Running head: Six Sigma in Big Data 1

Implementing Six Sigma in Big Data – Training Program for

Technical Consultant at PwC

Srinivas Pochincharla

Dr. Priscilla Berry

University of Florida

Six Sigma in Big Data 2

Implementing Six Sigma in Big Data – Training Program for

Technical Consultant at PwC

Authors:

Srinivas Pochincharla, 401 East Las Olas Boulevard, Suite 1800, Fort Lauderdale, Florida 33301

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Six Sigma in Big Data 3

ContentsExecutive Summary...............................................................................4

Introduction............................................................................................5

What is Big Data.................................................................................5

Current Difficulties..............................................................................6

Solution..................................................................................................7

What is six sigma................................................................................7

Implementation of Six Sigma methodologies in Big Data...................8

Conclusion............................................................................................10

Reference.............................................................................................11

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Executive Summary

As a consulting and advisory firm, Price Waterhouse Coopers (PwC) currently

provides data assurance solutions to clients. With the advent of big data,

PwC is also venturing into the field of predictive analytics; analyzing gigantic

amounts of data using different complex techniques ranging from NOSQL

databases to proprietary solutions, such as SAS. Big data is an abstract

ideology, where extraction, analyzing and sorting the data can help an

organization predict the future trends and achieve profitability in a highly

competitive market. The problem with big data lies with the uncertainty

associated and one of the many challenges involve the extraction process to

be time efficient and error free. By using the Six Sigma process, the process

can be enhanced efficiently. Six Sigma is an intricate process where the

organization meticulously observes and mitigates the errors and deviations

occurring in its operations by applying rules and strategies. Implementation

of Six Sigma has resulted in an estimated savings of $427 billion for the

Fortune 500 companies (Marx, 2007).

Through combining the elements of six sigma and the predictive analytics

concepts of big data, PwC can minimize the uncertainty associated with the

data and streamline the process. In big data, categorization is difficult.

Therefore, using the process of Six Sigma will make categorization easier, as

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Six Sigma is more statistical in concept. Furthermore, using Six Sigma will

also result in better time cycle, as time management for the teams working

on the data extraction will improve, thus providing a critical competitive

edge to the firm. Implementing Six Sigma through the teams working on big

data projects, will result in higher client satisfaction, thus increasing revenue

for PwC.

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Introduction

What is Big Data

The amount of data in our world has been expanding, and analyzing large

data sets —— so-called big data —— will become a key component of

competition, underpinning new waves of productivity growth, innovation, and

consumer surplus. Big Data is currently a $53.4 billion industry and is

growing exponentially, as shown in Figure 1 (Kelly, 2014).

Big data is usually referred to as large amounts of data. Having a large chunk

of data is useless unless some information is extracted from it. Not only does

the extraction have to be meaningful but it also has to be rapid. Extraction of

data usually depends on three factors:

1) Volume, how big the data is;

2) Velocity, how fast the data is growing;

3) Variety, what types of data are in the sample collected.

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Figure 1

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An excellent example is the retail market chain Target. Using data analytics

on its customers and by tracking what they are purchasing, the retail giant is

able to predict what the customers are planning to buy next and

consequently send them advertisements related to the product. The

prediction is very accurate. For instance, there was a famous incident where

a gentleman asked Target’s customer service to stop mailing him coupons

related to pregnancy.

He came to find out later that his daughter was pregnant and Target was

mailing him the coupons by predicting the purchase history occurring under

their household account (Goswami, 2014).

Current Difficulties

The significant problem associated with Big Data is being able to relate it.

Since the data is in large volumes and is spontaneous, being unable to relate

the data causes problems with the speed of extraction of meaningful data as

shown in Figure 2 (Taleb, 2013). The data has to be analyzed thoroughly by

professionals, and special statistics are used on the data to approximate its

meaning.

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PwC specializes in this field, where the risk assurance branch essentially has

a data assurance department, which studies large amounts of data for the

clients. Using different software tools and providing various control checks,

the data is extracted for the clients and its accuracy is insured. However, the

process is time consuming, because it requires effectively-managed teams

and emphasis on client priorities.

Another problem associated with Big Data, are the security issues. The most

recent data breach occurred with Target and Sony when the customers’

private information was compromised. Security is becoming an essential

element in driving customer satisfaction for companies. PwC provides IT

security services that investigate the companies’ security loopholes and that

make the data more secure by performing analyses.

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Figure 2

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Solution

What is six sigma

Six Sigma improves the quality of process outputs by identifying and

removing the causes of defects (errors) in business processes. Six Sigma

(although it seems like a technical component) is applicable to all kinds of

industries and companies. It assists users in developing minimal error

products, which also enhance and improve the efficiency of the process

involved.

