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Data Governance

White Paper

How to Use Six Sigmato Communicate Data Quality Improvement

by Joy Medved

2 | Baseline Consulting



How to Use Six Sigma to Communicate Data Quality Improvement | 3

� Data Quality Improvement: A Common Communications Conundrum .....................4

� Six Sigma: Improving Data Quality & Communicating Data Quality Improvement .....5

� Phase I: Defining High Impact Data Quality Elements & Data Processes ........................6

� Phase II: Measuring High Impact Data Elements & Mapping Data Processes ..............10

� Phase III: Analyzing Root Causes of Poor Data Quality & Data Process Breakdown .14

� Phase IV: Improving Data Quality by Improving Data Processes ..................................15

� Phase V: Controlling Data Quality Improvement by Controlling Data Processes .........16

� Conclusion ......................................................................................................................................17

Contents

How to Use Six Sigma to Communicate Data Quality Improvement | 5How to Use Six Sigma to Communicate Data Quality Improvement | 5

Data Quality ImprovementA Common Communications Conundrum

As data quality professionals we understand what data quality (or the lack thereof) is.

However, we can quickly find ourselves wrapped in a time-consuming communications

conundrum when sitting down with executives and other stakeholders to propose the need

for a data quality improvement effort or explain the progress of our existing efforts.

As many data quality professionals have discovered, Six Sigma is a continuous improve-

ment business philosophy that can be extremely useful for enhancing data quality improve-

ment efforts. What is not so commonly well known, however, is that Six Sigma’s structured

framework and tools can also be extremely useful to enhance communications with execu-

tives and other stakeholders.

Does this sound familiar? You are a mild-mannered marketing manager that has acquired

access to a recently developed sales and marketing data mart. After anxiously waiting six

months to implement a series of new high-tech marketing campaigns – the excitement of

analyzing the results building daily – you find yourself, once again, confronted with the

harsh reality of dirty data. With a sinking feeling, you quickly realize that instead delivering

comprehensive campaign results to the executives and other stakeholders, you are faced

with having to explain an expensive pile of returned mailings and statistics that even your

parrot would laugh at. Gathering your notes together, you prepare a comprehensive propos-

al for a data quality improvement plan. After several attempts to explain the improvement

effort, most of the executives and other stakeholders have finally agreed that a data quality

plan is probably a good idea, but… They really do not understand the scope of such an

effort – they need more time and information.

This was the situation as it was presented to Baseline Consulting by one of our clients, a

national pharmaceutical company. Understanding this common communications conun-

drum, Baseline was able to assist the client by implementing a series of Six Sigma tools

designed to identify, monitor and quantifiably communicate the need for improvement in

a way that was easy to understand by all levels of staff and management. Once high risk

data elements were exposed and effectively communicated, the data quality improvement

effort was successfully funded and scoped. In addition, the Six Sigma tools put into place

during the proposal phase allowed the data quality team to begin its effort with a signifi-

cant amount of valuable data already collected.

Are you preparing a data quality improvement proposal to secure funding? Already hip

deep in the middle of an improvement effort? Regardless of your situation, this paper will

provide you with an overview of how the Six Sigma methodology can be applied to your

efforts, with particular emphasis on communicating improvement needs and progress to

executives and other stakeholders.

After several attempts

to explain the

improvement effort,

most of the executives

and other stakeholders

have finally agreed that

a data quality plan is

probably a good idea,

but…



Six SigmaImproving Data Quality & Communicating Data Quality Improvement

Although Six Sigma has been around for many years, data quality professionals have only

recently begun to take advantage of the benefits of this tried-and-true methodology to

enhance their improvement efforts. There are two primary reasons why Six Sigma is so prac-

tical for data quality improvement. First, Six Sigma provides a structured methodology for

affecting change in quality by Defining, Measuring, Analyzing, Improving and Controlling

existing processes – any processes. Second, data quality can only be improved by affecting

change in existing data processes. In other words, improving data quality requires more than

just cleansing the data itself; to affect and maintain data quality improvement, there must

also be improvement to the data processes.

