Master data management (MDM) is a data management discipline to actively “manage” master data enterprise-wide rather than “maintaining” it in each transactional system. There is heightened attention on MDM recently due to the pervasiveness of business intelligence (BI) applications. MDM unlocks the true value of BI by providing a consistent view of business performance measured or analyzed through the key master entities of an organization.

 

Integrating master data can be perceived as an IT-related issue, and, hence, business stakeholders may be reluctant to engage in these initiatives. But MDM is a cross-functional, technically complex, process-oriented discipline affecting data governance of master entities, which requires acceptance and wide organizational support. MDM initiatives resonate well with each business function when a rationale, viability and applicability are unambiguously articulated and a credible business case is presented.

 

In this article, I will cover three dimensions of laying out a business case: master entity, technology landscape, and process and industry vertical. A scenario covering a specific master data entity – products, a specific industry and functionality – a distributor, a specific technology (tool agnostic) landscape and their organizational pervasiveness is explored.

 

Justifying MDM

 

Errors in master data across multiple sources/applications can cost an enterprise significantly in terms of missing business opportunities or creating dissatisfied customers. MDM helps in reducing such costs and also potentially helps business growth.

 

Technology initiatives such as enterprise resource planning (ERP), customer relationship management (CRM) and business performance management (BPM) offer an immediate, material impact on business performance and visibility upon implementation. Benefits such as increased revenues, increased productivity or reduced cost for these initiatives could be established, quantified and tracked. Developing a business justification of MDM is challenging because the benefits are often indirect and intangible, as MDM is essentially a technology stack lying between the transactional and analytical systems. There is also inadequate precedence of quantified business benefits due to limited implementations.

 

Each master entity has its own significance and unique applicability in each situation (size, scale of operations, IT landscape and requirements related to the master entities). For example, managing customer data will yield more benefits on the demand side when compared to products data, which may result in supply chain efficiencies. Hence, an effective business case should be built for each significant master entity and analyze the potential value in stewardship of that data. The following aspects help build a case for MDM:

 

  • Current challenges in the operations and transactional systems (scenario)
  • Business benefits in each functional areas (drivers)
  • Opportunities yet to be realized (analytics).

A persuasive case should showcase benefits that are achievable and quantify those using models such as ROI, total cost of ownership (TCO), return on assets (RoA) or total value of opportunity (TVO).

 

Scenario

 

Let us consider a fictional distributor for electrical and datacom parts called Axal Inc. This distributor has a network of 300 warehouses/branches supplying 1.2 million products with seven major product families and processes 10 million orders a month. This distributor has more than 23 legacy systems, including ERP and CRM systems that support the core functional areas of warehouse, logistics and distribution, sales and marketing and finance.

 

Axal follows a stringent availability promise of 30,000 (delivered within a day), 9,000 (on-the-shelf availability at its branches) to its customers. Axal grows inorganically through mergers and acquisitions to achieve a larger market share and economies of scale. The sales channels for this distributor are bulk sales to retailers, retail outlets and e-business. The supply side involves thecoordination of many vendors and manufacturers. Because, Axal is in a commodity market, cost levers (margins, profitability of product portfolio and efficiencies) take far more precedence than the demand-side market dynamics (volumes).

 

Assuming revenue of $1 billion with a gross profit margin of 5 percent, given the current volumes, each 0.1 percent increase in margin would be worth around $5 Million. The cumulative gross profit will be in excess of $25 million in five years, if average industry growth rate of 8 percent compound annual growth rate (CAGR) is sustained (representative numbers). A solution that provides for cost control or improves the process efficiency by 0.1 percent could bring in executive sponsorship without much difficulty.

 

Product Data Management Office (PMO)

 

PMO is entrusted with product lifecycle management within Axal Inc., PMO has a team of 10 people who serves a data stewards and possesses most knowledge about products. They receive data from partners (unit of measure (UOM) and costing), syndicated data service providers (product attributes and national pricing) and internal business units. PMO’s main activities are:

 

  • Identifying new products and setting up item classifications, specifications, documents and configurations.
  • Ensuring product data quality with validations and standardization.
  • Deciding whether to store a product in the warehouse or not.
  • Making changes to all types of product information - attributes, attachments, item relationships and structures including eliminating duplicate product set-ups.
  • Routing and assignments based on item classification and change type for proper approvals, which undergoes cross-functional, multiple-step authorizations.

Challenges in PMO

 

There are many manufacturers and trading partners (about 2,000) connected through EDI, enormous volumes of product data (extensible, user-defined change categories, types and attributions 23 legacy systems (ERP, CRM and warehouse systems). The processes are either manual or semiautomated, performed through emails and spreadsheets. Due to this, synchronizing product data is cumbersome and error prone. Some challenges faced by PMO are:

 

  • Avoiding manual errors arising in the areas that are not automated. Different product attributes are maintained in different systems. Synchronizing changes in product attributes is a tedious process. There are multiple points for failure because of stale information exchange between the IT systems.
  • There is no single way of referencing products across the organization.
  • A lot of time is spent on cleansing out-of-sync product information rather than providing valuable business insights. The estimations indicated that about 30 percent of the product information is inaccurate.
  • M&A results in additional redefinitions of their business rules and integration with new systems.
  • Due to limited product knowledge across the organization, PMO needs to attend to voluminous requests.
  • The processes are inherited and were not adapted to the technology. There are several manual touchpoints, resulting in slower execution and lower productivity.

