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 involvedStakeholder 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

Sources1. 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


http://dataqualitypro.com/data-quality-pro-blog/2010/6/17/data-quality-project-selection-a-missing-skill

http://dataqualitypro.com/data-quality-pro-blog/2010/6/17/data-quality-project-selection-a-missing-skill

http://searchdatamanagement.techtarget.com/news/2240020408/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.

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Justifying Your Data Quality Projects

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