- Point of view
CEOs and their teams have access to a wider pool of data than ever imagined even 10 years ago. The problem is not enough information, but getting to the right information to support sound decision-making. Many executives intuitively understand the value of master data in that process. They sense that highly accurate customer, vendor, product, and transaction data can help companies comprehend and react to paradigm shifts, and maximize profit or minimize loss. Where executives struggle, however, is creating a strategic organizational transformation that gets master data to work in a sustainable way, at scale.
Our experience indicates that a new operating model for master data—not just a new technology project—is the key to unleashing the insight-to-action power trapped in vast quantities of enterprise data.
Effective MDM is a process that requires an integrated operating model
Although many companies view technology as the driver of more effective MDM, the sad fact is that too many IT-led MDM initiatives still fail. Despite numerous commercial off-the-shelf MDM tools that provide critical out-of-the-box features such as data stewardship interfaces, in-built workflows, and higher data quality, these tools alone cannot enhance master data without an accompanying focus on clean data, smarter processes, and an enterprise-wide MDM governance structure for maintaining data quality. Industrialized processes, along with a target operating model that supports a unified authority and holistic views, provide the highest return on investment in MDM transformation efforts.
Figure 1 (PDF) shows the set of activities that can be shared and “industrialized” as opposed to those retained in individual business lines and legal entities.
Silos can’t handle volatility, but MDM can
In a recent survey of almost 1,000 executives from large companies in developed markets,1 we found that—quite surprisingly—master data management (MDM) was seen as a very important weapon against the key challenges affecting enterprises. Some striking examples: 55% of finance executives, as well as the same percentage of marketing executives, and 48% of procurement executives see MDM improvements as one of the “top 3” initiatives for enterprise agility and adaptability. Interestingly, 59% of procurement executives indicate that MDM helps materially reduce costs, 63% say the same about MDM’s impact on compliance (as 49% of finance executives do), and 59% believe that MDM can materially improve risk management. Although these scores are very high, it is interesting to identify who does not rate MDM as so important. The answer is: those who have more basic issues to grapple with. In a separate study,2 we observed that 48% of Finance Planning & Analysis (FP&A) leaders in mature FP&A organizations consider MDM and governance a very high priority—compared to only 25% of those in less mature FP&A functions.
So useful, yet not trivial to harness
Master data encompasses all key information such as customer profiles, product portfolios and performance, production capacities, inventory levels, supplier information, and host of other data that, in their own right, is required to process crucial operations across the business. Viewed independently, these bits of information deliver insights into the key parameters of sales, profit, and growth. However, viewed collectively, the data provides insights global enterprises use to build real-time scenarios that can have far-reaching implications in helping companies maximize profits and grow at a greater pace. For that reason alone, MDM should be near the top of any CXO’s to-do list for improvement.
Although senior executives seldom look at raw master data, their decisions are often drawn from insights provided by it, which is why accuracy and timeliness in collecting and integrating data are so critical to business success.
Better MDM can have a significant positive impact on managing market volatility. Systems that take months to update a record simply cannot support fast, decisive responses to market changes. Conversely, well-integrated data systems with analytical capabilities can quickly spot spikes in sales, production cuts, or changes in consumption across geographic or product sectors. Amazon, for instance, uses dynamic pricing based on MDM output by region to drive sales. Even banks, which traditionally have not emphasized MDM, are now using it to understand individual customer behavior and preferences in order to manage risk and selectively market products based on customer profiles. Consistent processes and systems across geographies and business lines are therefore essential for quality data and timely reporting.
Unfortunately, MDM is hampered not only by the sheer volume of information but also that the data is usually held in multiple business silos on disparate legacy systems and processed by separate teams that handle only one aspect (product, procurement, billing, etc.). In short, there is no single source of truth across countries or business lines, or any way to link the data to arrive at integrated reporting and analysis that would provide a clear and holistic view of operations. For bank executives looking to spot fraud or accurately assess a customer’s creditworthiness or retailers looking to understand consumer interests and product costs in various regions, this lack of coherent MDM and related analytics cripples the company’s ability to lower risk or to react quickly to trends and market changes.
Simply setting up data warehouses can often result in silos filled with vast amounts of information that cannot be properly analyzed or integrated to provide the comprehensive view that CXOs need. Cost accounting, supply chain data, and ERP functions, if conducted in silos, severely hamper CXO views of performance, with a subsequent negative impact on decision-making and, ultimately, the bottom line. CXOs need data that is free of duplicates and errors and adheres to global policies for completeness, as well as analytics that can “slice and dice” data at the required level of granularity. Achieving and maintaining such high-quality data is a process in itself, one that requires deep understanding of how data in each silo is entered, updated, shared, and reported.
Simplifying, automating, and standardizing the processes for maintaining master data across business lines go far in reducing redundancy and cost while integrating and managing customer, supplier, and product data in a globally consistent manner. Enterprise-wide, MDM processes can be managed through shared service centers or global business services. A global oil and gas company, for example, slashed costs 35 percent by creating an MDM Center of Excellence to manage materials, vendors, and service masters and standardize MDM processes across the company’s regions and business lines. This raised data accuracy to 98 percent and ensured that nearly 100 percent of master data changes were processed within one business day. These improvements, in turn, supported faster, more confident decision-making through better analytics and insight.
The right tools for MDM support smarter, simpler processes and analytics that help CXOs make sense of all that information. A surgical approach ensures that the right tools are deployed to address particular goals. The alternative is a massive and disruptive upgrade that, in the end, may not provide the necessary integrated view and analytics capabilities. Bolt-on tools and master data hubs should enable users to pull out the minimal data sets required to solve a problem, while supporting broader reporting capabilities and governance efforts.
No matter whether the enterprise updates its MDM tools, effective MDM requires comprehensive, enterprise-wide governance. This includes global policies that ensure all data meets an established standard and is widely accessible to stakeholders, and continuous monitoring of metrics that drive process performance. Many companies resist a centralized authority because process owners usually have a clearer view of ground-level realities; however, industrialized processes and global governance ensure that all data consumers and producers understand their role in capturing and protecting data throughout its life cycle. A unified authority is most appropriate for setting and enforcing global policies that ensure high-quality data.
The endgame in any effort to build a master data repository is to establish a single source of truth, one that is dynamically distributive in how it integrates and manages the flow of information across the enterprise. A properly centered MDM platform simply will not allow for the possibility of “two masters” for institutional data, let alone multiple masters. Leaders know such thinking is dangerous and oxymoronic, and ultimately muddies the waters of insight. Likewise, they understand that the insight springing from MDM’s transformation will set those data free in ways laggards will not be able to replicate for long—placing the latter on the wrong side of time-based competition.
Industrialized MDM is the plumbing that unlocks scalable and meaningful analytics
The importance of MDM for informed and agile decision-making cannot be over-stated. Some of the greatest challenges facing enterprises can be addressed by solving the MDM issues that originate due to disparate systems and scattered MDM operating models. These systems and models undercut information sharing and degrade the company’s ability to deploy analytics that would provide deep, real-time insight for managing volatility, lowering risk, and driving revenue. Because of the cross-functionality of many MDM problems, and the fragility of IT-only solutions, senior executives are well advised to take an up-close look at related efforts and ensure that they are seen as IT and operating model design efforts.
This has been authored by Gianni Giacomelli, CMO and SVP, at Genpact.
1. Research commissioned by Genpact and carried out by LinkedIn, April 2014
2. FP&A research, conducted by Zenesys consulting on behalf of Genpact, February 2013, on a sample of 150 FP&A leaders in large, global organizations