The State of Master Data Management, 2012

Transcription

1 The State of Master Data Management, 2012 Building the Foundation for a Better Enterprise May 2012 Nathaniel Rowe

2 Page 2 Executive Summary Master data, or critical information on customers, products, suppliers or similar categories, provides the foundation for many key business processes. When this information is accurate, consistent and up-to-date, organizations have better visibility into internal operations, interactions with their business partners, and the behavior of their customers. When it is missing, incorrect, or hard to find, organizations experience inefficiencies, delays, errors, and poor business decisions. Aberdeen's survey of 163 organizations between March and April 2012 reveals how companies are handling this critical information. Globally, there is a disparity between how important master data is to business operations and how little support it receives from senior management and budget committees. However, Best-in-Class organizations are more likely to have made investments to ensure high master data quality, and as a result are seeing dramatic reductions in data errors and improvements in employee efficiency. Research Benchmark Aberdeen s Research Benchmarks provide an in-depth and comprehensive look into process, procedure, methodologies, and technologies with best practice identification and actionable recommendations Best-in-Class Performance Aberdeen used the following three key performance criteria to distinguish Best-in-Class companies: Percent of master data that is complete and up-to-date Percent of master data that is accurate and properly classified Time spent searching for information (measured by number of hours / employee / week) Competitive Maturity Assessment Survey results show that the firms enjoying Best-in-Class performance shared several common characteristics, including: 65% have a formal Master Data Management (MDM) system in place 65% have automated the capture and creation of data from internal sources 60% have a cross-functional team from both IT and line-of-business to guide master data strategy Recommended Actions In addition to the specific recommendations in Chapter Three of this report, to achieve Best-in-Class performance, companies should: Align their data strategy to specific business objectives Secure support from senior management for improving master data Implement tools to manage and maintain data quality and consistency This document 2012 Aberdeen is the result Group. of primary research performed by Aberdeen Group. Aberdeen Group's methodologies provide for Telephone: objective fact-based research 5200 and represent the best analysis available at the time of publication. Unless otherwise noted, the entire contents of this publication are copyrighted Fax: 617 by Aberdeen Group, Inc. and may not be reproduced, distributed, archived, or transmitted in any form or by any means without prior written consent by Aberdeen Group, Inc.

3 Page 3 Table of Contents Executive Summary... 2 Best-in-Class Performance... 2 Competitive Maturity Assessment... 2 Required Actions... 2 Chapter One: Benchmarking the Best-in-Class... 4 Business Context... 4 The Maturity Class Framework... 4 The Best-in-Class PACE Model... 5 Best-in-Class Strategies... 5 Master Data Management in Chapter Two: Benchmarking Requirements for Success Competitive Assessment Capabilities and Enablers The Master Data Toolbox Chapter Three: Required Actions Laggard Steps to Success Industry Average Steps to Success Best-in-Class Steps to Success Appendix A: Research Methodology Appendix B: Related Aberdeen Research Featured Underwriters... Error! Bookmark not defined. Figures Figure 1: Poor Business Decisions Are Driving MDM Investment... 6 Figure 2: The Current Landscape of MDM Systems... 7 Figure 3: The Growing Complexity of Master Data Formats... 9 Figure 4: Data Integration Figure 5: Best-in-Class Differentiate on Discovery and Visibility Figure 6: Technology Supporting Best-in-Class MDM Performance Tables Table 1: Top Performers Earn Best-in-Class Status... 4 Table 2: The Best-in-Class PACE Framework... 5 Table 3: Best-in-Class Provide More Master Data Support Table 4: Master Data Benefits of the Best-in-Class Table 5: The Competitive Framework Table 6: The PACE Framework Key Table 7: The Competitive Framework Key Table 8: The Relationship Between PACE and the Competitive Framework... 26

