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Master data management: Seven levers that will make a difference to the CEO

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Introduction: The transformative role of enterprise-wide master data management

Many organizations have recognized the transformative importance of a strong master data management (MDM) foundation in the era of technology-enabled operations and analytics. The reality, however, is that many struggle with the complexity of design, build, and ultimately operationalization of an enterprise-wide MDM program.

To implement a successful solution, organizations need a strong operating model (not an implementation effort) that builds the MDM muscle in the company, and a robust method to prioritize scope, technology, process, and people choices, all of which must be embedded in a solid governance structure. To achieve advanced MDM practices, we have identified the seven MDM levers that every CEO should have their teams adopt.

Why MDM matters to CEOs and their teams

Master data is the information associated with millions of transactions and relates to vendors, materials, employees, services, etc. It is the foundation for transactional integrity, analysis, and compliance across multiple functions. The table (in PDF) shows the difference that strong MDM capabilities can make to the key roles in an organization by comparing the ideal world with the real world experienced by most enterprises.

It is no wonder that many executives see MDM as a significant opportunity, but also a struggle. In a recent study, MDM came out as a major component of the CFO, CPO, and other groups’ quest for a solution to their key challenges, from compliance to cost control. Most of those organizations, however, admitted to comparatively low levels of preparedness to evolve their master data functions, as well as relatively low levels of maturity of MDM practices compared with other strategic enterprise processes. The chart in Figure 1 (in PDF) explains these findings.

Generally, financial services companies started earlier than their industrial counterparts, and have a higher level of maturity. However, the challenges related to risk and compliance that financial services companies face have also become more complex. Among companies with significant physical supply chains, some industries have had to tackle the problem first, such as pharmaceuticals, where both cost and compliance issues have created significant burdens. Even in industries like life sciences or consumer packaged goods (CPG), only a few companies, like Unilever and P&G, have matured their MDM operating models. Other sectors, like petrochemicals and oil & gas, have recently been hit with significant macro conditions (such as very volatile commodity prices), and while some were already fairly advanced in this area, the rest have no choice but to focus on MDM.

Thanks to the experience gained from mature MDM efforts and a more granular understanding of the impact of master data on the effectiveness and efficiency of business process operations, it is now possible to achieve robust results in less time, with lower cost and more limited risks. Our experience shows that focusing on seven distinct levers is a powerful approach to the evolution of MDM.

Lever 1: Start with the end in mind

Depending on the company, the benefits that MDM can bring may vary. Certainly, the cost of MDM can be compressed across the enterprise, but that is typically not the biggest value. Table 1 shows the real strategic objectives for the C-suite. The value to some of those benefits is often not easy to pinpoint, and operations groups can be less skilled at building sophisticated business cases compared to their M&A counterparts, for example, but the effort is typically well-rewarded.

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Identifying the MDM must-haves is invaluable for securing support across the organization and during the build period. It also creates an internal yardstick for comparing the actual to the planned results. This effort proves invaluable when prioritizing practices and technology deployments because it helps the implementation teams to align the tools they have at their disposal with the expected goals, and execute trade-offs effectively. For example, limited resources can be more precisely allocated towards specific MDM areas (e.g., technical masters or specific business units) depending on the estimated value of an optimal outcome.

Broadly, companies focus their effort on cost reduction and compliance (and prioritize data domains relevant to procurement and finance, e.g., vendor, material or cost center) or revenue (and prioritize customer master data). There are cases, however, where these priorities are overridden by the finance organizations that require improvement to all data domains for the purposes of making management reporting faster. Companies that follow this path take much longer to achieve their business goals compared to the companies that are able to align data domains to their priorities. It is important for finance to balance record/report priorities (and related granularity of data) with other significant enterprise priorities.

At an industry level, strategic objectives can vary significantly. For example, commercial operations in pharmaceutical companies focus on customer master data, which generates the maximum savings through effective marketing. CPG companies focus on product data management to improve their capabilities to publish to a global data synchronization network, which enables trading partners to globally share trusted product data and ensure reduced penalties from incorrect product dimensions passed on to the retailer.

In sum, identifying the strategic objectives and business outcomes is the first step, which should trump any experiential MDM approach. The related conversation across stakeholders is an important part of the strategy and should be held formally and in a structured way.

Once this (iterative) step has provided the "true north" only then can the next step – scoping the effort – start.

Lever 2: Focus on the right scope

It is very common for large, technology-driven efforts to get into trouble because of an overly ambitious scope. In the case of MDM, not all data is born equal, and the MDM plan must reflect that. Data fields, systems of record, interfaces, business units, plants, and users vary in terms of the materiality of the impact they have on key business outcomes. For example, many fields are only relevant at local levels, and have limited value for the global organization, and shouldn’t necessarily be the object of an MDM effort.