Six sigma follows two project improvement methodologies-DMIAC (Define,

Measure, Analyze, Improve and Control) and DMADV (Define, Measure,

Analyze, Design and Verify), and each phase is composed of five different

phases. Companies usually start to implement the DMAIC methodology later

if the organization culture permits DMADV to be added to it. Only DMAIC

methodology will be dealt with, in the current article.

Implementation of Six Sigma methodologies in Big Data

Big data is becoming fundamental to the future of business. Six Sigma

essentially is statistics, as is big data. Processes, organization structures and

metrics were all designed to support the “zero defects” philosophy of Six

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Sigma. Utilization of the Six Sigma process can effectively diminish human

error problems (Goswami, 2014).

Six Sigma can effectively be utilized to provide big data solutions through the

five phase (DMAIC) process:

In the define phase, the voice of the customer (VOC), which translates all

customers’ core needs into technical requirements can process intangible

into a tangible/usable form. The VOC is crucial because it is known as (CTQs)

critical quality measures. This process is essentially important in the

consulting industry, because all the other processes are dependent upon this

phase. By critically understanding customer needs through applying the Six

Sigma process, chances of errors in correlating the data can be significantly

reduced.

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Figure 3

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The failure modes effects analysis (FMEA) in the measuring phase can

analyze the potential failure modes for each of the measured fields.

Executives use the feedback from FMEA to predict disruptions and allow

anticipated actions. This mode is very critical to the speed of extraction of

data, because any bottlenecks relating to data crunching can hinder the

process of extracting it efficiently and, most importantly, to extract it quickly.

The third phase (Analysis) uses data and decomposes the collected statistics

to offer practical solutions for the problems at hand. Experiment in this phase

is a tool that effectively and efficiently analyzes the cause-and-effect

relationship between the measured fields and the CTQ’s.

The improve phase identifies the variations and develops control charts by

simulating the changes in data flow. These charts can be used for real-time

monitoring (Hartwig, 2012).

The control phase in Six Sigma monitors the variability in the changed

system. The control phase is critical in the sense that any vulnerabilities

related to the data need to be exposed in real time. This process is extremely

difficult considering the volume of the data, and implementation of Six Sigma

methodology in this phase can essentially act as a safeguard for the data at

hand. Any data breach that occurs, if detected in real time, can help

companies employ better control schemes.

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Conclusion

With the advent of big data, industries are moving forward in a competitive

environment where predicting the future through historical analysis will

prove to be a major game changer. However, the technology is fairly new,

considering that Web 2.0, where users can actually interact over the Internet,

was conceived in the last decade. Vast amounts of data have to be managed

effectively, and to do that, research and progress are proceeding at a brisk

space, where new fields of study such as predictive analytics, visual analysis

and, information systems are helping to define the future.

Six Sigma has proven to be a very effective project management solution in

various Fortune 500 industries. In fact, major firms consider compensating

employees, if they are Six Sigma-certified associates. The statistical

improvements made with Six Sigma can prove to be extremely critical for the

future of big data. Not only can the analysis of the data be improved with the

application of Six Sigma, but collection of data and, most importantly,

reduced time cycles in extraction of data can prove to be the critical edge

that industries require in this competitive environment.

As a consulting firm for whom process improvement and risk assurance are

major components of revenue generation, it is extremely important for PwC

to apply older concepts to recent advancements to give our firm a

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competitive edge in the consulting world. Especially, since the competing

firms are already forming dedicated departments related to the issue of big

data (EY, 2014). The timing is of critical essence for innovation and progress

in the related field or PwC may risk losing clients to competition.

Reference

Marx, M. (2007, Jan 11). Six sigma saves the fortune 500 $427 billion. . Retrieved from http://www.isixsigma.com/community/blogs/six-sigma-saves-fortune-500-427-billion/

Kelly, J. (2014, Feb 12). Big data vendor revenue and market forecast 2013-2017. Retrieved from http://wikibon.org/wiki/v/Big_Data_Vendor_Revenue_and_Market_Forecast_2013-2017

Goswami, B. (2014, Feb 14). Why six sigma learnings are relevant for big data. Retrieved from http://insights-on-business.com/electronics/why-six-sigma-learnings-are-relevant-for-big-data/

Taleb, N. (2013, Feb 8). Beware the big error of ‘big data’. Retrieved from http://www.wired.com/2013/02/big-data-means-big-errors-people/

Dmiac vs dmadv. (n.d.). Retrieved from http://www.isixsigma.com/new-to-six-sigma/design-for-six-sigma-dfss/dmaic-versus-dmadv/

Six sigma dmadv methodologies. (n.d.). Retrieved from http://www.villanovau.com/six-sigma-methodology-dmadv/

Hartwig, C. (2012, Apr 10). The parallels between big data and the advent of six sigma. Retrieved from http://www.katoka.com.au/2012/04/big-data-and-six-sigma/

EY (2014, Apr 2). Corporate website detailing service offerings related to big data. Retrieved from

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http://www.ey.com/US/en/Services/Advisory/IT

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