Justifying Your Data Quality Projects

Justifying Your
Data Quality Projects

Data quality has become paramount to the success of any business that relies on data and
information for its everyday processes. From manufacturing facilities to financial organizations,
data are what make business processes move. Data quality is involved from order entries to
delivery and from transactions to customer contact. If data are not reliable, the processes a
business depends on are not reliable, either. When data are not of high quality and integrity,
the lack of quality begins to show itself in other parts of the business, whether it be in the loss of
orders, incorrect transactions, or the loss of customers and the ultimate failing of the business in a
worst-case scenario.
Getting a data quality project started will require approval from executives and funding for the
initiative. There are several ways to approach the justification process. This paper presents
some of the most effective tactics used to justify a data quality initiative, present a strong business
case, and get approvals from senior executives.
Introduction
When data are not of high quality and integrity,
it begins to show itself in other parts of the business.
2 JUSTIFYING YOUR DATA QUALITY PROJECTS

When a data quality project is in order, a good
first step in obtaining support from leadership
is to show them what affects data quality. As
part of your justification, start by listing the
missed business opportunities that were a
result of bad data.
Of course, the most common source of poor
data quality is human error. Data entry can
be a repetitive, grinding task, and often
yields transposed digits, typographical errors,
misspellings, and just plain mistakes in entry.
Another common type of data inaccuracy is
having data entered into the wrong field of
a form. A user may enter an invoice number
into a field designated for a second address
Demonstrate to leadership
what affects data quality
Listing the critical factors that affect
data quality will help shine a light on
missed business opportunities.
line, or a warehouse employee may enter a
waybill number into a telephone number field
accidentally.
Yet another source of bad data is the omission
of data altogether. As data flows through
systems and departments, some systems
expect to find data in certain fields. The
omission of data can lead to incorrect default
data, or a system assuming that no entry
equals “zero”, for example. Creating a list of
the factors that affect data quality will allow you
to demonstrate the scale and scope of the data
quality initiative to the senior management who
are responsible for funding it.

Another tactic for gaining approval is to pick one critical business process or system that has a
company-wide scope, significant impact on company performance, and easily understood data
descriptions. Presenting this subset of the enterprise data quality problem as a representative sample
– or even as an initial first project – is likely to win over executives more easily than presenting the
details of the entire organization’s data quality challenges. For many companies, this means targeting
the Customer Relationship Management (CRM) system.
CRM systems are good targets for
demonstrating scope and impact

Data quality issues in the CRM
system can have a huge impact on
your business, including loss of
customers and loss of revenue.
Because of the importance and wide-ranging impact of CRM systems on companies, they are logical
targets for identifying the highest potential ROI of data quality. CRM systems are typically the central
repository of data related to:
• Customer contacts
• Customer support history
• Sales forecasts
• Sales prospects
• Sales quotations
• Product descriptions and
product identifiers
• Pricing
• Contracts
As a result, the CRM system is a logical place to look for data quality issues. The impacts are potentially
huge, e.g., loss of customers, loss of revenue, the potential for errors is enormous (since CRM has
many people creating, entering, changing, and reporting on data), and senior executives are familiar
with the importance of accuracy in a CRM system.

One leading research firm found that the average cost per company for poor data quality was
over $8 million per year. Losing $8M annually due to poor data quality should be enough to catch
the attention of most executives, but ultimately, approval of a data quality initiative will depend upon
showing a positive return on investment (ROI) from the effort.
Demonstrate the return on investment
of the data quality initiative