A practical approach to determine the scope leverages the concept of context versus content. For context, it is important to create and define data in a consistent way for use across the company. Content, however, may be customized at the local level. For example, the context of VAT should be the same across Europe, but the content field will differ in size and format from country to country. While it remains relevant at local levels, it has limited value for the global CFO. Similarly, when maintaining vendor codes, having a parent–child hierarchy at a master level is good practice, but maintaining supplier site information, especially for a global supplier, can be done in the transaction system at the regional/local level.

For instance, a large pharmaceutical organization understood that it would achieve maximum business impact by focusing on procurement, in particular on spend analysis and supplier qualification/pre-qualification. Hence, it focused on underlying masters like vendor and material data.

Identifying the right scope, however, is not just a process consideration. Human behavior change is often the most complex part of a transition, but many companies fail to segment their users into groups based on the importance of their behavior on the desired business outcomes. Continuing to chase individuals in a fairly indiscriminate fashion can result in cost overruns, delays, ineffective change management processes, and potential wholesale failures because of the reaction that such programs may engender if not managed sensitively. Careful scoping of the stakeholders significantly increases the odds of success.

Lever 3: Design for operationalization

Most companies follow a traditional approach for designing, building, and operating MDM processes. There is undoubted value in using the right resources for each stage, e.g., ensuring that change management experts are brought into the fold when needed, and operations experts are tapped into when starting production and scaling the initiative. Very often, however, we see that the steps generated by design phases aren’t granular enough for implementation, or assume systems or people performance that is unrealistic. Also, the value of the experience in running MDM operations as part of business processes cannot be overstated, as it creates the foundation for feedback loops that pinpoint what works and what doesn't, and informs the next design efforts. Finally, by designing for operationalization, companies can reap early, low-hanging fruit without the need to wait for the complete design and build phase.

Operationalization can start by addressing the quick wins that generate fast returns and bolster the internal credibility of the effort, such as:

  • Build a pragmatic catalog taxonomy to reduce free-text fields
  • Data dictionary standardization and correction
  • Gradually enhance existing process to ensure quick benefits.

In this way, companies don’t have to wait for a full-blown MDM program to get stabilized to increase their spend visibility and go after incremental savings. These companies can achieve good spend visibility by adopting the right material taxonomy, driving catalogue and purchase order (PO) penetration, and creating a proactive mechanism to convert, wherever possible, supplier contracts into useable catalogs.

The second attribute of building for operationalization is the careful crafting of feedback loops across the company: key attributes in vendor master data include contact details, site details, and parent–child hierarchies needed to run the right vendor risk assessment processes, as well as key attributes in materials, such as hazardous material categorization to ensure the right downstream material risk management and spend analysis. These connections enable companies to focus efforts upstream on what matters to the processes downstream, and refine that knowledge continuously, by, for example, reducing the data stewardship efforts for vendors that aren't particularly important based on the details held.

Lever 4: Design across functions

MDM is, by definition, an effort that connects the dots between finance, supply chain (whether physical or virtual), procurement, and other functions, such as compliance and risk. Many MDM efforts optimize master data "for the sake of master data," focusing on the technical changes required for the data to be maintained and used easily. The issue with these approaches is that they fail to take sufficiently into account the business needs of the various functions in the company’s end-to-end process, for example, in order to cash or source to pay. The following charts describe some of those macro flows at a high level. While all executives intuitively understand those "wirings," MDM exposes the complexity of formalizing and maintaining the respective data structures.

Inter-company profit tracking and reporting is a good example, as the process encompasses multiple stakeholders across functions. The goal is to defer intercompany profit until the product leaves the organization, either by way of sale to a third party or disposal. At that time, the profit is released to the cost of goods sold. Figure 3 visualizes this cycle.

MDM can have an impact on the challenges related to releasing profit back to the P&L, which include:

  • Data mismatches (material attributes, intercompany partners, etc.)
  • Timing issues
  • Difference in billing systems
  • Multiple inventory accounting policies and loss of visibility to true product cost
  • Balance sheet reserve not in alignment with pre-paid tax due to inconsistent data between accounting and tax
  • Lack of central visibility of product inventory decreases corporate ability to optimize working capita
  • Period-end timelines impacted by manual processes

The problem is compounded by the multiple functions and stakeholders involved, as described in Table 3.

Setting the tone from the top of the organization with a cross-functional steering committee is key to the success of any such initiative. Additionally, by blueprinting the business processes associated with intercompany profit tracking and designing a single global standard process, it is possible to tackle the associated master data and define global versus local data attributes (materials are assigned global product IDs, whereas standard costs and units of measure maintain locally assigned values). The program yields a centralized view for inventory, standard costs, and transfer prices, and is sustainably and agreeably governed by the various parties involved in the process.

