Master data management is a comprehensive approach using the people, process and technology in the enterprise to provide and manage a unified, consistent and accurate view of the master data. To successfully deliver on the promise of MDM, you must ensure that master data delivers to achieve the business goals. A successful MDM initiative will provide quality master data to upstream and downstream systems, including transaction systems, data warehouses, data marts and business intelligence, which in turn should help the enterprise gain trustworthy insight to solve business problems.
Starting down the path to MDM begins with an understanding of what constitutes master data. It is best understood in the context of the business processes. Master data is the core set of data elements used by the key business processes. Customer, product, material, supplier and financials all are examples of master data in the enterprise. You get the best definition when you put data in the context of the business operations. Master data impacts a wide range of business processes in the enterprise, ranging from BI to online transactional systems.
It is prudent to highlight what MDM is not before continuing the conversation. MDM is not a technology solution, it is not a toolset that you can deploy to solve a technology problem. MDM is not an application to be deployed to solve problems relating to inconsistent master data in the enterprise. MDM is also not about the data; data only provides the setting for the discussion. The focus should be on the business problem you are trying to solve. Data, along with various other components, will set the steps to solve the problem at hand. MDM forms the foundation for information management. It enables enterprise-wide data analytics and predictive modeling capabilities to provide end-to-end real-time reporting and performance management capabilities and proactively identify shifts that help improve quality and efficiency.
Guiding Principles for Successful MDM
Facts and figures are essential to making decisions in a successful business. Master data is the lifeblood of an enterprise and is a valuable strategic asset. Yet master data is seldom accessible, accurate, complete and secure. Why does this happen? Adherence to key guiding principles and best practices help promote the success of MDM. MDM initiatives can’t be launched in isolation. MDM guiding principles vary based on the industry, enterprise strategy and business requirements. The following are some widely agreed-upon guiding principles for MDM programs.
- Manage master data as diligently as other strategic corporate assets.
- Link MDM to business strategy and process improvement.
- Business owns master data and is accountable for data integrity.
- Ensure measurable ROI.
- Establish governance and standards for all master data processes and objects.
- Set up the framework for a business-led and technology-enabled MDM environment.
- Enable one version of the truth for core data elements in the enterprise.
- Realize that data quality is reliant on process excellence and ongoing governance.
- Plan to ensure consistency and alignment across all master data-related activities.
- Technology is responsible for deploying the MDM framework to support business requirements and data quality standards.
- Institute the change management process to address organizational politics and conflicts of interest because all the functions of the enterprise are involved in the MDM program.
Leadership should lay down the guiding principles for the enterprise-wide MDM programs. As an example of a guiding principle, there should be no compromise on the reliability, availability or timeliness of the data because being unable to ensure data quality leads to erosion of the value of master data, which leads to misdirected enterprise initiatives and less than optimal decisions. Adherence to guiding principles ensures the credibility and success of the MDM program, and as a result, instead of focusing on getting the data right, business can focus on getting the analysis right, knowing that data quality and data integrity is ensured.
How Can MDM Help Business?
Managing high quality, consistent and reliable master data is fast becoming a necessity. Master data is used as the authoritative source of data internally within the organization and also when it is shared with external entities. Master data serves as the cornerstone of business transactions. Following are some of the common benefits that are reaped by implementing MDM.
Essentials for Successful Business Intelligence
The ideal BI strategy makes MDM, along with data governance, the cornerstones of its success. MDM leads to enhanced, timely and accurate reporting and improved decision-making based on the quality of master data. It helps transform data into information, information into knowledge and knowledge into actionable intelligence. A successful MDM initiative has a direct impact on the master data quality, which often dictates the success of BI projects. Ensuring complete, consistent, accurate and timely master data lays the true foundation of the successful BI environment. An effective MDM solution is a must for the successful adoption of BI by the people in the enterprise. The impacts of ineffectively managed MDM program on BI are far reaching, and the results can be both tangible and intangible.
Governance, Risk and Compliance
According to TDWI, though all reports may benefit from improved MDM, regulatory and financial reports are a hot spot, because they are scrutinized carefully today and can cause dire consequences when discrepancies are found. For example, the consistently applied definitions of MDM ensure that reports are populated with correct data, and the data lineage of MDM answers questions in the event of an audit.
It is vital for an enterprise to produce goods or services better than the competition and to more effectively comply with the regulations in order to meet the business goals. To achieve this, effective management of master data elements is a must, as it helps with GRC. Enhanced ability to comply with regulatory requirements and manage risk goes a long way in the world we live in today.