4 Page 4 Chapter One: Benchmarking the Best-in-Class Business Context Master data management is not a very sexy topic for most organizations. Metrics around data management and data quality don't have nearly the impact of sales figures or garner the interest of revenue projections. However, in many ways master data is the lifeblood of an organization. This information supports most critical business operations in some capacity, and is used to inform high-level, strategic business decisions. Aberdeen's research shows that the health (i.e., the reliability and accuracy) of this information has a direct correlation with the efficiency and performance of these business processes. When this data is unhealthy, it is often one of the root causes of inefficient, time-consuming business processes, or inaccurate business decisions. However, given the cost, time, and complexity involved in implementing most MDM solutions, many organizations find it difficult to justify such an initiative. A mere 7% of all organizations are able to measure and track the Return on Investment (ROI) of master data programs. Despite this lack of understanding surrounding the business value of maintaining high quality master data, there are many organizations that are succeeding in managing and using this information. The Best-in-Class companies were 45% more likely to have invested in a formal MDM system than Laggard organizations, and report dramatically better performance in data visibility, number of data errors, data accuracy, and how much time their employees were spending on data-centric business processes. Definitions For the purposes of this report, the following definitions apply: Master data refers to the critical, underlying data that feed the applications and processes driving a business, as well as the reference data that regulates how different data silos relate to each other Master Data Management (MDM) refers to a formal initiative to improve and maintain the quality of master data, usually involving specific technology solutions as well as policy and processes changes The Maturity Class Framework Aberdeen used three key performance criteria to distinguish the Best-in- Class from Industry Average and Laggard organizations, as shown in Table 1. Table 1: Top Performers Earn Best-in-Class Status Definition of Maturity Class Best-in-Class: Top 20% of aggregate performance scorers Industry Average: Middle 50% of aggregate performance scorers Mean Class Performance 95% of master data is complete and up-to-date 94% of master data is accurate and properly classified 1.2 hours per week per employee spent searching for information 83% of master data is complete and up-to-date 79% of master data is accurate and properly classified 4.4 hours per week per employee spent searching for information

5 Page 5 Definition of Maturity Class Laggard: Bottom 30% of aggregate performance scorers Mean Class Performance 53% of master data is complete and up-to-date 51% of master data is accurate and properly classified 8.2 hours per week per employee spent searching for information The Best-in-Class PACE Model Source: Aberdeen Group, April 2012 Top performance in managing master data requires a combination of strategic actions, organizational capabilities, and enabling technologies. The characteristics exhibited by the Best-in-Class organizations in this study are summarized in Table 2, and analyzed in greater detail in Chapter Two. Table 2: The Best-in-Class PACE Framework for Master Data Management Pressures Actions Capabilities Inaccurate decisions due to poor data quality Align data strategy with business objectives Map out data infrastructure and high priority data sources Best-in-Class Strategies Standardized training on MDM systems Define and enforce CRUD roles Discover all business data Classify all business data Continuously maintain and update data definitions The ultimate goal of master data management is to provide an authoritative repository, or "single version of the truth," of these critical records. A successful implementation can allow employees to quickly and easily locate the information they need, without having to worry about which version is the most recent or if it is in a format they can't use. In fact, not having this type of central, trusted system was one of the most common reasons why organizations are considering investing in MDM, cited by 53% of all organizations (Figure 1). However, Aberdeen's research shows that the most prevalent motivation for making an MDM investment was that organizations are concerned about poor business decisions. When data and reports on business activity are inaccurate or incomplete, decisions based upon them can be imprecise and Enablers (% Best-in-Class Adoption) Formal MDM system (65%) Automated data capture from internal sources (65%) Data enrichment (62%) Data de-duplication (56%) BI tools for master data (56%) Data cleansing tools (48%) Data quality tools (46%) Automated data capture from external sources (44%) Source: Aberdeen Group, April 2012

6 Page 6 potentially disastrous. Fifty-six percent (56%) or organizations listed this reason as one of their top pressures for improving their master data. Figure 1: Poor Business Decisions Are Driving MDM Investment Fast Facts: Data Completeness Additional analysis on the data quality reveals the following aspects of master data maturity: 88% complete, or "nearly complete," was the average master data record for the Best-in-Class. They reported having all essential fields and most supplemental information filled in. Source: Aberdeen Group, April 2012 Low employee productivity was another top concern, measured by the time and difficulty of accessing information necessary to do their jobs. As Table 1 shows, there is a difference of over seven hours per week in the amount of time spent by an average employee at a Best-in-Class company and one at a Laggard organization. In other words, if a Laggard organization could improve to Best-in-Class performance, they could save each knowledge worker 355 hours per year, or a full 8.9 work weeks of time and effort. It is no surprise then, that nearly half of Laggard organizations listed this as a top pressure, while only a third of the Best-in-Class did likewise. Finally, Aberdeen's research shows that the top performers are more likely to be concerned with the impact their master data has outside their organization. Business-to-business data exchange plays an important role in many industries. As mentioned in Aberdeen's October 2011 report, Data Quality and the Supply Chain, fast and efficient methods of transferring this business data between organizations can positively impact inventory visibility, complete and on-time deliveries, total landed costs, and the frequency of stock-outs. 77% complete, or "largely complete," was the average record for Industry Average. They reported having all essential fields and some supplemental information provided. 65% complete, or "partially incomplete" was the average record for Laggards. They reported having only some to most essential fields and little to no supplemental information provided. All organizations, regardless of maturity class, reported the desire for master records to be: >90% complete, or nearly "totally complete" Achieving the Master Data Dream Reaching master data success requires organizational support and technological tools, but it is also incredibly important to make sure that any data initiative aligns with overall business objectives. Over 70% of all organizations reported that this was their primary strategic action related to improving their master data. After all, clean and accurate data that sits in a database somewhere has no value to an organization. Clean and accurate