A similar situation is typical in the source to pay process. The chart below shows the steps, best practices, and associated best-in-class data requirements. Clearly, understanding the process end to end helps resolve the data requirements more effectively. Taking this approach makes it easier to identify which data sets are most material to the execution of the whole process and achievement of the main business outcomes, such as enabling business units to buy from the right suppliers and reap the benefits of volume discounts based on master data generated at the sourcing level. Conversely, it becomes easier to defocus the efforts on other data fields or reduce the emphasis on MDM training and compliance on specific groups.

Lever 5: Build to adapt instead of build to last

In many MDM deployments there will be unexpected events, such as system integrations that don't go as expected, the discovery of additional legal requirements that had previously been disregarded, the acquisition of a new business, etc. In many of these circumstances, the MDM teams will be forced to adopt solutions that weren't planned (or budgeted) for, such as the remediation of large data sets.

The volatility of the business environment is another major factor for the need for agility. This requires the ability to continuously tweak or outright build new parts of the solution. The traditional approach of having waterfall projects with clearly defined periods of design and build (often measured in years) should be supplanted by methods similar to the agile methodology now prevalent in software coding, which enables a faster conversion of small requirements with the ability to test and pivot faster. Any of these approaches requires the ability to design, build, and operate at the same time and out of the same group, and pushes those groups to be more interdisciplinary.

Organizations that break down the design-build-operate process into pieces and give parts to best-of-breed groups might inadvertently deprive the organization of the required flexibility to adapt.

Lever 6: Leverage a strong operating model

MDM is not an implementation - it is an operating structure that needs to complement other company functions. Master data is one of the newest functional developments in enterprise management, and doesn’t grow organically without concerted effort. Advanced operating models leverage specialized end-to-end process design, like the examples discussed in the fourth lever. They also employ specific organizational models, such as governance structures, centers of excellence, and global business services and/or outsourcing.

Finally, advanced operating models use technology judiciously. For instance, one important design choice is which system to use to maintain rather than store the data, or understand where the data comes from and how it has been manipulated. In the past, all these functions were performed by systems of record, such as ERPs, complemented by other tools. More recently, a new class of more agile technologies – what we call Systems of EngagementTM - enable better interaction with the people who work on and with the data, and facilitate the implementation and evolution of such solutions thanks to lightweight and often cloud- based implementations. For more information on this broad topic, read our research on advanced operating models, available at Insights.

Lever 7: Make your MDM operation an intelligent one

The continuous learning that enterprise processes can produce is often lost due to the fragmentation of systems and stakeholders. For example, understanding what data fields and which users are really important for certain downstream business outcomes, such as anti-bribery or other risk management areas, is clearly enabled by the continuous comparison (analytical in nature) between what was done by who, and what impact it had at the end of the process.

A continuous loop of learning can enable the fine tuning of tools, practices, and processes upstream. However, typical process designs are linear, and don't recognize that data, processes, and technology must cater to such feedback loops. For instance, a source-to-pay process may start with actions related to sourcing analytics and end with accounts payable (AP), but there is often very insightful data from AP that can guide MDM decisions focused on sourcing analytics effectiveness. The continuous analysis of how the MDM process is run and what results it produces generates significant opportunities for increased effectiveness. This is the base for an intelligent operation: its ability to sense, execute, and learn from its actions. Figure 5 illustrates this design principle with a case study from a CPG company focused on improving customer satisfaction with improved master data management.

Conclusion: Prioritization is more than a strategy, it is a design principle for MDM

In this analysis, we have described how MDM can be harnessed more effectively and make a material impact to the office of the chief executive by applying seven levers to your MDM strategy:

  • Start with the end, i.e., business outcomes, in mind
  • Define the right scope that maximizes impact and minimizes resistance
  • Design for operationalization - continuously stress-test the viability of design choices in the real world of people, systems, and existing processes
  • Design cross-functionally - have a single set of business outcomes and metrics in mind, irrespective of individual silos
  • Use a strong operating model encompassing organizational models, such as governance and technology, that, for example, apply systems of engagement instead of only relying on systems of record
  • Build to adapt instead of build to last to enable agility and continuous evolution
  • Create an intelligent operation for MDM that understands what changes must be made to master data operations to enable key business processes

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The underlying logic to all these levers stems from Lean management concepts: the ruthless, systematic, and data-driven prioritization of activities focused tightly on the end objective is the right strategy. Or, even better, use the levers to create design principles for your MDM operation and reap the benefits from more robust, agile, and intelligent operations across your business.