12 guidelines for success in data quality projects

12 Guidelines for Success in Data Quality Projects

IntroductionThe need for accuracy, completeness, and quality of data generated and used in companies and organizations

is not a new concept. The “father of computing”, Charles Babbage, asked over 150 years ago how “the

right answers” could come out of his computing machine if the “wrong figures” were put in. The concept

of “Garbage In, Garbage Out” was created by the earliest programmers in the 1950’s and subsequently

taught to generations of IT professionals.

This paper discusses key characteristics of data quality initiatives and provides actionable guidelines to

help make your project a success, from conception through implementation and tracking your ROI.

2 12 GUIDELINES FOR SUCCESS IN DATA QUALITY PROJECTS

The solution to the data quality problems that permeate companies and other organizations is for them to

undertake data quality programs. The objective is to profile, standardize, cleanse, and integrate data across

the enterprise in order to create consistent, accurate, reliable information for decision-making, reporting, and

day-to-day operations, and to put standards, policies, education, and processes in place to ensure that data

remains “clean” and accurate on an ongoing basis.

One of the simplest – and still current -- definitions of data quality is from Martin J. Eppler, in his book, Managing

Information Quality: Increasing the Value of Information in Knowledge-Intensive Products and Processes, where

he defines “information quality” as:

Data quality problems require data quality solutions

[T]he fitness for use of information; information that meets the

requirements of its authors, users, and administrators.1

Therefore, the challenge for any data quality initiative must be to ensure that data standards, quality, and

usage meet the requirements of all “authors, users, and administrators”. Further, this means identifying all of

the “authors, users, and administrators” and the business processes in which they use data.

Typical areas to address in deciding where to start data quality initiatives include:

• Business intelligence (BI) systems, data warehouses, analytics

• Customer-facing systems

• Financial reports for compliance or audit

• Transaction processing systems

• Analysis of purchasing and supplier spending


Ultimately, data quality initiatives must address three overarching principles:

Undertaking a data quality project or series of projects requires making a case to those who will approve funding

that such an effort will have a positive return on investment for the company. A critical step in beginning a data

quality effort is to translate the technical symptoms and problems into business problems, symptoms, and

financial impacts. This requires that any data quality project be envisioned and managed as a joint business/

IT project, with in-depth engagement of the business leaders.

Finally, the implementation, administration, and ongoing use of software tools for data cleansing, de-duplication,

and data management will require large-scale IT project investment and project management. However, the

key to data quality project success is to keep the IT and technology parts of the project secondary to the

business and process focus part.

1. Completeness of data

2. Accuracy of data

3. Uniqueness of data2

Any data quality project should be envisioned and managed as a joint business/IT project.


The following points are distilled from hundreds of case histories, representing data quality projects large and

small, and both successful and unsuccessful. The list is not intended to be exhaustive but rather to provide a

starting point for organizations to begin their data quality improvement programs.

Guidelines for implementing data quality programs

1. Document and communicate the costs and missed opportunities created by poor data quality

Look at both cost and missed revenue opportunities. Work with business representatives and analysts from

the finance department to assign values that will stand up to scrutiny. Each functional benefit, e.g., “accurate

invoices”, claimed for the project should have a metric and a dollar value conversion. For example, if the data

quality project aims to improve the accuracy of customer invoices, the metric is “% accurate” and the dollar

value (or financial benefit) would come from the statement, “Each inaccurate invoice costs $250 in labor plus

$500 in lost customer goodwill”. In this example, if the company generates 10,000 invoices per year, and the

baseline metric is 85% accuracy, then an improvement to invoice accuracy to 95% would be worth $750,000

per year (10,000 invoices X 10% improvement X $750 value of each newly accurate invoice = $750,000.)

2. Prioritize by attacking a small subset of the data quality problem first

Create a decision-making quadrant to aid

in selecting the best project to start first.3

Then focus on creating a series of small wins

and proving the value of data quality one

project at a time. Evaluate potential projects

against value and difficulty by creating a

scoring method and placing projects on the

matrix according to score. Then start with the

project that pays off the most for the least

effort, demonstrate ROI from that project,

and leverage success to move on to the next

project.

Start here

Irrelevant

No funding

Wait and see

Low HangingFruit

High value

Hard to do

Low value

Easy to do


A data project plan must show how all team members will work toward a common goal.

3. Set attainable and measurable business objectives

Translate potential improvements in data quality into impacts on business outcomes or benefits. Projects will

be approved and funded based on executives’ confidence that the efforts will (1) be completed successfully,

(2) be completed in a reasonable period of time, and (3) deliver positive financial benefits in support of overall

corporate or organizational goals.

4. Align business and IT objectives, expectations, and organizations

Funding is rarely granted for nebulous “data processing” or IT projects with no clear impact on business

objectives. The business case and project plan must show how all team members – IT, business, finance,

operations – will be organized to work together toward shared, measureable, clear objectives, and a common

set of expectations of what success looks like.


5. Confirm senior management engagement and participation

Data quality is an issue that involves the entire organization. As such, all project plans and regular reporting

must involve senior management long after the investments are approved and the “all systems go” is given. Plan

for project team members from all functions to provide regular reporting back to their functional leadership.

In addition, the project manager should be providing both scheduled updates and ad hoc reports to both his

own management and to all of the organization’s senior business leaders. It is important to show progress

by celebrating the small wins for each milestone along the way. This helps to ensure continued support and

engagement from all stakeholders.

6. Identify business processes, supporting data, and data interdependencies

An important early step in the process is to do a complete top-to-bottom assessment of the business, its

business processes, and operations. What data is captured, stored, passed between organizations? Where are

the data sources and where are the end points? This assessment will take around a month to complete but

will pay off in the end by saving you a significant amount of re-work down the road. The “we will figure it out

as we go” approach will not serve you as well.