7 Page 7 data that is easily used by employees and decision makers in their day-today operations, however, is an incredibly powerful tool. Aberdeen's research also shows that data discovery is another tactical action taken by organizations implementing or improving their master data. Forty-eight percent (48%) of organizations are mapping out their data infrastructure to identify high priority data sources, and 36% are analyzing these data sources for the number and type of imperfections they contain. Once these companies have a clear vision of their data environment and current level of quality, establishing consistent data standards and naming conventions was another top strategic action, cited by 41% of organizations. Implementing a master data management system, especially in a large, complex data environment, is no easy task. Fifty-two percent (52%) of organizations report that the main challenge they are facing is their master data is stored in too many separate silos. Usually owned by a specific department or division, or aligned with a single application like Customer Relationship Management (CRM) or Enterprise Resource Planning (ERP), these silos force data changes to be made in multiple systems at once, or inhibit an organization from getting a complete picture of their business activity. Furthermore, many organizations were concerned about the time it takes to implement an MDM system (36%) and the cost of starting and maintaining the system (32%). These obstacles can often end an MDM initiative before it even begins, which is a fairly common occurrence, as seen below. Fast Facts: For organizations without a formal MDM system, the following were the most commonly cited events that would cause them to make an initial investment: 35% reported it would take a clearly defined ROI for an MDM investment 29% reported it would require buy-in from senior management 24% reported it would take a low-cost MDM solution 24% reported they needed a fast, easily implemented MDM solution to consider investment Master Data Management in 2012 Across the 163 organizations in Aberdeen's study, the most common method of handling master data involved no formal system at all. Figure 2: The Current Landscape of MDM Systems Source: Aberdeen Group, April 2012

8 Page 8 Forty-five percent (45%) of organizations rely on manual methods, such as spreadsheets or individual databases, to manage and store their master data. However, almost three quarters of these organizations were small or midsized companies (under $500 million in annual revenue). The previously cited concerns over time and costs of MDM implementation have impacted these smaller organizations the most, keeping many of them from addressing their master data problems. Among organizations with formal MDM systems, custom or home-grown systems are the most popular by a very slight margin. Many companies attempt to keep costs low by using internal development talent to produce these systems, or they feel the specific needs of their organization are not well suited to a pre-packaged solution. However, these home-grown systems can be risky, as a lack of expertise or understanding of master data issues (such as governance and access control) can cause more problems than they solve. Nearly half of organizations (49%) in Aberdeen's January study on Document and Content Management reported that they felt homegrown systems would either provide no significant improvement to the quality of their business data, or would in fact make the situation worse. The remaining organizations are fairly evenly split between relying on a system for a single critical master data domain (such as customer, product or supplier records), and having an end-to-end system to handle multiple domains. However, Aberdeen's research is showing a growing trend in the adoption of non-conventional MDM systems, such as open-source solutions, Cloud-based (MDM-as-a-service), or systems that use hybrid data environments of cloud and on-premise data sources. While only 10% of organizations overall have adopted these types of systems, the Best-in-Class organizations are leading the way with 22% adoption, compared to a mere 6% of all other companies. Data Variety and Data Integration The modern environment for business data is rapidly growing in size, variety and complexity. Aberdeen's research has the annual growth rate for business data at a 36% average for all organizations, with the top quarter of companies seeing rates of 75% to 150% growth year-over-year. In the April MDM study, the average number of master data records per organization was 6.5 million. There was a large variance between companies, however; 40% reported less than 100,000 records, 29% reported between 100,000 and one million, and 31% reported over one million. Best-in-Class companies were more likely to be on the larger end of this spectrum, with 42% of them reporting over one million master data records. Furthermore, organizations are beginning to see the value of unstructured data formats, and new tools are being developed to analyze and leverage this type of information. As Figure 3 shows, currently master data records mostly consist of structured data, such as names, addresses, SKUs, and product attributes like weight, color, and size. The average number of descriptors or attributes per record was 37, with little difference across all three maturity classes. Not all of these elements, however, were structured. Definitions Unstructured data refers to data that does not fit easily into structured databases. Traditionally textheavy, this category also includes other forms of rich media such as images, audio and video. In all cases, unstructured data poses additional challenges for discovery, classification and management.

