The ROI of Data Governance: Seven Ways Your Data Governance Program Can Help You Save Money

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1 A DataFlux White Paper Prepared by: Gwen Thomas The ROI of Data Governance: Seven Ways Your Data Governance Program Can Help You Save Money Leader in Data Quality and Data Integration FLUX International +44 (0)

2 In difficult economic climates, responsible leaders look for opportunities to contain costs. Data governance programs are well positioned to assist in these efforts. This paper outlines seven ways that data governance & stewardship programs can help manage costs and a mechanism for quantifying the return on investment (ROI) for those contributions. Why Data Governance Can Help Data governance programs come in many flavors. Some concentrate on supporting compliance, security, and access to data. Some focus on supporting data quality. Others focus on enabling data integration, increasing the value of information assets, and enabling transformative efforts. Data governance programs may be tightly focused on a single repository, set of data, or business problem. Or, they may cast a wide net, seeking to implement standardization, auditability, and appropriate decision-making across an enterprise. Regardless of the flavor of data governance, all programs eventually address certain activities: Defining/aligning policies, standards, and rules Establishing decision rights Setting data-related accountabilities Providing mechanisms for issue escalation/resolution Identifying data stakeholders and understanding their needs Communicating with data stakeholders These are cross-functional activities, undertaken by representatives from across the enterprise. As a result, data governance programs and participants develop unique capabilities. They become aware of gaps and overlaps in management efforts They learn about software products and how they can be leveraged to achieve multiple goals They observe proactive, reactive, and ongoing processes They see where money is being spent, and they often see where costs could be trimmed Following are seven value propositions for data governance. If even one applies to your organization, you may find that data governance can pay for itself simply by avoiding other costs. If more than one of these value propositions applies to your situation, you may find that your budget for data governance is some of the smartest money you re spending these days. 1

3 Value Proposition #1: Data Governance Efforts Can Help Avoid Unnecessary Technology Work At many organizations, there is clear ownership of technology; it may be relatively easy to discover who can answer questions about whether a particular software application can accomplish a desired goal. But this may not be true of the services, data feeds, queries, and other mechanisms that move data from repositories to other repositories, applications, portals, and reports. As a result, a lot of duplicate work takes place. Many data governance programs task their data stewards with understanding where data resides, where it moves, and how it is used. As a result, unnecessary work can be avoided. Consider this example from a large financial organization. A marketing manager planned a technology project to enable a better reporting process. She briefly described her plans to the enterprise data stewardship team. One of the stewards, it turned out, used similar information that came from an entirely different source one that the marketing manager was unaware of. During their discussion, it became clear that the planned solution wasn t needed at all, and by using different data sourcing, the manager could get what she needed, and get it months earlier than she expected. The stewardship team was so impressed by what happened that they sent word to senior leadership stating that the data governance program had paid for itself for an entire year, based on that single discovery. Value Proposition #2: Data Governance Can Avoid the Costs of Duplicate Controls When you operate in a compliance environment, it s no longer enough to simply do technology work. You have to do it, control it, document it, and prove compliance. This new paradigm has lead to burgeoning costs associated with controls. AMR Research estimated 2008 costs for governance, risk management and compliance to top $32 billion.1 Technology managers may need help to avoid introducing unnecessary costs. After all, managers who are experienced in overseeing the "doing" of data management work may not be experienced in working with controls, documentation, or auditor-interaction activities. It doesn t make sense to ask managers to individually and independently research and understand the types of controls that are best used to adhere to compliance requirements. What does make sense is for your data governance team to help compliance and technology managers look for cost savings. Because data governance teams represent multiple perspectives, they may be in a position to see what others cannot. Consider these real-life examples: 1 AMR Research. The Governance, Risk Management, and Compliance Spending Report, : Inside the $32B GRC Market. March 25,

