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One industry sector that is booming globally is Business Analytics. One way to understand this domain is to divide in three parts - Descriptive Analytics (What has happened), Predictive Analytics (Understanding the future), and Prescriptive Analytics (Decision making to influence business outcomes). 

 

There are Analytics experts who do not link well with business excellence and there are business excellence professionals who do not know much about the vast and expanding domain of Analytics.  Benchmark Six Sigma is about to launch Business Analytics courses in January 2018 to address this.

 

Which among the three Business Analytics areas (Descriptive, Predictive and Prescriptive) are captured by the Lean Six Sigma community reasonably well and which areas still seem largely unexplored? 

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New generation tools and techniques allows us to measure practically everything and it is cheap to do so. This means that measure and analyze phase of DMAIC will no longer be impacted due to lack of sufficient data. Predictive modelling can be used in analyze and improve phase to make it more effective since the data availability is no longer a constraint. It will now become increasingly important to select relevant data from mountains of information available. It is not Lean Six Sigma vs BigData but BigData is an answer to a critical problem that plagued Six Sigma since its advent about data availability and analytics.

Descriptive Analytics – Insight into Past

Descriptive analytics is a preliminary stage of data processing that creates summary of historical data to yield useful information and possibly prepare for further analysis. It uses data aggregation and data mining to provide insights on what has happened.

Descriptive Statistics is a method of organizing, summarizing, and presenting data in a convenient and informative way with aim of understanding what has happened or current situation and aids in descriptive analytics. The actual method used depends on what information we would like to extract. The tools and techniques covered in Six Sigma that are applied in Descriptive analytics are measures of central tendency like mean, median, mode and quartiles and measures of dispersion / variation like standard deviation, variance and range. This is reasonably well captured in Lean Six Sigma.

Predictive Analytics – Understanding the future

Predictive analytics uses statistical models and forecast techniques to understand the future and answer what could happen. It helps predict what could happen based on data and these predictions are not 100% certain and is this uncertainty is denoted in form of probability. It uses historical data available within organizations to identify patterns and apply statistical models to forecast customer behavior, purchase patterns, inventory and sales. Another common application is to compute credit score. It helps fill in information that is not available based on information available. The tools and techniques covered in Six Sigma that can be applied in Predictive Analytics are Hypothesis testing, correlation and regression.  This is also reasonably well captured in Lean Six Sigma.

Prescriptive Analytics – Advise on possible outcomes and how to influence it

Prescriptive Analytics is a relatively new field that allows users to “prescribe” a no. of possible actions and guide them towards a solution. It helps to quantify effect of future decisions in order to advice on possible outcomes before actual decision is made. So it provides not just insight on what will happen but why it will happen in terms of actions that are required to ensure prediction is realized and how best to maximize benefits. It uses a combination of techniques and tools like business rules, algorithm, machine learning and computational modelling procedures which are applied against input from various sources like transactional, historical data, real time data feeds and big data. These are relatively complex and most companies aren’t applying it yet. The tools and techniques covered in Six Sigma that can be applied in Prescriptive Analytics are Design of Experiments and Simulation. This is captured in Lean Six Sigma however can be handled better with Prescriptive Analytics using BigData.

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Descriptive and Predictive areas of Business Analytics are captured by the Lean Six Sigma community reasonably well and Prescriptive is till seem largely unexplored. 

 

As we work on data gathering for results which is focusing on descriptive area of business analytics and hypothesis testing covers predictive area of business analytics.

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The history of Management is as old as the history of mankind. Ever since the human species evolved, there have been millions of Management concepts that have come up, been practised and either died a premature death or merged with another concept or evolved into another version. Some of these concepts have been shown up in due course of time as just a fad, with few of them being aggressively promoted by influential but vested, commercial interests. Very few of these management concepts have really stood the test of time and with every decade, the yardstick in terms of lifetime of a concept has dropped further and further lower as change keeps accelerating.

 

It was in the ‘70s (1979 to be precise) that Motorola CEO Bob Galvin asked the question, “What is wrong with our company?”, that triggered the whole sequence of activities that culminated in the evolution of the Six Sigma Methodology. So, Six Sigma has gone through the Seventies, Eighties, Nineties, Noughties and is still running in the Teens. In a period of accelerated change, “Six Sigma” as a Management concept is entering its fifth decade and is still going strong. The Lean Methodology certainly has been around for even longer, for around a century.