9 Page 9 Unstructured data currently plays a significant role in master data, and Aberdeen's research shows it is growing at a rapid pace (Figure 3). Figure 3: The Growing Complexity of Master Data Formats "Understand the requirements before you start an MDM initiative. Due to the amount of effort and money vested, reworking is usually not an option. Short-sighted decisions typically can't be reversed except at the very beginning, and you might end up living with a sub-standard result until the business pressure forces you to do it all again." ~ Manager Large (>$1B) North American Paper / Lumber / Timber company Source: Aberdeen Group, April 2012 Unstructured, free-form text can contribute to master data records as product descriptions for product catalogs or e-commerce platforms, s, or complaint tickets from customers. Almost half of organizations (47%) currently use this type of data in their master records, and an additional 19% indicated they plan to expand their records to include this format. Pictures and images of products, and rich media like blueprints or Computer Assisted Drafting (CAD) files are used by about a quarter of companies, but these are expected to double in the near future. The largest growth rate can be seen in the area of social media. Monitoring brand sentiment and customer opinion through sites like Facebook or Twitter can provide additional insight into product demand and customer behavior, but only 8% of organizations are supplementing their master records with this type of data. However, almost a third of organizations (29%) are planning on leveraging this information, a growth rate of over 360%. In addition to the growing size and variety of data formats, the number of data sources organizations are accessing is increasing as well. These sources can include internal databases and applications, data feeds from business partners, cloud-based data silos, or even external repositories of public information like weather statistics, postal codes or geo-locational data. Aberdeen's research shows that fully integrating a larger number of data sources into an MDM system is a mark of top performance. The Best-in- Class averaged 16 unique internal data sources feeding their MDM system, compared to 10 for all other companies. The disparity was likewise seen in the number of integrated external sources: seven sources for the Best-in- Class compared to less than four for all others. Figure 4 indicates that the top performers are likewise more mature in their integration efforts, leading

10 Page 10 the way in both early-stage partial integration of data sources, as well as final-stage complete integration. Figure 4: Best-in-Class Integrate Within and Beyond the Firewall "MDM must be tackled early in a merger/acquisition to minimize business interruptions and reporting inconsistencies." ~ IT Manager Large (>$1B) North American Consumer Packaged Goods company Source: Aberdeen Group, April 2012 Finally, the Best-in-Class are providing faster pipelines for this information to feed into their systems. Almost half (46%) of the top performers have real-time integration of their data sources, providing near instantaneous updates for this critical data. Only 13% of Laggards have this capability, at 3.5 times lower adoption rate. This speedy method of integration hasn't yet replaced more traditional batch integration methods such as Extract, Transform and Load (ETL) tools, as 58% of the Best-in-Class also have this technology. However, other organizations continue to lag behind, with 43% of the Industry Average and 33% of the Laggards reporting implementing these tools. The Larger Master Data Picture Even with this backdrop of increasing master data technical complexity, it is important to address the organizational side of master data. Organizations rated several aspects of their overall MDM initiative on a scale of 1 (low) to 10 (high), and as Table 3 shows, there is very little difference in how important master data is to core business operations. Over four out of five companies rated the importance as seven or higher, with only +/- 4% between the different maturity classes. This indicates that it isn't just the Best-in-Class that are feeling the need to invest time and resources into master data.

11 Page 11 Table 3: Best-in-Class Provide More Master Data Support Ranking Categories High* importance of master data to core business operations High support / buy-in from senior management High resources / budget support for master data High adherence to master data policies Best-in-Class Industry Average 83% Laggard 70% 52% 47% 57% 31% 19% 72% 28% 12% *"High" is defined as a 7 or higher on a scale of 1 (low) to 10 (high) Source: Aberdeen Group, April 2012 However, despite similar levels of need, there is a great disparity in the organizational support across the maturity classes. Any enterprise-level initiative absolutely requires buy-in from senior management, and so it is no surprise that the Best-in-Class are showing much higher levels of executive support. Likewise, the top performers are three-times more likely to have high levels of resources and budget devoted to their master data. The most significant difference between the three categories came in their adherence to the policies surrounding the creation, management, and use of master data. The Best-in-Class are an impressive six-times more likely to have employees that strongly adhere to these business rules. There are many possible reasons for this, from whether an organization even has defined policies, to whether they enforce these policies, to whether employees understand the importance of maintaining data quality and security. The amount of time, money, and effort invested by these top performers can seem daunting, and the question naturally follows as to whether it is all worth it. Aberdeen's research shows that not only did the Best-in-Class have the increased data accuracy, data completeness and employee productivity shown in Table 1, but they reported superior performance in several other key areas of their data environment (Table 4). "Master only that data that adds value to the organizations and supports the achievement of business objectives. Be flexible on choosing domains - some businesses may have a specific dataset that typically is not seen as master data, but that data set drives the business operations across departments and multiple business processes. Hook master data usage and consumption to known business processes. This ensures you will get value from your investment and provides a baseline point from where ROI and data quality measures can be driven." ~ IT Manager Large (>$1B) North American Transportation / Logistics company