4 A Sarbanes-Oxley manager requested a costly set of tests to prove that financial data was migrated from one repository to another according to certain specifications. This request was reviewed by a data governance team that was able to suggest modifications to regularly-scheduled systems integration work. The introduced controls provided the needed proof of compliance at a fraction of the cost of a separate set of tests. A compliance department was about to begin a formal access management program that would comply with multiple federal regulations. A review by the new data governance team revealed that most of the components of the program were already in place, operating under the label of best practices rather than controls. The formal effort was completed in a fraction of the anticipated costs. An audit preparation team submitted a requirement that an IT team develop records to prove that a series of processes took place. They were surprised to discover that in-place extract, transform, load (ETL) and data quality tools included the records they needed. Data governance teams are typically multilingual in that they speak the language of risk management, compliance, business, security, and technology. They can help translate requirements and look for existing solutions to new needs. Because they tend to work closely with data management and information quality teams, they are likely to be aware of the controls capabilities of your data quality, ETL, and master data management (MDM) software. Value Proposition #3: Data Governance Can Help Avoid Project Delays Here s a common scenario: During the course of a technology project, a data architect or data modeler receives a request to create or modify a database or other data repository. The architect studies the request, and then realizes the change might cause a problem for another application or set of reports. In good conscience, the architect cannot proceed. What happens next? Without data governance, this becomes a problem to be resolved by the project manager, who must understand and frame the problem, identify data stakeholders, pull together representatives to analyze the problem and suggest solutions, and then sell this new approach to project sponsors. Frequently, project delays result. On the other hand, organizations with data governance cultures in place have probably already identified data stakeholders. They ve already identified data stewards or others to represent constituencies when making decisions with cross-functional implications. They ve already settled on decision-making and issue resolution processes. They have strong working relationships with key data stakeholders and the technology/architecture staff with knowledge of key systems. The project manager may be able to hand the issue over to the data governance team for analysis, to be addressed in parallel with other project tasks. Even better, if architecture and governance staffs have been given the opportunity to review plans in the project s early phases, they may have been able to address concerns before the project plan was impacted at all. 3

5 Value Proposition #4: Embedded Data Governance Can Prevent Costly Data Issues Some data governance programs initially focus on reacting to data issues. Most mature programs, however, give at least some of their attention to preventing them. This can happen in several ways. Embedded data governance controls. The data governance team may work with information quality staff to profile data, analyzing the contents of databases and identifying common deficiencies. The team can then work with business and technology teams to develop controls to prevent the problems and embed them into systems and processes. Early identification of risk. A common complaint of those who work with data is that by the time they are in a position to notice issues, a project has progressed to the point that addressing the issue will result in unplanned expenses. To avoid this, many organizations have embedded a new task early in their system development lifecycles (SDLCs), in which data governance teams review project plans to identify data-related risks. Introducing multi-stakeholder perspectives. It is a common truth in large organizations that we don t know what we don t know. The designers of an operational system, for instance, may not be in a position to know how the information they work with will be used by others in the organization. One of the key benefits of data governance programs, however, is that they assemble data stakeholders from across the enterprise. Many organizations have embedded data governance reviews in their IT portfolio management process. In these reviews, data governance or stewardship teams review plans for major projects. The goal is to identify potential problems for downstream users of data so that their needs can be addressed and costly rework can be avoided. Value Proposition #5: Data Governance Can Prevent Costs of Non-Compliance Compliance may take many forms: adherence to legal and regulatory requirements, contractual compliance, and adherence to standards and other requirements set internally or by partners or industry groups. Likewise, the costs of non-compliance can take many forms: governmental fines; penalties paid to partners, suppliers, and customers; costs of notifying customers and stakeholders; the value of lost customers; and even higher auditing costs associated with loss of confidence by auditors. Data governance programs are well positioned to support data-related compliance efforts. Because they already have cross-functional teams in place, they can look for opportunities to drive cost out of compliance efforts. They can align requirements and rules, ensure that data-related controls don't negate each other, and suggest the use of 4

