Driving ROI with Master Data Management, Part II: Your First Project

ABSTRACT: Learn how to attain an optimal return on investment (ROI) with MDM by choosing the appropriate architectural strategy and evaluating progress during the initial project implementation.

While members of the United Nations speak hundreds of languages, they manage to conduct official business with just six. This proved the best way for them to streamline time, resources, and risk while keeping everyone on the same page. In a similar way, companies invest in master data management (MDM) to help different teams conduct their official business. There is no perfect formula: rather, each organization must decide how MDM can deliver the right return on investment given its situation.

Master data management (MDM) comprises practices and tools that aim for a single source of truth with consistent, trusted records for key business entities. MDM tools match and merge data from various source systems to create standard attributes and terms that describe entities such as products, customers, and partners. The resulting “golden records” strengthen data governance programs by reducing duplicates and resolving discrepancies. 

This blog, the second in a series, explores how companies can achieve the right return on investment (ROI) with MDM by selecting the right architectural approach and measuring success while executing their first project. It builds on the first blog, which helps companies prepare an overall business case for MDM. The third and final blog will recommend ways to iterate with subsequent projects based on the successes and lessons learned from the initial project.

The value of MDM 

MDM delivers value by reducing the risk, time, and resources required to process data for analytics and operational workloads. Given the inherent complexity of the endeavor, MDM projects tend to make things worse during implementation but improve them afterwards. A successful project should have the following aggregate impact over time:

MDM delivers value by reducing the risk, time, and resources required to process data for analytics and operational workloads.

As with any technology project, messy details tend to get in the way and threaten ROI. Let’s consider how data teams can meet or exceed ROI goals by selecting the right architectural approach and measuring the right key performance indicators. (To understand the MDM implementation process in detail, also check out the Rapid Delivery Blueprint that Semarchy wrote based on more than 100 projects over a decade.)

Architectural approach

The architectural approach has a big impact on data management processes and therefore the ROI of an MDM project. The data engineer should work with business owners, data consumers and the IT/CloudOps engineer to evaluate how each approach can streamline risk, time, and resources to support their company’s data environment and priorities.

The primary approaches are registry, consolidation, coexistence, and centralization. Here is a summary of these approaches, which Semarchy also explores in more detail in a recent blog.

  • A registry indexes master data so business units can periodically revise their records to stay in sync. The registry helps independent teams control their own data while still adhering to common standards. It suits decentralized organizations with autonomous business units that don’t want to rewrite their processes.

  • A consolidated hub copies, matches, and merges records from the business units to create golden records. Business users, data stewards, or applications within the business units then take those golden records and incorporate them into their processes. This architectural approach governs data centrally, helping compliance-sensitive organizations reduce regulatory risk.

  • Centralization takes a similar approach as a consolidated hub, but goes further to maintain strict control of golden record consumption. Users and applications must integrate their analytical and operational processes with the centralized hub, which can cost significant time and resources. It best suits compliance-sensitive organizations that prioritize risk reduction over time and resource reduction.

  • Coexistence also is similar to a consolidated hub, but adds the ability for business units to create golden records themselves. While this approach offers lots of flexibility, it can reduce efficiency because data stewards and engineers must synchronize master data both ways—and spend time deciding who creates different golden records. This approach suits organizations that need flexibility and have limited overlap among their business records.

Data leaders should assess each of these architectural approaches by estimating its aggregate impact on risk, time, and resource requirements during and after implementation. By answering this question as it applies to their environment and priorities, data leaders can select the right approach to achieve their target ROI. They also should select an MDM platform that supports most or all approaches to stay flexible and adapt to changing requirements over time.

Data leaders should assess each of these architectural approaches by estimating its aggregate impact on risk, time, and resource requirements.

Key performance indicators

Once they decide on their architectural approach, data teams must measure project performance during and after implementation. They can do this by tracking eight key performance indicators (KPIs): data quality, project execution time, project cost, process impact, user adoption, team productivity, customer satisfaction, and compliance standing. They must track these KPIs—including both quantitative and qualitative metrics—for periodic reporting back to the business owner and executive sponsor. 

  • Data quality. Data quality lies at the heart of master data management. Data stewards should monitor the accuracy, consistency, and timeliness of the records that business users consume and intervene to resolve errors. 

  • Execution time. Project managers should ensure data engineers, data stewards, and other stakeholders meet their deadlines. When things slip, they should assess the impact on project completion dates as well as cost and customer satisfaction.

  • Project cost. MDM software and staff implementation time incur both direct and indirect project costs. The project manager should measure these costs and re-balance resources when costs exceed targets.

  • Process impact. Project managers and business owners must track the impact of MDM changes to analytical and/or operational processes. Are data analysts and scientists able to generate reports and build AI/models as planned? Do sales and finance have timely access to accurate customer records? 

  • User adoption. Many MDM projects require voluntary user participation. For example, decentralized business teams might need to synchronize their records with a central registry. Project managers and data stewards should measure the number of MDM participants and the timeliness of their updates.

  • Productivity. Team productivity boils down to the amount of work completed per worker hour. Most organizations adopt MDM to increase this metric in the long run, so must keep a close eye on the results during and after implementation. Can a given team support new initiatives without adding staff? Does it spend less time fixing erroneous records and fighting fires?

  • Customer satisfaction. Incorrect sales and support records wreak havoc on customer satisfaction (CSAT). Project managers and business owners must measure CSAT via surveys or focus groups both during and after MDM implementations to ensure customers remain happy.

  • Compliance standing. An MDM project must maintain or improve the ability of an organization to comply with internal and external policies for data governance. Data teams must satisfy auditors that they are controlling data access and meeting the patchwork of regulations related to personally identifiable information. 

A final measure of success, of course, is the risk that one or more of these KPIs fall short of expectations during and after implementation. As discussed in our earlier blog, MDM project managers and business owners must keep a vigilant eye on such risks. Do quality errors, process delays, or regulatory infractions seem more or less likely than before? Are stakeholders becoming or less confident in project outcomes? Teams must ask themselves these questions and communicate definitive answers to the business owner. The executive sponsor also will want periodic risk assessments.

Completing project one

Any of the four architectural approaches to MDM—registry, consolidation, coexistence, and centralization—can deliver the intended results, depending on an organization’s priorities and environment. Data teams must make a careful selection, then measure the results before and after implementation, to deliver the intended ROI. As we know, projects never go quite as planned. Our next blog concludes this series by recommending ways to learn from the surprises, failures, and successes of the first MDM project. Data teams can use these lessons to iterate and deliver better ROI on subsequent projects.

Kevin Petrie

Kevin is the VP of Research at Eckerson Group, where he manages the research agenda and writes about topics such as data integration, data observability, machine learning, and cloud data...

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