12 Page 12 Table 4: Master Data Benefits of the Best-in-Class Performance Metrics High* trust in master data High trust in data systems & policies Percent of records with significant errors Percent of organizations that can fix an error in <1 hour Time-to-information (year-over-year change) Time-to-decision (y-o-y change) Time to integrate new data sources (y-o-y change) Employee time spent on data-centric processes (y-o-y change) Best-in-Class Industry Average Laggard 67% 40% 12% 71% 28% 16% 5% 16% 30% 70% 53% 30% 21% reduction 13% reduction 12% reduction 17% reduction 8% reduction 7% reduction 15% reduction 3% reduction 1% increase 17% reduction 1% reduction 1% increase *"High" is defined as a 7 or higher on a scale of 1 (low) to 10 (high Source: Aberdeen Group, April 2012 The employees at top-performing organizations were 4- to 5-times more likely report high levels of trust in both the data itself but also the policies and systems they use for their daily operations. Higher trust can directly impact the weight given to reports and business decisions based on this data, and how often employees try to work around systems they feel are impeding their productivity. Furthermore, the Best-in-Class had six-times fewer significant errors in their master data systems, and were able to fix what errors did occur in a much shorter amount of time. Some back-of-the envelope math can illustrate exactly how much of an impact these errors can have. Best-in- Class companies have on average 7.4 million master data records, an error rate of 5%, and take 3.6 hours to manually identify an error and correct it in all affected systems. Laggards reported 5.2 million records, 30% error rate, and 10.6 hours to correct an error. This means that if a Best-in-Class company had a dedicated team of 10 full-time employees doing nothing but manually correcting data errors, it would take 6.7 years of work. Laggards, on the other hand, would spend eight and a half decades doing the same task. In addition to the time element, inconsistent or incorrect information in these critical records can have a direct negative impact on customer interaction, inventory levels or supply chain operations. Preventing errors in the first place can mean avoiding unforeseen costs and headaches.

13 Page 13 Finally, the top performers are seeing dramatic improvement year to year. They report decreased time to not only find business information, but also to make decisions and act on this data. They can improve their MDM system through integrating new data sources faster, and most importantly, they are streamlining the main data-centric processes used by their employees. To put this in perspective, an employee at a Laggard organization spends 8.2 hours per week searching for information as part of these business processes. If this company were to reduce this time by the Best-in-Class level of 17% per year, this would free up 70 work hours for each knowledge worker in the first year alone. Aberdeen Insights Security While not every master data domain involves sensitive information that requires extensive security measures, the protection of master data is still an important part of any MDM initiative. Regulations regarding the storage and access of Personally Identifiable Information (PII) and Confidential Information (CI) are increasing, and many countries have specific laws focused on this area. Despite this growing concern, Aberdeen's research shows that only 35% of organizations have implemented data encryption for their master data, to protect it at rest or in motion over a network. Furthermore, there was only a 5% difference between the level of adoption for the Best-in-Class and the Laggards, indicating that even the top performers need to pay more attention to this issue. In addition, only 35% of the Best-in-Class had implemented content monitoring and filtering solutions such as Data Loss Protection (DLP) to prevent unauthorized access and reduce data loss or exposure. While this number is still low, only one in five (20%) of all other companies had taken this step. For more information on issues of data security and the tools used by top performers, see Aberdeen's report DLP, The Ideal Referee: Let the Games Go On!, (November 2011). In the next chapter, we will see what the top performers are doing to achieve these gains.