 

No fad or poorly designed management concept can run for a such a long time without merit. The merit of the Six Sigma methodology lies in its completeness as a Management Concept. The statistical tools in the Six Sigma bouquet have been around for a very long time before Six Sigma, some of them being around for centuries. The tools were all used adequately till then, but it was the Six Sigma Methodology that further increased their effectiveness when used. It is not just coincidental that this data driven methodology uses data and the output of what has come to be called Business Analytics in all its phases and steps of all phases.

 

Thus, the science of Business Analytics is well embedded in different steps of the D-M-A-I-C phases of the Six Sigma Methodology. Obviously, there would not be an explicit reference to that. But the principles followed in certain steps are the same.  The intersection of the Six Sigma Methodology and the output of Business Analytics could be documented as under.

 

Six Sigma Phase

Six Sigma Process Step

Facet of Analytics used

Used for

D

E

F

I

N

E

Generate project ideas

Descriptive

Identifying potential improvement opportunities from past data

Select project

Descriptive

Selecting the most suitable opportunity from past data

Project Objective (Y) finalized

 

 

Current Process Mapped

 

 

Improvement Project Charter prepared

 

 

Stakeholder List prepared

 

 

Define Phase Tollgate Review Completed

 

 

M

E

A

S

U

R

E

Data Collection Plan prepared

Descriptive

Collecting current data for MSA and Process Capability

Measurement System for Y validated

Descriptive

Using the current data collected to validate the Measurement system; if required set right the measurement system and revalidate with fresh data

Performance Standard prepared

 

 

Current Process Capability assessed

Descriptive

Using the current data collected to calculate the current process capability

Gap Analysis completed

Descriptive

Identifying the gap between the current and targeted process capability

Measure Phase Tollgate Review Completed

 

 

A

N

A

L

Y

Z

E

FMEA Conducted

 

 

All potential causes (X) identified

 

 

Critical Xs identified

Descriptive

Using data collected under different conditions and conducting Hypothesis Tests

Sufficiency of critical Xs for the project verified

Predictive

Confirming with appropriate data that, if the critical Xs identified are controlled, that the targeted Y is achievable. If not, collect more data to reach sufficiency

Measurement System for Xs validated

Descriptive

Using the current data collected to validate the Measurement system; if required set right the measurement system and revalidate with fresh data

Analyze Phase Tollgate Review Completed

 

 

I

M

P

R

O

V

E

Alternative solutions generated

 

 

Alternative solutions evaluated

 

 

Best solution selected

 

 

FMEA conducted for selected solution

 

 

Selected solution piloted

 

 

Solution validated with or without changes

Prescriptive

Confirming with appropriate data that the targeted Y is achieved. If not, improve the solution further, re-pilot and test again with fresh data

Improve Phase Tollgate Review Completed

 

 

C

O

N

T

R

O

L

Control Plan for Xs prepared

 

 

Control Plan for Xs implemented

 

 

Documentation reviewed and revised

 

 

Benefits documented

 Prescriptive

 Using post improvement data, computing benefits, projecting benefits assessed for the future

Improved process transferred to process owner

 

 

Control Phase Tollgate Review Completed

 

 

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During these times of intense competition, corporations world over are trying to woo customers with continuous innovations in their products and services. At the same time working towards continuous improvements in their processes for driving down costs is an equally important priority to improve the bottom lines since clients always want more and more at less and less price.

 

Lean and Six Sigma both are adopted by organisations to drive continuous improvement in their processes and drive down costs. Lean concepts help us to eliminate waste (muda) relying on qualitative techniques like 5S, Kaizen, Kanban etc. Six Sigma is adopted with a goal of reducing variance in processes and relies on quantitative and statistical techniques to understand the central tendencies, ascertain dispersions in data sets, check for specific patterns of data distributions etc. Analysis of Variance, Correlation and regressions analysis is then done to establish whether a hypothesis is true or false. Six Sigma uses statistical techniques for problem solving which is essentially descriptive analysis to understand what has happened or what is happening with a primary objective of process improvement.

 

Business Analytics is the use of statistical techniques for Predictive and Prescriptive analysis gathering customer and competition data at different echelons of business to predict customer behaviour in different situations at different times and for different product or service offerings. Results of good predictive analysis help the business to take appropriate decisions. Business Analytics is therefore a domain which attempts to predict customer behaviour and help companies to take actions to positively influence customer behaviour to help improve top line.

 

Lean Six Sigma is therefore a methodology having a primary objective of improving bottom line through descriptive analysis which is mastered by Six Sigma experts. On the other hand Business Analytics is a methodology having a primary objective to improve top line through predictive and prescriptive analysis. Although the statistical techniques would largely remain the same, it is the change in mindset that would help six sigma experts to become excellent at Business Analytics.