Any proposal to start a data quality program and make the necessary investments should present clear
examples of how improved data quality will contribute to any or all of those three drivers.
Return on Investment is expressed as a percentage that describes how much a given investment in tools,
services, and time will pay back to the investor. The formula for return on investment is:
(Value of Benefits – Investment) / Investment
where “Value of Benefits” is your estimated total of all dollars earned, saved, and/or avoided as a direct
result of the proposed data quality investment, and “Investment” is the total cost of doing the project.
Therefore, to calculate ROI, you’ll need to determine all investment costs (software, services, labor) and all
expected benefits (which will primarily be cost reduction, elimination, and avoidance, but may also include
some estimate of improved revenues from better decision-making, business agility, and happier customers).
Some of the benefits and costs will need to be estimated. It is useful to use a priority ranking system to
estimate costs where necessary. For example, certain databases are more data quality critical than others
and carry both higher risks (costs) and greater impacts. Conducting an inventory of systems and rating
them based on the need for high quality data will allow you to more accurately estimate value of accurate
data or cost of inaccurate data.
1. Grow revenues (through better business
agility and responsiveness)
2. Reduce costs associated with poor data
quality
3. Reduce risks and comply with regulations
There are three ways for data
quality to contribute positive ROI:

Enterprise-wide data quality requires significant investment in time, money, and human resources.
However, in any typical company, those investments will produce a positive return as the costs of poor data
quality are reduced and mitigated. Therefore, the return on investment is primarily delivered through the
reduction in costs associated with poor data quality.
Therefore, the single most persuasive item in a data quality justification is the presentation of the costs that
will be eliminated, reduced, or avoided by implementing a data quality program.
The “Cost of Poor Data Quality” can be expressed as:
Cost of Preventing Errors + Cost to Correct Errors
+ Cost to Resolve Customer Issues + Lost Business
Researchers estimate that the cost of poor data quality can account for as much as 10% to 25% of a
company’s revenues. Presenting executives with this kind of information along with the specific cost items
that have been identified can help persuade them to invest in projects to alleviate data quality problems.
The real cost of poor data quality
Poor data quality can account
for as much as 10% – 25% of a
company’s revenues.

Some specific areas of cost to try to quantify for your justification include:
• Lost time spent in cleaning up files
• Cost associated with higher-wage workers correcting data problems that
should be addressed by lower-paid data workers
• Postage for returned and re-sent mail
• Wasted production costs for inaccurate mail addressing
• Increased help desk and customer service costs
• Costs of incorrect billing
• Incorrect shipping
• Inaccurate invoices
• The need to perform frequent verification of figures in reports
• Re-coding of computer applications and database fixes
• Costs of multiple copies of the same data (often due to departmental or indi-
vidual “hoarding” data because of mistrust of other data sources)
• Fines and jail terms due to serious compliance issues resulting from inaccu-
rate reporting
Finally, the most important cost of poor data to present is the cost of lost customers. Incorrect
customer data can cause customers to lose faith or trust in an organization. Incorrect invoices,
obsolete addresses, and inaccurate customer support histories can cause customers to become
skeptical of everything they receive from the company. These frustrations will eventually cause
customers to leave, causing a ripple effect that is far worse than just the lost revenue. Poor data
erodes our ability to identify the comprehensive purchase history of each customer, therefore making
purchase power predictions and sales forecasts more difficult.

The other half of the ROI equation is, of course, the cost of investing in systems, software, tools,
services, and labor to undertake the data quality initiative.
To calculate the required investments to propose for approval, you’ll need to address all of the
components of investment cost.
Information Technology costs:
• Software
• Hardware
• Network
Labor costs for the initiative
• Program leader
• Departmental contributions
• IT labor
Outside service costs
• Consultants
• Service providers
• Systems integrators
Investment costs
With estimated investment costs and forecasted financial benefits in hand, the ROI becomes the
central justification for moving ahead with approval for the initiative.

Getting stakeholders involved
Stakeholder involvement is imperative to justify the investment in an organization’s data quality. Getting
leaders and representatives from the business involved will help propagate the message that data quality is
important and action must be taken. Those who are spearheading the data quality investment project must
show what the current issues surrounding data quality are costing the business, how much a solution to
improve data quality would cost, and what the ROI would be in implementing the new, higher quality system.
Stakeholders become involved when they understand exactly where the data quality issues in their own
departments lie. Find out what business processes are most important to a business unit or business user,
and relate the information, people, systems, and data to those processes. Ask them to consider what would
happen if the data they use is unavailable, inaccurate, or out of date. Who has the largest risk if that data
is jeopardized? By answering these questions, stakeholders will be able to provide strong support for an
initiative to improve data quality.
Stakeholders become involved when they understand exactly
where the data quality issues in their own departments lie.