6 existing controls to meet compliance requirements. Data governance teams can recommend approaches to employing data stewards and others to support compliance in a cost-effective manner. Consider the case of a multi-national energy company that directed their data governance and quality team to profile its corporate location master data. Within the first week, they discovered errors in mapping work to the place in which it was conducted issues that had significant tax ramifications. Because they were able to correct these issues, they avoided paying taxes they didn t owe, and they avoided the penalties that would have been assessed had they missed legitimate payments. Value Proposition #6: Data Governance Can Help Contain Outsourcing Costs Outsourcing can be a great way to pay less for services and products. But it doesn t always work out that way. Savings can be countered by unexpected costs if data provided by outsourcers does not meet expected standards. Savvy companies involve their data governance teams in setting the rules for outsourcers. These teams provide policies, standards, and business rules to the outsourcer s technical teams. They help establish decision rights for data-related decisions that will impact multiple stakeholders. They help set accountabilities and communication flows to ensure that internal stakeholders and users of information will be made aware of changes to the architecture or even values of reference data and master data. They establish the rules of engagement for resolving data-related issues. They look at how information will be delivered to the outsourcer and then fed back to the company, identifying ways that quality errors could be introduced, standards overlooked, or integrity compromised. Applying data governance principles to outsourced efforts as well as internal efforts helps ensure that the information that flows through your outsourcers systems will meet your standards. It s a low-cost way to avoid costly mistakes. Value Proposition #7: Data Governance Can Help Save Money During Mergers and Acquisitions Due diligence is a part of any merger or acquisition. Teams are sent in to look at finances, inventory assets, and look for opportunities to save costs through synergies. These due diligence efforts often include an inventory of key IT systems. However, it is rare for this examination to include a careful review of information assets. As a result, the acquiring company is at risk of incurring unplanned costs during the merging of corporate data stores. Why is this? Suppose Company A stores customer information in one format, and Company B stores it in a different format. The information will have to undergo a harmonization process before it can be combined. What if one system relies on information that is simply not collected in the other? Bringing the two sets of information into compatible states may be costly. 5

7 A data governance advance team, working with the other due diligence resources, can examine key systems to uncover architectural issues and incompatible standards. They can perform data profiling of key information (customers, inventory, etc.) to determine the quality of information assets. (Alternately, the data governance team can interpret the work of an independent party brought in to perform data profiling.) This data profiling can be important to containing costs. Often, the price of the company is based in part on the number of customers that will be acquired. What if the information you re acquiring contains duplicate records? Are you paying for both of them? What if the two companies have customers in common? Will this affect the price? What if the two organizations use different business rules to define a customer? With a data governance team to interpret these rules and establish an accurate count, your negotiators will have information they need to avoid paying too much for assets. Establishing the Return on Investment for Data Governance Contributions Are data governance teams focused on containing costs? Sometimes. Data governance council meetings may not have an agenda item called Identify Cost Reductions. Many of the examples in this paper were serendipitous. In the course of doing data governance, opportunities to contain costs presented themselves, and the data governance team seized the opportunity. On the other hand, data governance reviews and controls are embedded into processes and systems specifically to prevent costly issues or to detect problems before they become issues. Some data governance contributions, such as participating merger and acquisition efforts, take place as a larger effort to manage risk and contain costs. Often, it s not necessary to measure the contribution of these data governance contributions. Their value is clear. Other times, however, you may want to quantify contributions. If so, you may want to use an ROI metric to measure data governance efforts. This is a common metric, used to decide whether expenditures are worth the cost. ROI follows a simple formula, as shown in Figure 1: Start with a benefit, which is usually revenues gained or costs avoided. Then subtract the cost of achieving that benefit, and divide the result by the cost of the benefit. ROI is expressed as a percentage, so multiply the results by 100%. 6

