader needs to ask the following.

What insights is this style of analytics actually giving you? Is it reactive or predictive?  Does it find the actual causes of issues? More importantly, what is the real value you are getting out of your data and is it giving you meaningful answers to existing or future issues?

BDC’s proprietary CAUSAL ANALYSIS ENGINE (CAE) is breakthrough technology and AI which allows us to analyse vast amounts of data to uncover the causal drivers in your business. We think of it as a superpower to uncover the unknown unknowns in your data and unlock hidden commercial value, efficiencies and benefits you didn’t know existed. In essence, you are getting REAL insights that informs you that “this causes that”.

BDC’s technology has been verified by top scientists and mathematicians to be world leading and a breakthrough with applications across virtually every industry.

Management decisions that rely on “associational” or “correlation” based data analytic methods run the risk their decisions will not be properly informed and could be wrong. These decisions are made from linear methods rather than non linear, empirical causal methods which uncover the root cause of what effects what. 

A common theme from management is “we are doing data analytics but we don’t know what to do with it or what value we get from it”.

If this is you, consider exploring Causality and discovering what causes what in your data. 

If your organisation could know the actual cause(s) of a particular effect with reasonable accuracy (not just that something appears to be associated or correlated with the effect), business leaders can then take steps to alter the effect and improve the outcomes and processes within.

Some examples of value driven outcomes are:

  • Reduced outages of sensor-driven equipment or devices.
  • Regarding health, more precise determination of individual patients’ treatment effects.
  • Analysing the infrastructure on an oil and gas rig may mean better finetuning or preventative maintenance which means increased SAFETY, SAVINGS and EFFICIENCIES.
  • Performing causal analysis across a fleet of mining equipment may also mean a more efficient maintenance schedule, extending or reducing some component hours accordingly and therefore increasing revenue and safety.

By not doing this, management does not know the actual cause or driver of a particular outcome or effect – they basically infer a cause from a correlational or associative evidence basis only and there are likely to be faulty conclusions.


Where 'Big Data Causality' Operates

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