Simply put, the Six Sigma DMAIC methodology can be highly effective for any business, in

any industry, for any type of data quality improvement effort. The methodology focuses on

decreasing measurable defects, or errors, which can be defined as anything that causes cus-

tomer dissatisfaction. The tools associated with the Six Sigma DMAIC methodology provide

a highly effective means with which to quickly and easily communicate improvement needs

and progress to executives and other stakeholders. Some of the more effective tools are

highlighted below.

The Six Sigma DMAIC methodology can be incorporated into your existing data quality

methodologies to affect change in existing data processes through the following framework:

�� Phase I – Defining High Impact Data Quality Elements and Data Processes

�� Phase II – Measuring High Impact Data Elements and Mapping Data Processes

�� Phase III – Analyzing Root Causes of Poor Data Quality and Data Process Breakdown

�� Phase IV – Improving Data Quality by Improving Data Processes

�� Phase V – Controlling Data Quality Improvement by Controlling Data Processes

The tools associated

with the Six Sigma

DMAIC methodology

provide a highly

effective means with

which to quickly and

easily communicate

improvement needs and

progress to executives

and other stakeholders.


Phase IDefining High Impact Data Quality Elements and Data Processes

Before data quality can be improved, it is necessary to identify the data processes—i.e.,

processes with data quality-related issues—that will benefit most from an improvement

effort. This first phase in the DMAIC methodology, Defining, provides for identifying and

communicating the goals, objectives, data process deliverables, data customers (i.e., data

users), and high impact data elements for your improvement effort. Much of this informa-

tion should be collected early in the effort, as it is needed to define and communicate

scope to executives and other stakeholders.

Following are several example items that are typically identified and communicated during

the Defining phase.

Key Data Customers and Data Processes to be Improved: Creating SIPOC Flowchart

Diagrams can be very useful to communicate the macro-level scope of your improvement

effort. By design, each SIPOC flowchart diagram identifies a single specific data process to

be included in your improvement effort, as well as precisely where the data process starts

and where it ends, and who your key data customers are.

ANALYZINGEvaluate level ofvariation and rootcauses; identifies

vital few that,when improved,will provide mostsignificant return

IMPROVINGDevelops andimplements

improvementprocedures based

on root causeanalysis and

validatedmeasures

CONTROLLINGEstablishesand controls

procedures tomonitor and

maintainimprovement

efforts

DEFININGIdentify and definegoals, objectives,

data processdeliverables, datacustomers, andhigh impact dataquality drivers

MEASURINGPrioritize highimpact data

elements, developmeasurable

baseline metrics,create detailed

mappings

DM

AI

C

Figure 1: The Six Sigma DMAIC Framework

How to Use Six Sigma to Communicate Data Quality Improvement | 98 | Baseline Consulting

How to Use Six Sigma to Communicate Data Quality Improvement

Each diagram should include the following information about your data process:

�� Suppliers – Also known as data sources, suppliers are the starting point of the data

process. This step identifies all source systems (automated and manual) and the peo-

ple involved. This will give you a list of where to get information for building a more

detailed micro-level flowchart later on, as well as a list of some of the key data cus-

tomers that need to be involved.

�� Inputs – This step identifies which data elements will be considered in the improve-

ment effort. It is not necessary to include all source system data elements loaded into

the data process, only those identified as most affected and in need of improvement.

�� Process – This step provides a macro-level overview of the entire data process. It

should include five to seven main process areas that follow a sequence of events mir-

roring the data flow from start to finish, e.g., Suppliers to Customers.

�� Outputs – This is the ending point of the data process. This step identifies how the

data elements flow out of the data process. In other words, how the data elements are

used. Identify what reports, applications, etc. use the data at the end of this data

process. Knowing the desired outcomes of the data being used will give you an

understanding of how and why the data is or is not fit for use.

�� Customers – This step identifies the data users, i.e., additional key data customers.

