Opportunities arrive at the speed of information—and die at the speed of decision-making. What CFOs need most is information—accurate, up-to-date, detailed data to support fast, confident decision-making. And as the CFO’s role expands in increasingly strategic support of the entire enterprise, leading companies are evolving their master data management (MDM) to lower risk, boost profitability and competitively differentiate themselves.
Accurate and correct master data provide the foundation to create actionable insights through business intelligence and analytics—whether the need is to obtain a holistic view into a retail bank customer’s activity to determine credit worthiness or spot fraud, to predict customer behavior, or to determine the cost of a product. A granular, timely, enterprise-wide view into the customer, supplier, and employee and product portfolios gives CFOs deep insights that can help the company quickly adjust to market changes, maintain compliance and grow revenue.
A More Agile Enterprise
A moribund information management system that takes months to update a record simply cannot support fast, sound decisions. An industrialized approach supports all four essential pillars of best-in-class MDM: data, process, technology and governance.
CXOs need data that is free of duplicates and errors and adheres to global policies for completeness. Maintaining high data quality is a process in itself. It requires deep understanding of how data in each silo is entered, maintained, shared and reported. Determining who needs what information and when is the starting point for industrializing MDM processes.
The most successful companies are achieving satisfactory ROI by implementing enterprise-wide MDM structures that ensure the tools are well integrated with better processes and global policies for maintaining data quality. For instance, one global oil & gas company lowered costs by 35% by creating a center of excellence to standardize its master data processes across all its regions and business lines. These improvement measures also gave the enterprise much greater confidence in decision-making by raising data accuracy to 98% and ensuring that nearly 100% of changes to master data were processed within one business day. This, in turn, provided materially improved agility through better analytics and insight.
As this company discovered, many data management processes can be “industrialized”: simplified, automated and standardized across business lines and delivered via shared service centers (SSCs) or global business services (GBS) organizations that ensure compliance to internal policies and external regulatory requirements. Such organizations can drive down MDM costs by eliminating redundancies and integrating and managing customer, supplier and product data in a globally consistent manner. They provide CFOs and their peers reporting that is faster, more accurate and more detailed than most IT-focused legacy systems can deliver.
Partnering with an experienced service provider can help overcome institutional biases and effectively integrate disparate systems. A unified delivery organization also brings together requisite skill sets and uses standardized processes and common tools.
While better tools alone cannot drive more effective MDM, the right technologies are essential for supporting smarter processes. Many commercial master data hubs exist for centrally managing all domains, as well as bolt-on tools and smart technologies. The ability to deploy the right technology cost- and time-effectively often hinges on the ability to centralize process ownership and skills.
The last and most essential component of better MDM is comprehensive, enterprise-wide governance. The best MDM organizations institute policies that ensure every important record meets an established standard and is part of a single source of highly accurate information easily accessible to all stakeholders. The benefits can be significant. These include automated and real-time reports, more accurate forecasting and spend analysis, fewer disputes, and proactive spotting of trends in customer or supplier behavior. Shorter cycle times improve working capital, while the ability to quickly uncover fraud or supplier issues can vastly reduce risk.
Master data objects, when integrated with predictive analytics, provide deep and relevant insights. For example, customer records integrated with predictive analytics that examine customer behavior help the sales and marketing teams create more targeted and effective campaigns.
Many companies fail to realize that more accurate data, better processes and clear governance are required whether or not the company upgrades its data management tools. Since MDM must serve every stakeholder equally with the timeliest and most accurate information possible, it becomes an enterprise-wide responsibility, best managed from an enterprise perspective. Each consumer and producer of data has a role in capturing and protecting that data throughout its lifecycle. This is greatly facilitated by global policies enforced by a unified authority.
Gartner recently observed that a new chief data officer (CDO) role is emerging as an answer to the critical CXO need for meaningful insights. Even when a CDO is not in place, the most successful companies think globally when it comes to MDM. Creating an SSC or GBS MDM organization, whether in-house or partially outsourced, establishes a coherent governance structure with the power to integrate systems and keep each business line and division from instituting separate standards and policies. This MDM target operating model is adept at enforcing standards, monitoring compliance, and choosing metrics to measure success in maintaining data quality and speed of reporting.
Technology Alone Is Not the Answer
While MDM technology has evolved and matured quickly in the past decade, too many IT-led MDM initiatives fail to justify the return on investment. Commercial off-the-shelf tools for MDM now provide critical out-of-the-box features such as data stewardship interfaces, in-built workflows and higher data quality. And recent advances include social and mobile MDM—integrating external data from social media to create a “system of engagement” available on the user’s mobile devices.
Despite all these innovations, these MDM initiatives are often handicapped by the complexity of large-scale projects and an over-reliance on technology. Companies are discovering that established MDM methodologies and conventional approaches to improvement are no longer adequate.
MDM differs from other types of technology-driven consolidation efforts because of the need to closely tie technology, through business process integration and rule-based operational systems, with formally defined and centrally managed business rules. While data governance functions are rolled out in most organizations, they are often not active and operational. The business does not proactively manage data quality and SLAs through metrics and KPIs, even though the technology capability may exist within the organization. Additionally, project teams often don’t have a detailed understanding of the business outcome being influenced by the analysis enabled by master data. For that reason, they are unable to prioritize as required to limit implementation risk and keep projects relatively simple and time-effective.
A purely IT-focused approach is outdated and less effective than holistic industrialized operations that provide fast access to integrated data, enforce data quality and enable robust analytics. Modern businesses cannot afford to deny themselves the benefits of faster cash flow from fewer disputes, lower costs from integrated operations, and the priceless advantage of timely insights that let them seize opportunity as it arises and ward off risk before is too late.
Prakash Hariharan is VP and enterprise MDM practice head and Susmita Kanjilal is AVP MDM practice with Genpact, a consulting company focused on transforming and running business processes and operations, including those that are complex and industry-specific.