Data analytics is integral part of Lean Six Sigma. It can be helpful in problem solving through data by establishing the significance of the relationship between problems (Y) and potential root causes (X). Because there is a large variety of tools available, the selection of the right tools is critical.
However, that selection of tools depends on the data analytics task at hand, the overall objective as well as source and types of data given. In contrast, discrete data, such as counts or attributes require different tools than continuous data, such as measurements. Whilst it is possible to transform continuous data into discrete data for decision making, this process is irreversible.
Depending on the data in X and Y, regression analysis or hypothesis testing will be used to answer the question whether there is a relationship between problem and alleged root cause. This leads to problem solving through data.
Most noteworthy, these tools do not take away the decision, but they tell the risk for a certain decision. The decision is still to be made by the process owner (example).
The road map for these tests follows a certain standard:
In conclusion, applications for problem solving through data analytics are evident in all private and public organisations without limits. Already some decades ago, companies like Motorola and General Electric discovered the power of data analytics and made this the core of their Six Sigma movement. They made sure, that problem solving was data-driven and applied data analytics wherever appropriate. Nowadays, data analytics is widely used for problem solving as part of most Lean Six Sigma projects. Black Belts are usually well-versed in this kind of data analysis.
I only believe in statistics that I doctored myself. Winston Churchill