Data science has touched every aspect of business functions today, enabling greater speed, accuracy and improved quality of business decision making. As an inherent part of core business operations, the “people function” hasn’t been immune to this development either.
The best data science practices are known to combine management tools with statistical and machine learning insights. Such a combination has immensely helped in strategic decision making, hence directly improving the productivity – and profitability – of a large number of businesses worldwide.
What is Six Sigma?
Six Sigma is perhaps the most established and well-documented approach along these very lines. Fundamentally, six sigma is a data-driven methodology for improving the quality of a process (i.e. any repetitive business function) via reducing the variation around the mean of the process. In order words, ensuring that the process falls inside the acceptable tolerance range (as far as possible). This is referred to as the process entitlement level in six sigma.
Below are some of the most FAQ we’ve come across regarding this topic.
Q. What is Six Sigma to HR?
Six Sigma is a data-driven framework for improving the quality of HR processes, a process being any repetitive business function pertaining to HR. Six Sigma accomplishes this by reducing the variation around the mean of the process.
In simple words, Six Sigma ensures that critical HR functions fall within the acceptable quality/performance level. This is typically referred to as the “HR process entitlement level”
Q. How does Six Sigma actually reduce variation? What is the process (very briefly)?
Six Sigma unearths the cause of the variation via a single-pronged attack, “the root cause analysis (RCA)”. RCA is simply a cocktail of basic statistical analytics, problem-solving and brainstorming techniques. However, the specific tools employed within RCA can be very organization specific.
Q. Why do we need Six Sigma for HR?
Through the use of statistical hypothesis testing Six Sigma delivers evidence before substantiating claims – the first time and every time. This makes it extremely reliable and universally applicable. However, the data sampling rules need to be rigidly followed.
Six Sigma unearths obfuscation, repetition and unsubstantiated claims early on. It also helps to identify gap analysis, recommends optimization models for business processes, suggests optimal budgets and required training/ orientation.
Q. What challenges does it address and what opportunities does it present?
In terms of opportunities, there literally are a hundred, but let’s identify three key reasons spontaneously:
A. Six Sigma is a strategic process improvement framework which starts and ends with the quantification of HR processes data. It begins via measuring a sigma score (of the HR process under improvement ) and ends with an improved sigma score (i.e improved quality ) of the HR process. In layman’s terms: the sigma score (including the cp and cpk score) provides a unified point of reference and clear, “quantifiable evidence” of sustained HR process improvement.
B. Six Sigma is an “all-encapsulating” quality control and process improvement umbrella. It has around 4-5 extremely well-documented frameworks that can be seamlessly adopted and applied across the entire spectrum of HR process design and improvement, often producing results after less than 48 hours of analysis.
C. Six Sigma has a repository of over 100 well-documented tools. These include free flow and sequential brainstorming, problem identification tools, tools for the strategic alignment of HR, descriptive and inferential insights on HR process data, the controlled design of experiments in HR processes, etc. The adoption and use of these tools depend on the scope of the adaptability and the particular mechanics in question.
Q. Does this mean HR analytics and Six Sigma are different entities?
No, they are intertwined. Both are based on the fundamentals of data science applied to HR process data. The purported difference is simply that Six Sigma approaches analytics from the quality control and process improvement perspective.
In fact, in order to get the best results, Six Sigma projects can be seamlessly amalgamated with HR data science problems like managing attrition, absenteeism, succession planning, the analysis of cross-department data etc.
Q. Can Six Sigma help in taking HR analytics from the design stage to production and can it identify an optimal organizational specific approach in HR analytics?
One of our key Six Sigma DFSS (Design For Six Sigma) projects identified just 5 fundamental approaches to adopting data science to HR production (for any business domain whatsoever):
1. Strategic HR consulting focused around econometric and applied statistics models (typically developed via R or python data science libraries) ;
2. Integrated analytic modules built within HRMS systems like “success-factors workforce analytics”, “workday prism analytics”, “Oracle HRMS analytics” ;
3. Dedicated HR analytics platforms like Visier, CrunchHR ;
4. Generic, component-based analytics platforms like SAS miner, KNIME, Rapidminer server etc. ;
5. Analytics integrated with cloud/on premises-based data visualization platforms like Tableau, Click-Sense, Microsoft BI.
Past controlled design of experiments indicates that building customized, analytics-driven (via R integration) dashboards on powerful visualization platforms like Tableau, Click-Sense, and Microsoft BI delivers the maximum bang for the buck.
Q. On another note, you have done some consulting on HR automation. Why do you think that the blind adoption of automation to HR processes is bad?
The term automation has been over-obfuscated causing considerable confusion and misinformation. Automation is simply database driven triggers, functions, and procedures coupled with integration among disparate systems, for example through a REST API. The basis of those triggers could be through elementary math (hard-coded rules ) or statistical modeling/deep/machine learning (soft coded rules). Interestingly, the fundamental technology behind AI is almost 30 years old.
Implementing automation in HR can be a very straightforward process. For example, automating a part of the HR onboarding process via out of the box analytics platforms like KNIME/SAS/Rapid-miner through REST APIs could take as little as half a business day
Having said that, it’s important to realize that the cognitive capacity of computers is still at a relatively nascent stage when compared to the complexity exhibited by human cognition. Production automation for decision making based on a structured data set is plausible. However, automation is still some time away from deciphering contextual meaning from open text/ human language for direct business applications.
Q. What is HR doing sub-optimally?
Unless HR uses data analysis for strategic decision making, it simply isn’t capitalizing on its potential the way marketing, finance, manufacture, and general management has been doing for years.
How do decisions within HR affect finance, marketing, manufacturing, and overall business profitability? How is HR process data intertwined with the sheer market performance of the organization? A good HR analytics system should help to calculate return-on-investment for (nearly) every human resources activity and enable HR to take proactive accountability for a portion of the organization’s financial health.
HR is often not able to connect with the flow of revenue/profitability in a business. The term HR as a profit center has been often repeated while at the same time it has been touted that econometrics and finance are not our strongest points. Right, no perhaps Wrong!
Interestingly, our root cause analysis shows that a typical operational HR professional ends up working across 4 or 5 different types of business across their professional carrier. Unless an HR professional invests and dedicates time at the start of their professional stint in a new organization to get acquainted with the domain knowledge of their core business, we won’t make much headway.
As HR continues to digitize its operations and collect more data about its processes, it is possible for it to integrate strategically and evidence-based approaches like Six Sigma. The age of analytics in general and HR analytics in particular, is already upon us and we all can work together to improve our business processes and deliver a satisfying ROI.
About the authors:
Raja Sengupta is a Data Scientist, Statistician, and HR Analytics Expert. He has the Six Sigma Master Black Belt, is Head of Product Development at XcelPros and a Researcher on Computational Linguistics.
Soumyasanto Sen is a widely recognized People Technology Advisor specialized in (among other things) Management & Transformation and a true Future of Work Evangelist.
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