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Let me start with interpreting the 3 types of analytics in the BA world as per the below table.

 

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With the brief understanding of the 3 types of Business Analytics as above, we assess their coverage in the prevalent Lean Six Sigma practices.

 

Descriptive analytics is an area that is widely covered by lean Six Sigma approach in terms of data collection for past events and interpretation, during the Define to Measure phase. The popular tools such as histograms, descriptive statistics, control charts, all perform the task of providing insight into the past performance, which will be useful for proceeding to diagnostic analytics and determine the causes. In today's world, with high data aggregation, mining and computing capabilities, the descriptive analysis is possible using big data and make it more 'real time'.This could further support and enhance the descriptive analytics that is already a part of lean Six Sigma program. 

 

Predictive Analytics is also an area covered in our existing Six Sigma cycles. Control charts are examples that use a predictive approach to prevent the occurrence of non-conformance using the statistical control limits and the probability based rules for instability. Regression exercises using multiple variables are good examples of predictive models. Response Surface Methodology is a collection of statistical techniques giving rise to an empirical model to predict the settings for optimum output response.

 

Through Prescriptive Analytics, we expect a best course of action under given situation. While Lean Six Sigma tools, be it Design of Experiments, Value Stream Analysis etc. do provide with actions or optimal settings for getting the best outputs, this could be an area for vast improvement. In the modern world prescriptive analytics looks for patterns and inter-relationships based on large amount of historical data, which could be unstructured, which is dynamic in nature. The prescriptive analytics also combines the algorithms and business rules along with established patterns based on descriptive analysis. There is the concept of "machine learning", though which we season a computerized system to continually modify predictions and prescribe the best and up to date options.

 

 

 

 

 

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Business Analytics has to deal with three types of distinct analytics, which are:

1) Descriptive Analytics: It uses data aggregation and data mining to provide insight into the past and it answers, "What has happened?"

2) Predictive Analytics: It uses statistical models and forecasts techniques to understand the future and it answers, "What could happen?"

3) Prescriptive Analytics: It uses optimisation and simulation algorithms to advice on possible outcomes and it answers: "What should we do?"

There may be a 4th type of analytics also which comes after Descriptive Analytics and is known as Diagnostic Analytics, it deals with the question "Why did it happen?"

Descriptive analytics are reports that provide historical insights regarding the company's production, financials, operations, sales, inventory and customers.

Predictive analytics provide estimates about the likelihood of a future outcome. Foundation of predictive analytics is based on probabilities.

Prescriptive analytics is all about providing advice. Prescriptive analytics are comparatively complex to administer, and most companies are not yet using them. It can be successfully used to optimize production, scheduling and inventory in the supply chain.

I personally feel that out of the three types of analytics, Prescriptive analytics is the one which has been least discussed in Six Sigma curriculum. Though little bit of Descriptive and Predictive analytics is dealt with but still systematic exposure to them from Business Analytics point of view is still lacking, because as a Six Sigma expert focus is different, so a course on Business Analytics will certainly help even those who are already certified Six Sigma BB / MBBs.

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Business Analytics explores an organisation’s data with a focus on statistical analysis which will ensure a data-driven decision making. 

 

Benefits of Business Analytics
  - Ensures the organisation to be a data driven one
  - Makes an organisation highly competitive in the industry

 -  It helps in scientific way of analysing problems,patterns and trends of business situations

 

Types of Business Analytics:

There are primarily 4 types of Business analytics namely, Descriptive analytics, Diagnostic analytics , Predictive analytics and prescriptive analytics  Let us see each one of the analytics with a very simple definition.

 

Descriptive Analytics: It provides a summary of the historical data or provides a view of what has happened.  

 

Diagnostic Analytics: It tells about past performance. What had happened and why it happened. We are interested/focused more in the Why analysis here .

 

Predictive Analytics: It tells about what can happen in the future.  It is a forecast.  It can make use of trends/patterns to help out the prediction in a much better fashion.

 

Prescriptive Analytics:  It prescribes the best possible solution/result amongst a set of choices, for a given problem or concern.