8 ROI = ( Total Benefit Cost of ) - Benefit 100% X Cost of Benefit ( ) Figure 1: The formula for ROI. Consider the example of the costly compliance tests that were avoided in one of the examples above. Those tests would have cost about $20,000 the total benefit. The cost of the alternative effort was about $4,000, for a savings of $16,000. Divide this by the cost of achieving this benefit, and you end up with an impressive ROI of 400%. Using the simple ROI metric to measure the contribution of data governance efforts can be challenging, however. Why? These efforts are sometimes two or more degrees of separation from actual hard-dollar benefits. If you want to calculate ROI for such efforts, you ll need to use a modified ROI formula. Degrees of separation from the ultimate benefit In the example above, the effort was just one degree of separation from money. Typically, simple one degree of separation calculations are what we see when ROI metrics are published. Should we acquire Company XYZ? Let s see: Here s the cost, and here s the benefit, and here s our final ROI. However, simple ROI calculations do not represent the complicated efforts that may be part of the overall decision. For example, consider the due diligence effort that takes place as part of the acquisition. This due diligence effort is two degrees of separation from the ultimate benefit. It contributes to the acquisition, by helping understand the potential benefit, negotiating costs, or understanding risk. Now consider a data governance contribution: reviewing data standards and profiling customer data. This effort contributes to the results of the due diligence study; it is three degrees of separation from the ultimate benefit. If you re looking at your data governance program to help contain costs, you should know that their contributions will almost always be two or three degrees of separation from the ultimate benefit. Organizations rarely look to measure hard dollar returns on these types of efforts. Still, if it s important to do so, you can measure a data governance contribution and compute the ROI for that contribution. What you need are three numbers: 1. The total benefit the costs that are being avoided (or revenues gained). 2. The percentage of credit that data governance would be given for avoiding these costs. If this cost is a certainty without the data governance contribution, then this figure will be 100%. If several efforts will go into avoiding this expense, then data governance should be allocated a smaller percentage. 3. The costs of the data governance contribution. 7

9 Now you can plug those figures into a modified ROI formula, depicted in Figure 2: ROI of = DGov ( Percentage ) Total of benefit Benefit X contributed - 100% X by DGov Cost of DGov contribution Figure 2: The ROI of data governance. ( ) Cost of DGov contribution Let s look again at the example of the costly compliance tests that were avoided. The benefit was $16,000: $20,000 saved minus $4,000 spent on an alternative solution. Data governance gets 100% of the credit for this benefit, since the original plan would have gone forward without the intervention of the data governance team member. This team member spent just over an hour negotiating this alternative approach with participants. Rounding the cost of the data governance contribution up to $200, we get an amazing ROI of 8,000%! More importantly, we realize that in one hour, this member earned over two months salary two months in which she continued to concentrate on finding opportunities to contain costs. Over the course of a year, this bank went through several reductions in force. Nonessential projects were cut, and programs were downsized. The bank s data governance program increased in size, however. Why? Senior executives recognized that this crossfunctional program was not only paying for itself, but contributing to ongoing efforts to increase revenues, ensure compliance, and contain costs. 8

10 Conclusion Data governance programs may focus on many types of efforts: enabling new capabilities, ensuring data quality, or bringing the standardization needed for SOA, data integration, and business process improvement. Data governance programs emphasis may be on increasing revenues and the value of information assets, on ensuring compliance, or on containing costs. Regardless of the focus and scope of data governance and stewardship efforts, their cross-functional nature means that multiple perspectives from across the organization can be brought to the work of saving money. Data governance efforts can help: Avoid unnecessary technology work Avoid the costs of duplicate controls Avoid costly project delays Prevent costly data issues Prevent costs of non-compliance Contain outsourcing costs Save money during mergers and acquisitions Some organizations feel that the value of data governance contributions are so obvious that monetary calculations are not necessary. Others decide to apply ROI formulas for some contributions. Regardless, organizations with successful data governance programs find that they bring significant benefits, and that data governors and stewards can be important resources in efforts to avoid unnecessary cost. 9

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