14 Page 14 Chapter Two: Benchmarking Requirements for Success The selection of technology solutions and tools for managing master data is a critical step in improving and maintaining data quality. However, it is just as important to supplement these tools with training, policies and business capabilities, and to ensure that employees and management have bought-in to the importance of data discipline and governance. Case Study Large Global Beverage Company Charlie Whalen, Global Process and Solution Lead at a multi-billion dollar global beverage company, discussed the role of master data within his organization. Our organization is essentially a franchise system, with bottlers spread out all over the world. We give those business units a distinct amount of latitude and autonomy in how they want to run their business, but we also wanted to ensure some level of consistency in the data and data exchange. We tackled this problem through developing a new software platform that could be used by the entire network of bottlers, suppliers and distributors. This system provides standardized business processes methodology and master data management functionality, specifically around data governance, data maintenance and defined data standards. As a multi-domain system, it provides support for customer, material, vendor, financial and product code masters. This system has been very well received, decreasing the time to onboard new bottlers, and providing increased visibility and control throughout the supply chain. Prior to the rollout, said Whalen, forty different bottlers would have forty different names for the same product. Now there is a single standard, and we are getting a unique definition for each finished product. This allows us to finally start creating a consistent, comprehensive global catalog that can be used anywhere in the world. Since this system was implemented, the number of total stock-outs was reduced, and the average asset return was increased. The company is also looking at possible improvements to this system. The platform initially focused on the governance and maintenance of the master data domains, with high priority given to establishing that set of data standards. Now additional attention is being paid to data enablement, ensuring data is high quality prior to entering the system, and managing changes once it has been loaded. continued Fast Facts: Best-in-Class were more likely to integrate their MDM systems with other critical business applications. 72% integrated with Enterprise Resource Planning (ERP) systems, 1.3-times Laggards 63% integrated with Supply Chain Management (SCM) applications, 1.8-times Laggards 60% integrated with Customer Relationship Management (CRM) applications, 1.8-times Laggards 54% integrated with financial or accounting applications, 1.4-times Laggards 54% integrated with industry-specific applications, 1.8-times Laggards

15 Page 15 Case Study Large Global Beverage Company Furthermore, a web portal is being created as a central repository of master data best practices and techniques. Any company using the system will be able to get information on the supported master data domains, such as technical details and settings, standards, mappings, business rules and definitions. The challenge is that many people don t even know what they don t know, Whalen said. Bringing this system to their attention is the first step in getting them to buy in and adopt these improved data management practices. Whalen also discussed how new technologies are changing the way this master data can be used. With smartphones and mobile apps today, a customer can scan a barcode and directly access information about a product. If our master data is standardized, with accurate pricing information based on the market and region, and is presented in a less technical and user-friendly manner, this can be a great way to interact with our consumers. When these apps are used, we can also supplement that information with social media data and our existing customer data to get better visibility into how our products are selling in different areas and demographic segments. Whalen concluded, My advice to any organization looking to implement or improve their master data is to start with a business value proposition. Master data by itself is not a business driver, it is an enabler. Don t just improve the data for the sake of improving the data but if you want to improve your supply chain or get more accurate reports and metrics, then by all means investigate how better supplier and product data will help you. Also, make sure you don t skimp on data quality techniques and tools, and make sure you consider all the business data requirements. These data systems and processes will need to be maintained and supported, and you need to make sure you have the resources and staff available and willing to take on these tasks. Competitive Assessment Aberdeen Group analyzed the aggregated metrics of surveyed companies to determine whether their performance ranked as Best-in-Class, Industry Average, or Laggard. In addition to having common performance levels, each class also shared characteristics in five key categories: (1) process (the approaches they take to execute daily operations); (2) organization (corporate focus and collaboration among stakeholders); (3) knowledge management (contextualizing data and exposing it to key stakeholders); (4) technology (the selection of the appropriate tools and the effective deployment of those tools); and (5) performance management (the ability of the organization to measure its results to improve its business). These characteristics (identified in Table 5) serve as a guideline for best practices, and correlate directly with Best-in-Class performance across the key metrics.