Consider why the data process exists and who the data users serve, both internally

and externally. After key data customers have been identified, as described above, you

can interview them to identify their needs and priorities, including who needs what,

where, when, why, how and how much. Key data customers should represent a vari-

ety of staff, analysts, and managers who contribute to and/or receive data from any

part of the data process.

SIPOC Flowchart DiagramSample Data Process Illustration

ICustomers

CS P OOutputsProcessInputsSources

MarketingReportsSales Reports

MarketingManagerSalesManager

CustomerAccountAddress

CRMERPEDW3rd Party

Apply ETLRules

ModelAttributes

AcquireData

Load DataMart

CreateReports

Figure 2: SIPOC Flowchart Diagram


Data Process Business Rules: If data business rules and acceptable data parameters do not

currently exist or are not yet documented, they will need to be defined and documented

before they can be measured. Some examples of requirements and acceptable data parame-

ters that may need to be collected include: unique identifiers, key fields, and data types;

null fields, minimum, maximum, and other values; formatting standards, precision and cal-

culations; relationships, aggregations, history and timelines; definitions and other metada-

ta; and all other transformation rules.

In order to collect meaningful data, interviews should be conducted one-on-one or in a

small group, and without leading or otherwise influencing answers. Interviewers should

make every effort to collect information about where documents, metadata, business rules,

and other forms of information may be located and accessed. Interviewers should also

understand that the data customers’ perceptions of the state of the data quality are just as

important as the actual state of the data quality, and directly impact data reliability and

usage.

In addition to collecting valuable information, conducting interviews with key data cus-

tomers provides an opportunity to communicate the purpose of the data improvement

effort, to solicit participation, and to understand and respond to concerns.

Why do you thinkyou are unable to

access what you needfrom the data mart?

What information doyou need most for

your reports?Who do you rely onto query the data?

What, if improved,would make your

work better, easier,and faster?

What are your top10 most important

data elements?

When do you needthe data updated,and how often?

What data do you needthat you currently donot have access to?

Where do you getyour data?

What source?

Figure 3: Sample Data Interviewing Questions



Scope: A Project Charter is a highly effective method for collecting and communicating

information about the scope of your data quality improvement effort. The project charter

provides an opportunity for executives, team members and other stakeholders to define,

document and obtain agreement on (sign-off on) all aspects of the improvement effort. The

project charter should be written such that all details related to the project are explicitly

defined and easy to read. To this end, the project charter should include the following

items:

�� Project Scope: This section communicates the effort’s purpose, limitations and priori-

ties as they are known at the time. It should briefly describe: the need for improve-

ment (justification), including the problems and opportunities to be addressed; the

process of how the effort was initiated; and, the data process or processes being tar-

geted for improvement.

�� Roles and Responsibilities: This section communicates the roles and responsibilities

of all stakeholders and team members involved in the effort. It should include a

responsibility matrix that identifies high-level tasks and decision-making responsibili-

ties for everyone involved.

�� Objectives and Deliverables: This section communicates the effort’s intended goals,

and should be designed with input from the data customers. Objectives should be

quantified as much as possible and include how baseline metrics will be identified

and how the improvement (and success) will be measured. Specific deliverables and

milestone dates should be listed, including dependencies and major tasks necessary

to complete the deliverables. Deliverables can be a product, process, plan, or service,

such as training.

�� Impact and Risks: This section communicates how improvement, or the lack thereof,

will potentially impact the business. It should include an overview of what is impor-

tant to the data customers and why, as well as an overview of the potential risks and

challenges. This section should not include assumptions or solutions.

�� Resource and Budget Management: This section communicates all resource, budget

and scheduling information by phase, and how changes are to be approved.

Consideration for both internal and external resources should be described for each

phase, including the source for each resource and what the expected budget will be.

A Project Charter is a

highly effective method

for collecting and

communicating

information about the

scope of your data

quality improvement

effort.


Phase IIMeasuring High Impact Data Elements and Mapping Data Processes

With the improvement effort successfully scoped and funded, this is where the fun begins.