Enterprise Performance Management
MDM helps in creating a global view of the legal entities and financial chart of accounts to provide seamless financial planning, budgeting, consolidation and closing process. MDM also assists by providing executives with the global view of the enterprise-wide data for analysis to solve the business challenges by efficiently planning and budgeting at more granular data levels with increased accuracy. To stay ahead of competition, the enterprise needs a way to evaluate performance across a number of master data hierarchies, such as geographies, cost centers, products and channels, while keeping the focus on customer and MDM helps enable it.
Product Information Management
PIM refers to processes and technologies enabling an enterprise to effectively manage and share the product data. The master data hub for the product data helps in building, procuring, marketing and managing the products effectively. PIM supports multilingual, multiunits of measure, as well as multicurrency systems across channels, cost centers and geographies. The benefits of product master data management are manifold, namely, reduced errors in item coding, item shipment, better aggregated spend analysis, inventory management, effectively managed procurement and sales functions. PIM plays a strategic role when you need to share product data with customers based on evolving data sharing standards such as global data synchronization. MDM helps the enterprise in a big way with global expansion strategies. The ability to provide consistent product data throughout business process workflows involving multiple vendors and partners results in rapid time to market and cost-effective solutions.
Customer Data Integration
CDI enables the centralization of customer information from multiple disparate systems. It provides a single view of the customer data across the enterprise. Different functions can access and use the information about customers from the same CDI hub. A consolidated customer master goes a long way in enabling better customer relationship management in addition to helping the enterprise meet strategic goals. MDM helps enable the single view of the customer, leading to an increase in customer satisfaction and better interaction with customers across touchpoints. A consolidated customer master provides accurate install base information and enhances the ability to cross-sell and up-sell products and services.
Customer and product master data helps the enterprise perform profitability analysis. Profitability analysis is a breakdown of the whole into parts to assign cost and revenue at a granular level of data in order to perform the analysis. It requires that all relevant costs are traced and then matched to their corresponding revenues. MDM enables such analysis at the customer and product levels across the business units of the enterprise, affecting the business strategy and a wide range of management decisions such as product pricing and product portfolio selection. MDM helps achieve changes in business strategies such as high volume/low margin, proactive cost management and competitive product pricing. MDM helps enterprises develop a clear understanding of customer and product profitability, cost and profit implications of changing the product portfolio, and associated commercial risks and implications.
Foundation for Service-Oriented Architecture
MDM enables a single view of the business across operational and analytical systems. A well-designed master data solution goes a long way in ensuring the success of SOA implementation, which leads to improved productivity of standards-based application development. SOA has the potential to transform the technology landscape. Deployed and managed correctly, it has the power to create business flexibility and ability to adapt to changing business requirements. Organizations that deploy SOA and MDM together can realize significant sustainable competitive advantage, as their technology and process groups become centers of innovation. On the other hand, for MDM to be most effective, a modern SOA layer is needed to propagate the master data to the applications and expose the master data to the business processes. In combination, SOA and MDM can have a profound impact on the ability of an enterprise to respond to changing business requirements, as the full potential of their respective capabilities is realized.
Successful MDM: Business-Driven and Technology-Enabled
In order for any MDM initiative to be successful, it should align with the business strategy. Driving MDM based on the business context ensures the successful alignment of business objectives with MDM initiatives to determine the business values being delivered and business needs being addressed. Some of the examples of business needs that are addressed by the MDM in combination with business process improvement include customer base expansion, growth in market share, cost reduction, cash flow improvement, product promotion, portfolio design and customer profitability analysis. These are the end goals of MDM, and not the tools and technologies being implemented. Keeping business context in the driver’s seat ensures that you don’t start confusing the tool with the solution.
According to Gartner research, success in MDM depends on ensuring that the MDM program is business-oriented and holistic. Organizations need to have the right focus and make the right decisions with respect to the business case and metrics, data governance, data stewardship and change management, as well as choosing the right technology and architecture. Without the right focus, many MDM initiatives will likely fail.
The MDM business case should address how will it help the enterprise achieve business objectives and how will it enable better operational, tactical and strategic decision-making. Business should drive the MDM program to ensure the successful adoption of the delivered solution. Keep in mind that a successful MDM program improves knowledge management, enables performance management, advances business by making the best use of information and enables BI penetration into the business processes.