16 Page 16 Table 5: The Competitive Framework Process Organization Knowledge Performance Capabilities and Enablers Best-in-Class Average Laggards Standardized training for master data system 52% 29% 13% End-user needs for data access and use collected 39% 30% 22% Executive sponsor for master data management 52% 33% 40% Cross-functional master data management team 60% 29% 38% Defined create, read, update and delete (CRUD) roles 44% 23% 12% Discovery and identification of all business data 48% 27% 28% Classification and definition of all business data 50% 32% 20% Measurement tools to track and report data quality 29% 24% 17% Return on Investment (ROI) for MDM defined and tracked 16% 5% 3% End-user's time-to-access master data tracked and measured 24% 8% 3% Source: Aberdeen Group, April 2012 Based on the findings of the Competitive Framework and interviews with end users, Aberdeen s analysis of the Best-in-Class demonstrates the support and priority given to their master data system, the increased understanding of the state of their data environment, and what is needed to meet and maintain their organizational standards for data quality. As Figure 5 shows, MDM business capabilities can be divided into four main categories. Capabilities that have high adoption by both the top and bottom performers are considered baseline. Capabilities with low adoption by all companies can be classified as emerging. When top performers are much more likely than Laggards to have implemented a capability, but the overall adoption remains low, the Best-in-Class are seen to be early adopters. Finally, areas where the top performers have high adoption, but the Laggards have low adoption defines the every-important category of Bestin-Class differentiators. "When addressing the factors that contribute to high quality data, try to follow these steps: Define Measure Analyze Improve Control Set clear accountabilities for data quality. Measure the performance of Senior Management through the use of data quality target metrics. Create data quality awareness throughout the entire company by launching a formal training program. ~ Director of Human Resources Large (>$1B) European Software company

17 Page 17 Figure 5: Best-in-Class Differentiate on Discovery and Training "Data quality reflects the level of responsibility that the business demands and delivers; if no one cares then data suffers." ~ Manager Large (>$1B) European Oil / Gas company Source: Aberdeen Group, April 2012 Baseline Master Data Capabilities The capabilities that form the basic foundation for master data success can be grouped into two main themes: Organizational buy-in. This first and foremost encompasses senior management support, especially in the form of an executive sponsor or champion for master data. Without this approval, master data initiatives won't get a green light and budget won't be allocated to support existing systems. However, buy-in must happen throughout the rest of the organization as well. Another essential component is having a cross-functional master data team, involving stakeholders from management, IT, and line-of-business. Such a group can ensure that the master data initiative is aligned with and supports larger organizational needs, and everyone understands the role they play. Understanding the existing data environment. While the Best-in-Class were 1.7 and 2.5 times more likely to have discovered and classified all their business data, respectively, over a quarter of the Laggards had taken this step as well. Knowing the state of an organization's data architecture, and which data sources are linked to critical business processes, are capabilities that are

18 Page 18 close to being Best-in-Class differentiators. After the initial discovery, however, it is also important to implement and maintain standards for data quality. Emerging Master Data Capabilities Both top and bottom performers reported having low implementation in the following areas of measurement and assessment: Assessing the needs of end-users that frequently interact with master data systems is something that only two out of five (39%) of the Best-in-Class do, and only 22% of Laggards. Understanding how these employees need to access and use this information in their day can help an MDM system to be tailored for ease-of-use, and increase overall productivity. Measuring data quality. Fewer than one in three (29%) of even the top performers have tools in place to assess the data quality of individual records or larger databases. Fewer than one in five (19%) can link issues with data quality to difficulties in key business processes (such as inventory accuracy, stock-outs, customer complaints or on-time deliveries). Without this diagnosis and impact analysis, it can be difficult to identify which master data sources need extra attention and care. Early Adoption of Master Data Capabilities Almost none of the Laggards have made progress in the following areas, but a small number of the top performers are leading the way in implementation: Measuring time-to-access. As the adage goes, you can't manage what you don't measure. A quarter (24%) of the Best-in-Class have the tools to track how long it takes their end-users to get the information they need, which allows them to identify potential bottlenecks and pain points in their data systems. A mere 3% of the Laggards reported similar capabilities. Measuring the ROI of MDM. This is a challenge for many organizations, as the improvements to efficiency and ease-of-use that MDM can provide are often subjective or "soft" measurements. However, 16% of the Best-in-Class have been able to connect their MDM investments to their business operation, define metrics to measure, and ultimately track the value they are getting. Again, only 3% of Laggards could say the same. Best-in-Class Master Data Differentiators The capabilities that show the strongest correlation with top performance are as follows: Standardized training for master data governance and master data systems. Over half of the Best-in-Class (52%) have implemented a formal process to instruct their employees on how