Drivers for Product Management

 

Some important master entities impact different functions. Creating an environment with “complete” product data available for sharing in a collaborative environment will result many benefits discussed in the sections below. The main objectives for product management are to minimize lost sales, stock-out situations and erroneous data in master files and processing errors in ordering, receiving, invoicing and reconciling through providing accurate and consistent product data.

 

Mergers and Acquisitions (M&A) Challenges

 

Growth through M&As creates challenges in terms of integrating and rationalizing systems, channels, brands and customer information. Accelerating integration of core functions post-M&A results in “realizing” the synergies that are anticipated of M&A faster. Along with finance function, which is the first function to be integrated in such scenarios, if the product portfolio of the acquired company is assimilated, the best sourcing options can be recognized. This is achieved through renegotiation of company-wide purchasing agreements, discounts, charge-backs or rebates with vendors. In other words, if an acquired company has better terms, those rebates will be leveraged. This leverage is attained by the ability to quickly and thoroughly apply cost/chargeback at the lowest level of business transaction - product/branch.

 

Product Knowledge

 

Product knowledge is an organizational asset. It is the basis for strategic decisions, such as new product introduction, channel penetration and acquisition plans. Lack of a strategic approach to enterprise product information management hinders the ability to quickly respond to the market conditions. Due to decentralized approaches and the transactional systems catering to the local/functional needs of the line functions, “silos” of knowledge about the products have been created.

 

  • A comprehensive reference about products aids faster decision-making and encourages collaboration between business units.
  • Business units or functions will save time, if they could self service rather than depending on long-cycle PMO request.
  • Acquisition targets can also be evaluated from a product portfolio perspective to get a better understanding of total value.

Item cross references between the systems and relationships such as substitutes and equivalents, supercessions, cross-sells and up-sells. Relationships help in:

 

  • Customer item cross-references support order processing and shipping functions.
  • Maintaining an approved manufacturer list (AML) for tracking the relationship between items and their manufacturers.

Influence on Some Business Functions

 

Any errors in master entities can result in operational inefficiencies and also increase in costs due to non-value add work required for resolving them. We illustrate some benefits of good MDM in order management and inventory management business functions.

 

Order Management and Invoicing

 

The following benefits drive the product MDM initiative:

 

  • Increased speed to shelf for new items and reduced merchandising time on item introductions. This can potentially increase market share and customer loyalty.
  • Reduced shelf-tag and checkout errors in the stores. This reduces administrative costs.
  • Reduced time and errors generating purchase orders, invoice reconciliation and product delivery. This reduces administrative costs.
  • Increased efficiency of processing orders, fulfillment and shipment of goods.

Inventory and Warehouse Management

 

Inventory and warehouse operations are a major cost for any distributor. Distributor industry averages for stock outs is approximated at 8 percent. Reduction in stock-outs by each 1 percent significantly affects customer satisfaction and thus, market share. Improving inbound and outbound operations will result in higher profitability. Analytics that support these two broad themes are enabled when MDM is implemented:

 

  • Identify high product returns and reduce them.
  • Predict, identify and control obsolescence of products.
  • Ability to study inventory and replenishment patterns to reduced out of stocks.
  • Hedge against price increases or future shortage by means forward buys.
  • Reduce the items that are stocked in the warehouses with a goal to minimize stock but also to maximize the range of product offerings.
  • Analyze factors affecting inbound/outbound logistics (receiving and handling) costs and reduce them.
  • Save time in store through reduction of shelf-tag and scan errors.

Processes Affected by MDM

 

Because there are many different coding schemes for product identification the manual identity resolution for cross-referencing is often flawed. Inconsistent and outdated or delayed information exchange across the supply chain creates inefficiencies and poses reconciliation challenges. It is necessary that an improved supply chain/demand visibility is enabled across transactional systems, leading to lower inventories and order processing errors. In this context, it is important to design the right business processes for managing an effective MDM considering factors from IT and data governance perspectives.

 

Simplification in IT

 

Any typical IT portfolio has many legacy and stovepipe systems supporting operations, often sharing a lot of master data between themselves. Their diversity increases also due to M&A activities.

 

A simplifying and rationalizing product data result in improving various point-to-point interfaces related to master data between various transactional systems and improves data latency for timely decision-making and visibility. It improves the controllership and enhances IT efficiencies, which lowers the overall TCO. It is widely agreed by the practitioners of MDM that it creates agility in IT to implement collaborative initiatives such as RFID and SOA, etc. These initiatives will also help in general nimbleness for further integration during M&A. MDM is also considered a foundation technology for BI initiatives.

