Six Sigma or Big Data? Why Not Both?
Last Updated October 14, 2020
What’s more useful to your organization – Six Sigma or big data?
The fact is that they both affect the bottom line, though in different ways. Six Sigma is a process improvement methodology that seeks to reduce errors that result in defects in a product or service. Big data represents the large amount of data that a business or organization produces in its day-to-day operations.
Organizations have always collected data. The difference today is that information technology platforms have made it easier to collect a greater volume of big data in near real time.
The question is, how do we use big data to improve our organizations? This is where Six Sigma can be useful.
“Six Sigma provides the methodology and statistical tools to make sense of the seemingly endless flow of data needed to improve processes. The improvements are then verified by collecting more big data,” said Marv Meissner, Lean Six Sigma professor of practice at Villanova.
Six Sigma and big data can complement each other well when used properly. Big data is not as effective without Six Sigma and Six Sigma is sub-optimized without big data.
Using Six Sigma in Conjunction with Big Data
What came first, the chicken or the egg?
That’s the question you’re tackling when you try to prioritize Six Sigma over big data – or vice versa. How do you create data? By taking action. How do you streamline and improve action? By studying data. When merging Six Sigma with big data, it can be approached from either direction.
Here’s an example, starting with big data: what’s the most valuable and actionable data your organization produces?
- For a company like Netflix, it might be the viewing habits of certain demographics
- For a restaurant, it might be service ratings from customers
- For a baseball team, it might be a pitcher’s earned run average
For your organization, it might be units sold, contracts signed, outbound calls made or something similar. Whatever it is, write it down. How do you feel about your number? Should it be higher? Lower? How does it align with your goals and expectations?
That’s where process improvement comes in. This number is the outcome of many processes, and those processes are made up of variables. For example, if the data advises you to increase your sales numbers, then you need to look at the processes that create sales for your company.
Determine if they can be simplified or reduced, and whether any waste can be cut from the process. It could be altering a sales script, prioritizing upsells or calling prospects at a different time of day.
The solution exists somewhere inside of the process – and you’ll likely pinpoint the issue by using the Sigma methodology of DMAIC (define, measure, analyze, improve, control) or its tools (such as the Five Whys and the Cause and Effect Diagram). Once you find it and change the variables, the big data will change too.
Why Big Data Needs Process Improvement Professionals
For companies with thousands of employees, analyzing large data sets can be complicated. The sheer amount of numbers, figures and statistics may be overwhelming to someone who isn’t trained to analyze them.
That’s why process improvement professionals are getting more involved in the analysis of big data. In 2012, Mark Beyer (the Vice President of Research at Gartner) said this in an article for the Performance Excellence Network titled, ‘Is Big Data the New Six Sigma’:
“Today’s information management disciplines and technologies are simply not up to the task of handling all these dynamics. Information managers must fundamentally rethink their approach to data by planning for all the dimensions of information management.“
Things have only gotten more complicated since then.
When striving to make business improvements, business leaders shouldn’t feel the need to choose between Six Sigma and big data. The two concepts work together cohesively, passing insights and wisdom back and forth like old friends.
The question is whether you know how to listen to what they’re saying.