The Measuring Phase is where data quality professionals can really start gaining momen-

tum. Now that all the data processes and related elements have been identified and defined

(as described in the Defining phase), it is necessary to prioritize high impact data elements,

develop measurable baseline metrics, and create detailed mappings of the data processes.

Following are several useful tools for collecting and communicating baseline metrics and

priorities during the Measuring Phase.

Decision Matrices: Once all data customers have been interviewed and all related data ele-

ments have been identified, a decision matrix can be developed to help prioritize and com-

municate the specific high impact data elements requiring improvement. Data customers

are provided with a tailored list of potentially high impact data elements and asked to pri-

oritize the list according to their needs and use of the data. Specifically, data elements are

ranked based on the impact of having missing or incorrect data. This provides focus to

those vital few data elements that, when improved, will provide the greatest return to the

business.

Control Charts: By using a variety of basic statistics, data profiling, and control charts, you

will be able to develop and measure baseline metrics for the high impact data elements that

were determined to be of high priority by your data customers. Control charts monitor and

communicate how a particular data element changes over time and can provide an extreme-

ly helpful visual explanation of precisely where and when errors occur. The data collected

from control charts are also extremely helpful for assisting with root-cause analyses and

improvement procedures, as described below in the Improving phase. Control ChartSample Data Element Illustration

Average Daily Data Entry Errors (in Hundreds)

Figure 4: Control Chart



Pareto Charts: Once preliminary measures have been collected from control charts, you

will be able to use Pareto charts to develop and measure acceptable variation parameters for

established data business rules and other requirements. Pareto charts are, by design, a

graphical representation of the 80-20 rule – showing how 80% of the problems are caused

by which 20% of the issues. A Pareto chart is a column chart that provides for data ele-

ments to be organized according to their respective number of data errors in descending

order. In addition, a line graph is overlaid that represents the cumulative percent totals for

the data elements being measured. Together, the bar chart and the line graph can very

quickly and easily communicate which data elements represent the greatest number of

issues, and which few would represent the greatest return, if improved.

Process Sigma: The Process Sigma is a statistical representation of the actual level of quality

for whatever is being measured. By using the business rules defined in Phase I, and the

acceptable variation parameters identified in Phase II, it is possible to calculate the initial

Process Sigma level. This calculation can provide an excellent baseline metric to communi-

cate improvement needs and improvement progress. Process Sigma is calculated by using

data from control charts and other similar tools, and is represented as “number of errors”

out of “number of Defects Per Million Opportunities (DPMO).” To have a Process Sigma of

6 (i.e., operating at 6 Sigma), there can be no more than 3.4 total errors or defects out of a

possible one million opportunities. Operating at a Process Sigma level of 6 is equivalent to

operating at 99.9999966% perfection. Unfortunately, most companies operate at a 3 to 4

Process Sigma level, tolerating somewhere between 6,210 to 66,800 DPMO.

400

350

300

250

200

150

100

50

0

High Impact Data Elements - # of Errors

Emails Phone LastName

City Street DOB SSN FirstName

State Country

Figure 5: Pareto Chart


Once a baseline Process Sigma level has been calculated, you can use that information to

quantifiably extrapolate and communicate potential cost savings to support your improve-

ment effort. To extrapolate potential cost savings, simply identify the Process Sigma level for

whatever you are measuring—for example, customer addresses being used for a marketing

campaign—and determine how many have incorrect data preventing the mailer from reach-

ing the customer. By calculating the potential profit (gross or net – your choice), you can

easily calculate how much potential business is being lost from bad data. Next, calculate in

the cost of the mailers themselves and the cost of the improvement effort, and you will

have some rough, but useful, return on investment (ROI) calculations to communicate to

executives and other stakeholders. It is interesting to see how much money can potentially

be saved by implementing a data quality improvement effort!

Data Process Mappings: Data process mappings are an excellent method for graphically

communicating not only where data process deficiencies exist, but why they exist, and how

the data process can be improved. Keep in mind that in order to be an effective means of

communication, detailed process flowchart diagrams should always be developed with the

appropriate shapes, symbols and directional arrows, using common business terms every-

one can understand.