The MDM strategy should include a broad set of processes, technologies and stakeholders for collecting, integrating, accessing and analyzing master data for the purpose of helping the enterprise make better business decisions. It should start with a business case and highlight how it intends to solve business problems. It should highlight in very clear terms the executive mandate that the MDM program carries. MDM is typically an enterprise-wide initiative where people and processes from all functions are involved so organizational politics, conflicts of interest and change management can stall the progress unless you institute adequate governance processes and procedures. You should not limit your ability to apply the principles with a restrictive MDM strategy. An MDM vision should be planned in advance of any iteration being implemented, because it is vital to establish an MDM strategy to ensure that implementation of specific components fits in the overall strategy. Adopting a multistep and multiphased approach typically works better for MDM.
MDM Technology and Solution Architecture
MDM is part of the full information lifecycle, from data creation to data consumption. MDM technology selection should be done based on proven methodologies and processes to help manage risk, schedule, cost and effort around the toolset selection. MDM technology selection has less to do with the features and more to do with the fact that selected tools can deliver on the specific requirements. MDM can be deployed in a variety of architecture styles, ranging from a virtual MDM hub as a reference for the data that is physically in the source systems to a centralized physical hub storing the master data. Selection of the architecture depends on business requirements, state of the data and the level of readiness. You can also adopt different architecture styles for different types of master data as different master data elements relate to business processes differently. MDM architecture styles vary according to various parameters such as data touchpoints, business functions and industry. As a result, customer, product and financial master data can have different architecture styles, leading to a variety of ways of deploying the MDM solutions in the enterprise.
Data governance ensures that the controls, policy, process and audit mechanisms are in place for master data elements. It provides a framework to create a methodical approach toward managing the data across the enterprise, and it is a must before launching an MDM initiative. In very clear terms it defines and manages the roles, responsibilities, rules and accountability structure. Data governance with MDM ensures that you achieve the goals of increasing confidence in decision-making. By instituting data governance, dual goals of making master data universally visible and instilling confidence in users about information reliability are achieved. Data governance provides for an enterprise-wide data governance body, a policy, a set of processes, standards, controls and an execution plan for managing the master data. It promotes master data quality, integrity, consistency, timeliness, security and information privacy, and thus increases the information usability and reliability.
Strategies such as service-oriented architecture, BI and business process modeling can’t deliver on their promise unless the underlying master data meets the data quality standards to add value. The data quality approach should be comprehensive, and it should have an enterprise-wide perspective, otherwise the master data for different functions will deliver different information leading to inconsistent BI solutions. Master data should be managed all the way from inception to consumption. Master data issues should not be put on the back burner just to comply with the schedule or budget targets. Keep in mind that disregarding the master data quality results in limited acceptance or rejection at a later stage because the information delivered at the end can’t be trusted. The quality of master data can be measured by determining the number of the indispensable characteristics such as master data being accurate, precise, valid and timely. Additional criteria such as depth, latency, volatility, completeness, consistency, uniqueness, accessibility, availability and cohesiveness also help in measuring the quality of the master data.
It is critical to effectively manage master data across the entire data lifecycle as it moves within the enterprise and also when it touches and communicates with business partners. The data architecture includes the data structures used by business and technology groups. It defines the master data in storage, in use and as it moves along the business processes. Descriptions of data stores, domains, hierarchies and items are included as well as where the data items stand relative to systems, applications and business processes. When you are designing the data architecture, keep in mind the end goals of better decision-making, improved productivity, efficient operations, reduced risks and consistent quality. Factors such as business policies, processes, data processing requirements, investment capability, compliance needs and technology drivers influence the data architecture design. Data architecture is designed at conceptual, logical and physical levels, and it accounts for business entities, relationships between the business entities, realization of the data at the granular level and data flow along the business processes.
Agile and Adaptive MDM
There’s no such thing as one size fits all with MDM, and keeping the business goals and guiding principles in sight means the difference between a failed initiative and a successful deployment. Each MDM initiative has its unique challenges, but all are driven by the business requirements of the enterprise. MDM initiatives should be designed to be agile and adaptive. The approach should enable continuous improvement to meet the enterprise objectives. It should be your intent to evolve your MDM initiative as part of the enterprise vision. Consider the current trends and also the coming wave of forward-looking approaches to build a successful MDM solution.
Prashant Pant is a manager with Ernst & Young LLP. He is a performance improvement professional within the Advisory Services. He has more than 14 years of consulting experience in technology strategy, information management, BI/DW, MDM, ERP, BPM and SOA. He has cross-industry experience in finance, health care, energy, retail and public sector. He holds master and bachelor degrees in engineering. He is a TDWI-certified master-level BI professional, project management professional, Oracle-certified professional, and Teradata-certified professional.
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