 

Comparison of Business Analytics with Lean Six Sigma terminologies

1. Descriptive analytics tells about the problem and is much akin to the problem statement that is

    present in the define phase of a Lean Six Sigma (LSS) project
2. Diagnostic analytics talks about the cause of an event/issue, which is akin to the root cause

    and analyse techniques of analyze phase in LSS such as Paretto chart, Fishbone diagram.
3. Prescriptive analytics states about the best possible solution which is akin to design phase in  

    DMADV or Improvement phase in DMAIC and usage of tools such as Pugh Matrix /Design of

    Experiment to provide best solution or to prove the results respectively.
4. Predictive Analytics is one area which needs to be explored from a LSS perspective. While the

    analytics uses lot of trends, model and probabilities for its usage, in LSS , probability
    mechanisms might be used. For finding trends, proper charts such as run charts , and other

    relevant charts should be used

 

Conclusion
While as we see each one of the analytics are meant for different purpose, the descriptive analytics, the diagnostic analytics and the prescriptive analytics are well covered in LSS community, the predictive analytics requires more focus from the LSS community.

 

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Business Analytics:

 

It is an area of the system of analysis or business analysis that contains the usage of various skills, techniques, technology, tools, assumptions, experiences, practices and importantly statistics to analyse the situation of a business in order to either know where we are with respect to where we should be or what could be done in order to reach where we should be or what steps could have been taken to reach where we should be.

 

meaning, it helps us to know the current situation, gaps, and help deciding appropriate actions towards the set goals.

 

three parts of analytics: 

 

Descriptive: what has happened ; 

most extensively used. Analysis using historic data facts or experience. 

It is reactive yet suitable to prepare or to predict the future.

 

Predictive: understanding the future. According to me, it is mostly depends on descriptive analysis. But it can also without descriptive analysis. This is very important part of the business analytics. 

 

Prescriptive: prescribes for future. Deciding the actions for future. It can be done alone, but it would be more effective if followed by the initial two parts.

 

according to my understanding, LSS captured very well the descriptive and prescriptive parts of analytics. 

LSS has not largely explored the predictive part of the analytics. 

 

 

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Descriptive Analytics is quiet well captured by Lean Six Sigma community. As industry has largely systems established to track the performance through performance metrics and these historical data is used to understand the trend of the business based on which future goal of the business can be decided. This also facilitates solving current problems of the business understanding trend of performance.

Predictive Analytics are captured to a reasonable extent by Lean Six Sigma community, though there are system in place to capture performance and use the same to build predictive engines, but due to challenges in data management and tracking, these systems has not been used to best extent.

Prescriptive Analytics are somewhere in between other two, in terms of how well they are captured by Lean Six Sigma community, as many business decisions are now based on the outcomes of the historical data trends and predicted outcome of the process. As business decisions are largely happening based on top management expectation based on their experience, prescriptive analytics is slowly pitching in for making better decision, and bringing confidence to management to make decisions based on these. But more confidence need to be built by Lean Six Sigma community to make sure this system is completely reliable.  

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As per my opinion Descriptive and prescriptive analytics areas are well covered by lean six sigma community well.

 

We need to work more on Predictive analytics area of business analytics areas. Hope this area will be covered and well explored by business analytics rather than six sigma.

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Which among the three Business Analytics areas (Descriptive, Predictive and Prescriptive) are captured by the Lean Six Sigma community reasonably well and which areas still seem largely unexplored? 

Six Sigma as a Tool Kit does justice to all three Business Analytics areas. That said, when i look at the 3 from a relative basis, personally(though i could be wrong) the following sequence comes to mind :

Descriptive : What happened in the past is best explained of the 3 areas. The data is in and the tools does a very good job in pulling out all statistical measures.

Predictive : Between the Predictive & Prescriptive, Predictive gets more justice from the the Six Sigma Tool Kits/ experts ability to capture the trends and existing variables and make a decent estimate of the times to come

Prescriptive area is the area that is fast evolving / changing; thanks to a rapidly changing business landscape. It is more to do with the multiplying pace of Change and variables falling in and out of the bucket that can influence / impact the Future !! The existing Tool Kits are having to evolve in tandem, to do enough justice to this area; making it the "Biggest Opportunity Space"

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My view is Lean Six Sigma community (in current form) is good at working on Descriptive Analytics and Prescriptive Analytics.

In simple words they analyze the past data and make meaning out of it (Descriptive Analytics) and also try to provide solutions for future business outcomes. (Prescriptive Analytics).

 

However, when it comes to Predictive Analytics-  I think both pure analysts and Lean community are doing limited value creation.

 

Pure analysts (without appreciation of Business excellence) cannot really add value to the various possibilities that are thrown open by software.

Lean Six sigma community by adapting to emerging technology can create tremendous value in the realm of predictive analytics.

