Making Sense of Big Data and Six Sigma
Working on Big Data with Your Process Improvement Skills
So, you have the relevant Six Sigma knowledge and experience, and there is an opportunity within your company internally for data analyst to deal with big data. Will you be a good fit for this opportunity? The answer is a big YES, albeit with some learning curve.
Big Data? Six Sigma?
Let’s lay down some underpinning knowledge for both. Six Sigma deals with process variation reduction in your work processes. To deal with this, respective process owners are selected and trained to identify defects and then eliminate them. Having a better quality output to the customer resulting from the reduced defects will create a ripple of profit to the company. The trained Belters (as we called them in Six Sigma companies) are equipped with statistical tools coupled with project management and change management skill sets.
Big Data has become fundamental to businesses of the future, especially so if the company is an internet or technology company. Data analyst or statistician are given tasks to mine and analyse data acquired from external or internal resources. They will churn out results which give valuable insights into processes. Those insights can help company to understand market trends, consumer usage behaviours and help shape their future strategies. For that, analysts with a strong data mining and analytical skills are greatly needed.
A shared key success factors to both programs is Data. In many Six Sigma initiatives, data may or may not be readily available for the Belters to analyse. Hence many times they will have to do proactive data collection on a defined operational area of interest. As such, sampling of data is needed and quantity of data will be limited. Unlike Six Sigma, Big Data is flooded with huge amount of data that is readily available for analysis – but clouded with noise and errors. Website hits, enterprise data and Internet of Things (IoT) like trackers, smartphones and many network connected devices churned out large data on continuous basis. Statistical analysis tool like Minitab and JMP played a central role in Six Sigma deployment but for Big Data, analytical software from Google, SAS and IBM are the robust and strong choice.
Getting the best from one another
The Body of Knowledge of Six Sigma provides Six Sigma Belters with adequate tools to solve process problems in a project setting. In Big Data analytics world, data are heavily crunches to find correlations – a skill set already acquired during the Measure and Improve phase of Six Sigma DMAIC project life cycle. Missing from the equation is the know-how to mine data and perform predictive analysis, both may come with a bit of learning curve.
Big Data is, and will be vital to how companies operate. Two of the largest corporations that at the forefront of this are GE and IBM, both whom has already implemented a strong and successful Six Sigma culture. With this example, companies must recognize that it is too important for the data to be left to data analysts alone, which currently are in short supply. Six Sigma Belters can be invaluable gems that can help with your company’s Big Data strategy, and vice versa, data analysts can be a good resource for Six Sigma implementation too with their skill sets.
Who says you have to be either one or another?