Using the macro-level SIPOC flowchart diagrams created during the Defining phase as a

model, you can now develop more detailed micro-level process flowchart diagrams that

graphically frame the boundaries, sequence of events, and rules of the data processes.

Although process flowchart diagrams may connect multiple data processes, each diagram

should contain only one data process and be an expansion of a single SIPOC flowchart dia-

gram.

Process Sigma ChartSample Calculation Illustration

6.005.515.004.504.003.503.002.502.001.501.000.510.09

3.430

2301,3506,210

22,70066,800

158,000308,000500,000690,000840,000920,000

99.999799.997099.977099.865099.379097.730093.320084.200069.200050.000031.000016.00008.0000

Sigma DPMO % Yield

Figure 6: Process Sigma Chart

Once a baseline Process

Sigma level has been

calculated, you can use

that information to

quantifiably extrapolate

and communicate

potential cost savings

to support your

improvement effort.



When creating your detailed process flowchart diagrams, keep in mind that you will ulti-

mately create a set of four diagrams, one set per data process:

�� The first mapping details the data process as it currently is. In other words, the

process as people actually use it. This takes into account skipped and repeated tasks,

and other extra efforts people have incorporated themselves in order to fulfill their

own data needs.

�� The second mapping details the data process as it currently should be. In other

words, the process as it was originally established. This can give you a good idea of

process breakdown points.

�� The third mapping, which is typically completed as part of the Analyzing phase,

details the data process as it will be with proposed improvements.

�� The fourth mapping represents actual improvements as implemented. This last dia-

gram will be a “living” diagram that needs to be continuously monitored and updat-

ed as the process evolves over time. It is also an excellent training tool for new hires

or anyone else utilizing data processes.

Detailed Process FlowchartSample Shapes Illustration

Preparation

DataInput/OutputStart Process

ManualOperation

Decision

Document

Database Subroutine

End

Figure 7: Detailed Process Flowchart


Conducting root-cause

analyses means drilling

down below obvious

symptoms to identify

the true root causes of

high-impact data

elements.


Phase IIIAnalyzing Root Causes of Poor Data Quality and Data Process Breakdown

Phase III is where you will take advantage of all the information you have collected so far.

By analyzing all of the interviews, charts, mappings, diagrams, etc., you can begin to identi-

fy data process best practices and determine the most beneficial process improvement

opportunities. This is done by analyzing the problematic data processes to determine the

root causes of poor quality. Conducting root-cause analyses means drilling down below

obvious symptoms to identify the true root causes of high-impact data elements. By evalu-

ating the level of variation and concentrating on potential causes that can be reasonably

identified and defined--not to mention managed and controlled for cost-effective improve-

ment--you can identify the vital few data elements that, when improved, will provide the

most significant return.

Two tools commonly used to help identify root cause are the 5 Whys Diagram and the

Fishbone Diagram. A 5 Whys diagram is a structured, question-asking method of brain-

storming that focuses on one defect or category per diagram. This method is most effective

if drilled to at least five levels; however, as long as additional answers can be identified,

there is potential to identify additional levels of root cause. If multiple causes are identified,

using the Fishbone diagram can help graphically organize all cause and effect relationships

with increasing detail on a single diagram. For either diagram, be sure to concentrate on

current causes, not symptoms or hypothetical causes.

Why?

Why?

Why?

Why?

Why?

Figure 8: 5 Why’s Diagram



Phase IVImproving Data Quality by Improving Data Processes

The purpose of any data quality improvement effort is to upgrade the data’s fitness for use,

such that the data customer has the highest possible degree of confidence in the data’s relia-

bility and effectiveness. By “fitness for use,” we mean that there is a high degree of data

quality. As you have probably figured out by now, in order to improve and maintain data

quality, improvement efforts must include more than just profiling and cleansing data; it

must also include an effective data process improvement effort that has been developed,

implemented and tested based on root-cause analyses and validation measures. In order for

a data process to be considered improved, there must be verification of improved data qual-

ity—improved data fitness for use—based on the business rules defined in Phase I, and the

acceptable variation parameters developed and measured in Phase II.