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Descriptive analytics: perform statistical analysis on the data observed from a particular group only. The outcome of analysis/result/conclusions made for the specific group data, No conclusions beyond the group and any similarity to those outside the group cannot be assumed.
Predictive analytics: Perform statistical analysis from past/present data of organisation and predict trends/behaviours. Using this analytics we can perform any type of unknown events, it could be in the past, present or for future

Prescriptive Analytics: The analytics enables multiple solution/possible actions. The analytics quantifies the effect of future decisions in order to advice on possible outcomes before the decisions area actually made. Prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.

In Lean Six sigma, All statistics performed on structured data. But on Business Analytics(Descriptive, Predictive, prescriptive) any kind of data can be used and the raw data need to be organised into structured data.
Descriptive Analytics i believe Six sigma uses extensively, predictive analytics little bit functionality used in Six Sigma. But in predictive analytics future event or issues can be predicted, this prediction six sigma not contributing.

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Business Analytics (BA) Present day Business is being driven by 3 D’s (Data, Dimensions and Discovery). In this highly competitive, customer dominating and cost sensitive market, success of an organization is becoming directly proportional to its ability to extract maximum information from the business data… bytes of complex Data on multiple Dimensions of customers/organizational processes for Discovering the unknown strategies of achieving Business Excellence. Business analytics can be efficiently executed by individuals trained in Statistical and Optimization Techniques for effectively handling Big Data.  Though Traditional data analysis methods are required and effective most of the times when the data is reasonably small, they are not efficient for handing big data which is multidimensional for predictive analytics. Already trained Six Sigma practitioners are able to analyse Big and multidimensional complex data in the most efficient & effective manner and building predictive business models for developing business strategies.

  Six Sigma is not just a Quality/Productivity improvement methodology; it aims at achieving Business Excellence using Data as a primary driver and Statistics as a key technology. Six Sigma Practitioners possessing Business Analytics skills will be in a perfect position to understand business requirements, analyse the big data in a structured manner by associating the key issues of the customer & organization and finally deliver most efficient solutions. The strength of Six Sigma methodology (DMAIC/DFSS/Lean) comes from its focus on voice of customer and achieving breakthroughs by performing analytics on customer/organizational data in a structured manner using statistical thinking and optimization techniques. Six Sigma Experts install a new mind-set of driving business improvements through statistical thinking, building statistical skill sets, and a structured/disciplined project execution approach that helps an organization towards achieving Business Excellence.

 

Whereas BA is a mix of skills, technologies and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Six Sigma provides a methodology based on statistically validated logic  aimed at improving existing or New Business Processes.

Business analysis can be accentuated through the DMAIC/ DMADV methodology.

A Six Sigma Expert, for good Business Analytics, defines the goals using key parameters based on identified requirements in the first phase of Define. With approaches like SIPOC, Swimlane charts etc, this is a more systematic, and data oriented way of doing the BOSCARD in BA. Thus Six sigma Methodology helps as a catalyst for Descriptive analysis.

As the relevant aspects of the current process are measured and Quantified- It helps the Business Analyst to do an in depth analysis of the Measured data to reveal the Cause and effect Relationships along with the impact through methods like Pareto, FMEA etc. .

Thus when a cause is discovered, an Improvement is implemented to reduce variation and eliminate defects. The use Hypothesis checks and DOE helps to provide prescriptive and Predictive Analysis. Finally Six Sigma also helps to set up or design reliable control systems like control charts , check Sheets, Value Stream process charts etc to control the future performances of the process and to ensure that any deviation are corrected before they result in defects.

Simultaneously, Six Sigma also advocates the optimum use of VOC, which keeps the business analyst updated of the customer’s needs and expectations- thereby always maintaining the scope for continuous improvement and better Business process Planning.

Thus Six Sigma methodology is an enhancer of means for an Business analyst. With regards to the Question above, I personally cannot think of any Business analytical area that Six Sigma Has not explored or touched.

 

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Dear friends, once again thank you for your responses. This was really a good topic to ponder upon and of course, is not as easy as it seems.

 

Most of us got the definitions/understanding of Descriptive, Predictive and Prescriptive business analytics right. Diagnostic analytics was also mentioned usually followed by descriptive analytics.

 

The main objective of the question was to understand if and how much LSS has explored the business analytics?. Few members really enlightened us on how LSS has explored business analytics.

 

Having said this, it was really a tough choice between Anita Upadhyay and Mr. Venugopal R. To me both the answers were perfectly drafted. However, since we can select only one winner, I would go with Anita  Upadhyay as the most appropriate answer for this. Cheers !!”

 

Kind regards,

Neeraj

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