Explaining the various ways to approach a data quality initiative can help to gain buy-in from executives.
Senior management is more likely to approve expenditures proposed by people they believe are fully
competent and have considered all risks. One simple way to gain the confidence of the executive staff
is to present alternative approaches.
Data quality project leaders can use a top-down approach, which is a method of choosing projects
that will match up with strategic priorities. This approach involves obtaining approval from executive
leadership and carries greater risk than taking a more local-level approach, but overall improvement
and ROI can be greater in the long run.1
The other method is the bottom-up approach. This approach takes on data quality initiatives by starting
at a departmental or business process level instead of on a large scale. This is more of a quick solution
for a specific problem, but does not solve the problem as a whole.2
The bottom-up approach is typically
used when the data quality team needs to start small, show positive results, and move forward in
stages.
Show alternatives for starting
data quality initiatives
A top-down approach to data
quality projects can yield better
overall results.

Before seeking executive approval, it is best to
first determine the source(s) of funding.
In addition the ROI and business case, a decision to invest in data quality also requires a source of
funds for the investments. Significant investments such as data quality tools and resources will require
budgetary approval. It is best to determine the source(s) of funding before seeking executive approval, if
possible and practical.
Request support from the finance department in the earliest stages of the project. Investigate availability
of funding, and how it might be allocated to data quality improvement. The finance team can then
work with the executive leadership to show where there may be money available for a data quality
investment.3
As we said earlier, determine which executive level business leader is most affected
by poor data quality. This person may have budgetary authority within his or her own department for
investments in improving operations or decreasing costs. Finding this person, making a business
case within his scope, and finding funds under his authority will allow you to approach the top-level
executives for enterprise-wide approval with the source of investment funds already identified.
Look for funding
before seeking approval

Data quality needs to be made a priority in any business that relies on data for its success, but
achieving acceptable data quality requires investments in time, technology, and labor. Justifying a
data quality initiative requires creating a business case, identifying stakeholders, finding funding, and
requesting approval from executives.
The path to approval requires an economic analysis, presented as a Return on Investment case
backed up by careful, thorough investigation into the total cost of the proposed data quality initiative
and the financial benefits the company will enjoy as the result of a successful data quality program.
The “return” half of the ROI equation must be built upon three keys: (1) increasing revenues, (2)
decreasing costs, and (3) decreasing risk. Meanwhile, the “investment” half of the equation must take
into account all costs associated with labor, capital and expense associated with data quality tools and
systems, and all outside services required.
Summary
Tactics for getting your data
quality initiative approved:
• Demonstrating to business leadership the effects of poor
data quality
• Calculating the ROI of proposed projects
• Working with stakeholders to create the business case
• Finding people who can help with project justification
• Finding funding sources before requesting approval
Sources
1. Dylan Jones, Data Quality Project Selection-A Missing Skill?
2. Dylan Jones, Data Quality Project Selection-A Missing Skill?
3. Mark Brunelli, Execute Data Quality Improvement Projects with Senior-level Thinking

Innovative Systems has been providing software and consulting services to major companies in
more than 40 countries for more than 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. Our world headquarters is in Pittsburgh, PA, and our EMEA / APAC
headquarters is in London.
World Headquarters
790 Holiday Drive
Pittsburgh, PA 15220-8127 US
Phone: 800.622.6390
International Call: +1.412.937.9300
E-mail: info@innovativesystems.com
About Innovative Systems, Inc.
www.innovativesystems.com
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Phone: +44 (0) 20 7422 6310
E-mail: info@innovativesystems.com

Justifying Your Data Quality Projects

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