 

Data Governance and Standards

 

A key driver for MDM is standardization and consistency in data related to Products. Standardization is often accomplished by means of adopting open standards such as GTIN/GLN. Various standards and standards bodies are UCCNet, RosettaNet, CIDX, ebXML, WSDL also helps in data synchronization through identity resolution and cross-referencing of the entire ecosystem of the IT landscape. This helps in meeting compliance, regulations and standards on Hazmats such as RoHS or Sunrise 2005 Compliance and Corporate reporting.

 

Products data needs to be governed actively because:

 

  • There may be a same product sourced from a vendor as well as a manufacturer or a partner. They are usually set up in the transactional system directly. Time spent on resolving interdepartmental product identity resolution can be reduced.
  • Inadequate data survivorship and attribution processing. This reduces the valuable information related to item relationships and obsolescence lead factors.
  • Errors due to manual and semiautomatic processing.
  • Data integrity caused partly due to different generations of legacy systems. Data conversions and migrations during M&As, limitations in interfaces between the systems.
  • Standardization: Noncompliance to UPC or Global Trade Identification Systems standards, the lack of an agreed-upon system with all trading partners.

Opportunities for Better Analytics

 

With a good single view of the product (qualitative and quantitative data) in place, various opportunities can be unlocked using analytics which otherwise are not leveraged. All this data may not always be consolidated in a specific data repository; however, consistency and quality of this data and metrics will add benefits that MDM alone may not achieve. The diagram in Figure 1 provides product analytical context.

 

  

Marketing and Sales

 

Some benefits of MDM to sales and marketing areas:

 

  • Enabling application of marketing programs consistently, locally or globally. Customer segmentation and targeting based on the accurate buying patterns of products, thus promoting sales of preferred product lines, potentially replacing lower margin or less desirable lines.
  • Identifing a market trends for products, making predictions on whether to sell or discontinue, and helping continuously refine business rules about sell, stock or offer and through which channels. Ability to predict sales for the new items (based on the category, commodity code, etc.).
  • Targeted marketing campaigns based on inventory carrying cost and gross margin. Helps determine policies for discounts or chaining discounts together in a sequence.
  • Sales analysis of product lines at various levels of organizational structure to find patterns and ensure promotion efficiencies of inventory, increase in sales, etc. Also, determine the effect of price increases (price sensitivity) on sales.
  • Detecting up-sells and cross-sells. Identification of item relationships, such as substitutes and equivalents based on the item attributes.

Profitability Analysis and Pricing

 

Margin analysis helps in identifying profitability at different levels. Pricing the product better helps competitiveness. Following are some analytics:

 

  • Identify the low value items (commodities) and increase the high value items and manage margin degradations. Recognize top-selling items or high-margin items. Identify the product lines that can be eliminated without affecting the margins or growth.
  • Ability to measure profitability by business units, product lines and customer segments based on raw margins. Perform what-if scenarios, analyzed by item, by product group, price group or vendor. Can offer benefits for the management of the vendor margin squeeze. Any upcoming margin degradation can be discovered – to the lowest business unit level.
  • Relation between margin degradation and obsolescence. Ability to answer questions like, are the margin squeezes happening because of the supply side issues or demand side issues or market forces? Ability to predict obsolescence based on the trends and margin erosion. This also enables prediction of margins overtime (after stale factors are identified).
  • Ability to baseline product pricing. Analyze pricing based on different pricing such as manufacturer’s list, wholesale, suggested retail and zone.
  • Study interrelationships such as revisions versus cost changes, obsolescence or supercession versus margin degradations, new items versus margins. Analytics also enables you to traverse the hierarchy such as product group, category, commodity, brand.
  • Range items to organizations based on organizational business indicators for optimal margins.

Inventory Management and Sourcing

 

Sourcing the right product from the right vendor at the right price at the right time.

 

  • Vendor ranking (based on quality, timeliness, cost, service) and capability assessment.
  • Identify the best source for commodity items to increase the margins.
  • Analyze competitive bids by benchmarking with the information.
  • Analyze stock outs and increase the service levels.

MDM improves the quality of information of the master entities in the upstream transactional applications and downstream analytical applications. Implementing MDM needs support from all business functions, which is influenced by a strong business justification for a master entity such as product or a customer.

 

For Axal, it is understood that a product MDM solution could improve profitability, increase supply chain efficiencies, improving business processes and their subprocesses through eliminating manual processes and inefficiencies due to data errors. MDM also helps attain transparency and knowledge sharing within the organization and meets compliance and regulatory requirements.

 

MDM is a data management discipline that helps realize the true value of analytical applications and should form a strong foundation for information management initiatives. It also enhances organizational readiness for advance analytical solutions such as predictive analytics, which can give an enterprise a great competitive advantage.

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