Using structured methodologies to identify business processes, workflow, and how data flows throughout an

organization is highly recommended. Many companies choose to utilize the services of an outside firm for

“scanning”, uncovering, and documenting the organization’s processes and data flows.

7. Educate and evangelize

As mentioned earlier, data quality problems affect the entire business. Therefore, it is imperative that all

employees who use data, do data entry, or rely on data for their jobs are aware of whatever data quality efforts

are planned or taking place, and of their responsibilities in maintaining high-quality data. Many organizations

conduct “lunch-and-learns” or “brown bag sessions” to communicate data quality initiatives and details to all

employees. Most companies utilize intranets, internal social media, and wikis to allow employees to contribute

and stay up to date on progress.


8. Commit trained personnel to data quality throughout the organization

In addition to the general employee population, there needs to be a group of people who are highly trained

in data quality and data governance to help keep data and metadata accurate. Most companies that have

undertaken data initiatives – data quality, data governance, master data management – have created the role

of “data steward” and “data custodian” and propagated these individuals throughout the business.

9. Employ a proven methodology

Whether conducting projects in-house or contracting outside consultants or service providers to assist, it is

critical to use proven methodologies to attack data quality projects. Ad hoc project management and software

implementation methods slow down project completion or cause project failure.

10. Source proven software and tools

The growing need for data quality and data governance has spawned an ever-growing number of suppliers

eager to get into the business of improving data quality. While this is terrific for competition, it does not

necessarily bode well for organizations trying to effect data quality. Data quality is an enterprise-wide business

and technology issue that demands the same level of evaluation and analysis of vendors and products as any

other technology with an enterprise-wide scale and scope. The impact of failure and the risk of project delays

or shortfalls is simply too great to allow for unproven products with little or no track record. Project leaders

should require multiple references from potential vendors and inquire about the time, cost, and resources

needed to implement the solutions – as well as the business results that were achieved.

Proven methodologies are key to the success of any data quality project.


12. Track return on investment

Data quality projects are approved and funded based on the project leader’s proposed benefits, in the form

of Return On Investment (ROI), Internal Rate of Return (IRR), and Net Present Value (NPV). As each data quality

project is completed, the project team must continue to monitor it against the goals that were set in the plan.

Each functional item, benefit, cost saving, expense reduction, and any other claim made to justify the project

should be measured against the metrics created for it. Since all functional metrics, e.g., accurate invoices,

etc., had dollar value estimates jointly created between project team, business representatives, and finance

representatives at the outset (see example for invoice accuracy, in “Document and communicate the costs

and missed opportunities created by poor data quality”), ROI and other success measures may be determined

by measuring the preordained functional metrics and applying the financial factors.

The key is to set up regular reviews (monthly to start, then quarterly) of the key metrics, convert them to dollar

values, and report the ROI results to senior management. In this manner, projects with positive ROI will become

the stepping stones to further data quality projects yielding even greater returns.

11. Use a phased roll-out schedule

Like any enterprise-wide project, a data quality improvement project will encounter obstacles and unexpected

problems along the way. In addition to selecting the best data quality projects at the start, the project manager

should also create a project plan with multiple phases and “stage-gates” between start and finish. Each “stage”

of the project should be reviewed upon completion and subject to a go/no-go approval “gate” based on

both objective and subjective criteria. The stages or phases of the rollout create the milestones for all senior

management to be briefed – or better yet, to take active roles in the stage-gate reviews and approvals.


Data quality is a business imperative that requires solid assessment, planning, and execution. With computer-

generated data permeating every area of almost every business, the need for accurate, clean data is self-evident.

Yet research shows that almost every company suffers from some magnitude of data quality problems. Estimates

of 25% to 30% of all corporate data being inaccurate are in the press and research reports. Incredible annual

cost numbers like $600 billion in wasted costs and 40% of all corporate IT have been published to describe

the size of the problem.

Methods, software, and tools have emerged to assist in the effort to create and maintain data quality in companies

of all sizes. New initiatives in customer data integration, master data management, and data governance bring

data quality to the forefront of IT discussions and to the boardrooms of organizations worldwide.

From years of data quality programs, hundreds of case studies, and research by industry experts, a number of

common success factors have emerged.

Summary


12 guidelines for success:

1. Document and communicate the costs and missed opportunities created by poor data quality

2. Prioritize by attacking a small subset of the data quality problem first and focus on a series of

small wins

3. Set attainable and measurable business objectives

4. Align business and IT objectives, expectations, and organizations

5. Confirm senior management engagement and participation

6. Identify business processes, supporting data, and data interdependencies

7. Educate and evangelize

8. Commit trained personnel to data quality throughout the organization

9. Employ a proven methodology

10. Source proven software and tools

11. Use a phased roll-out schedule

12. Track ROI

Sources1. Martin J. Eppler, Managing Information Quality: Increasing the Value of Information in Knowledge-intensive Products and Processes, 2003, p.294.

2. David Loshin, Data Quality and Cost Reduction, 2010.

3. Adapted from Steve Sarsfield, The Data Governance and Data Quality Insider blog, located at http://data-governance.blogspot.com/2009/07/data-quality-project-

selection.html


http://data-governance.blogspot.com/2009/07/data-quality-project-selection.html

http://data-governance.blogspot.com/2009/07/data-quality-project-selection.html

Innovative Systems has been providing software and consulting services to major companies in more

than 40 countries for over 45 years. We deliver both on-premises and cloud-based (SaaS) multi-domain

enterprise data management solutions that can be deployed for operational or decision support

requirements.

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12 guidelines for success in data quality projects

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