Therefore, in order for your data to be fit for use, your improvement effort must demon-

strate better:

�� Data Accessibility – Meaning that the data can be: easy to find and easy to access;

readily available when and where needed, with appropriate timeliness and perform-

ance of reports; and, updated in a timely manner and with the appropriate amount

of history. Data Accessibility also includes easily accessible metadata that is under-

standable for all data clients.

Figure 9: Fishbone Diagram

Fishbone DiagramSample 8-Ps Illustration

Procedures

TheProblem

Price Procedures People Processes

Place/Plant Policies Product

DetailDetail

DetailDetail

Detail

DetailDetail

Detail

DetailDetail

Detail

Detail

DetailDetail

Detail

DetailDetail

Detail

DetailDetail

Detail

DetailDetail

Detail


In order to monitor and

maintain data process

improvements, your

data quality

improvement effort

must also include an

effective and sanctioned

Data Governance

process.


�� Data Accuracy – Meaning that the data maintains: purpose and relevance of data

context and metric definition; correctness and meaning of values; and, appropriate

level of detail and relationship.

�� Data Integrity – Meaning that the data maintains: consistency in how data are col-

lected and measured over time; compliance to business and process rules, require-

ments, and regulations; objectivity of values and their metadata; and, overall com-

pleteness and precision.

�� Data Usability – Meaning that the data maintains: uniqueness and lack of redundan-

cy; relevance to the business; and, overall usefulness, stability, and serviceability.

Phase VControlling Data Quality Improvement by Controlling Data Processes

As stated earlier: “Data quality can only be improved by affecting change in existing data

processes.” Without implementing improved data processes, it is impossible to affect

change in data quality. But how do you keep the data process itself from breaking down?

With control! Once you have Defined, Measured, Analyzed, and Improved your data

process, or processes, Controls need to be put in place in order to monitor and maintain

the new improvements.

It is important to understand that a data process improvement effort is not just a “one-off”

project, but rather an on-going effort that requires continuous monitoring and maintenance

to ensure all data processes are managed as efficiently as possible.

In order to monitor and maintain data process improvements, your data quality improve-

ment effort must also include an effective and sanctioned Data Governance process that, at

a minimum, ensures that:

�� Improvements will be continuously monitored, maintained, and reviewed.

�� Effective communications between IS/IT staff and business end-users.

�� Changes, issues, and tasks will be documented and communicated.

�� Data business and process rules, requirements, and regulations are complied with.



ConclusionThis paper has provided you with an overview of how the Six Sigma methodology can be

successfully applied to your data quality improvement efforts, and how that same method-

ology can also enhance your communications with executives and other stakeholders.

Whether you are preparing a data quality improvement proposal to secure funding, or are

in full swing with your improvement effort, we urge you to consider the methodology and

tools described above and see how you can apply them to enhance your efforts.


About the AuthorJoy Medved is a Senior Consultant and certified Six Sigma Black Belt with Baseline Consulting, a

business advisory and technology solutions firm. Joy has more than 16 years of experience in: infor-

mation and data quality; business and data analytics; process improvement and re-engineering;

and, training and instruction design. Joy’s information and data quality expertise centers on obtain-

ing optimal improvement through both quantitative and qualitative analysis methods, and then

training technical and non-technical staff on how to maintain that improvement. In her spare time,

Joy is an expert motorcyclist employing her professional skills to help others improve their motorcycle

riding experience.

20

Baseline Consulting is a management and technology consulting firm specializing in

data integration and business analytic services to help companies enhance the value of

enterprise data and improve the performance of their business. Baseline’s proven,

structured approaches uniquely position us to help clients achieve self-sufficiency

in designing, delivering, and managing data as a corporate asset.

Baseline Consulting Group15300 Ventura Blvd., Suite 523Sherman Oaks, CA 91403

1-818-906-7638

www.baseline-consulting.com

© 2009 Baseline Consulting Group. All Rights Reserved.

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