19 Page 19 to use their data systems and the data itself. While this might seem like an obvious step to take, the top performers were more than 4- times as likely to have such a program as Laggards. With the minimal time and cost involved in setting up basic training, this has high potential to improve the management of master data in underperforming organizations. Enforcing Create, Read, Update and Delete (CRUD) rules. There is a large difference between setting a policy, and enforcing said policy. Having the tools in place to define and restrict who has the authorization to access or change master data is a capability reported by 44% of the Best-in-Class. The Laggards are 3.7 times less likely to have this technology, which partially explains why (as shown in Table 3) only 12% reported high levels of adherence to master data policies. The Master Data Toolbox There a number of features and functions available in master data systems. Some come as part of larger end-to-end solutions, and some are available as stand-alone products. While the list of all possible features is far too long to address in this report, Aberdeen's research indicate that there are three areas where the Best-in-Class are more heavily investing, and which are contributing to their superior performance (Figure 6). Master Data Automation As the size and complexity of master data environments grows, organizations are beginning to realize that manually-intensive techniques for managing this data are not feasible in the long term. The Best-in-Class companies are quickly turning to automated solutions for many aspects of their master data system, in order to scale to meet the growing requirements. As Figure 6 shows, several of the tools that are strongly correlated with top performance include automatically capturing data from both internal and external sources, indexing and sorting this data as it enters the system, then cleansing this information to ensure high initial quality. Managing Master Data Basic data management always starts with people and policies. However, Aberdeen's research has shown that the strategic addition of tools to augment and streamline these processes directly correlate with master data success.

20 Page 20 Figure 6: Technology Supporting Best-in-Class MDM Performance Source: Aberdeen Group, April 2012 The technology enabler with the largest disparity between Best-in-Class and Laggard adoption was data enrichment. These tools are able to identify incomplete aspects of master records, and automatically locate this missing information in other data sources and add it in. Other enablers include the de-duplication of records to ensure that there aren't multiple versions of the same information, which is essential to achieving the vision of a single version of the truth. Data governance solutions allow for business rules to control who has the authorization to access types of sensitive or private data. Finally, data cleansing and data normalization tools can improve the accuracy and reliability of low quality master records, and bring them in line with company standards. Accessing Master Data The most accurate, complete master data files in the world are worthless if they sit in a database and are never used by employees. General data access capabilities such as database interfaces and query tools are used by most companies, but the top performers are still more likely to have invested in them. Collaboration tools, to allow multiple parties to access, change and contribute to a record or file, are excellent ways to bring together geographically separate divisions or remote workers. These tools

21 Page 21 can be used just by internal employees, or extend beyond the firewall to allow business partners or even customers to access select information. A more dramatic method of supporting remote or distributed workers is to provide mobile tools, bringing master data to any employee with a smartphone, tablet or laptop. Finally, adding Business Intelligence (BI) functionality on top of a master data database allows this information to be used to analyze, track and predict business activities. Best-in-Class companies were up to 7-times more likely than Laggards to have invested and installed these tools for their MDM system. Aberdeen Insights The MDM Pain Index One of the major findings of this research study was that many organizations are seeing low levels of support for their master data. However, the solution to this problem is not always large investments - rather, it is balancing the level of investment with the level of need. In fact, Aberdeen's research showed that the Best-in-Class were not always the companies with the largest MDM budgets, but they were the ones that most closely matched the level of support with their data needs. As first reported in the 2011 MDM report (March 2011), this was measured on a simple scale of 1 (low) to 10 (high) on the following aspects of an organization's MDM initiative: 1. Importance of master data to core business objectives 2. The difficulty of using master data for the average employee 3. Master data as a priority for senior management 4. Level of resource / budget support given to master data. With these four ratings, an easy formula can give an approximation of the organization's resource balance surrounding master data: (1 +2) - (3+4) Adding #1 and #2 together gives a value for the need an organization has for master data improvement. For instance, a high value indicates that master data is both important, and difficult to use. A low value indicates master data is either not important, easy to use, or both. Adding #3 and #4 gives a value for the overall backing given to master data to potentially address and remediate those negative impacts. A high value indicates master data has significant support, and a low value indicates lack of support. Finally, subtracting the second value from the first provides the gap between master data needs and current level of support. Ideally a company would perfectly match these values. The size of the gap between the two provides a pain index value, or an approximation of how much more (or less) attention and resources an organization should be spending to alleviate their master data needs. continued "There is no point in implementing a MDM system until you have the following in place: 1) Strong data governance processes 2) A culture that values master data. ~ Vice President of Human Resources Large (>$1B) European Metals / Metal Products company

22 Page 22 Aberdeen Insights The MDM Pain Index In the current study, the pain index values were: Best-in-Class: 0.2; some even had negative values, indicating organizational support exceeded their needs Industry Average: 2.7 Laggards: 4.1 The overall themes of managing master data, and the adoption rates of the various capabilities and enablers are roughly equivalent using the pain index as a proxy for the Aberdeen Maturity Class. In addition to evaluating a company's MDM maturity through the capabilities and enablers mentioned in Chapter Two, this thought exercise can provide additional insight into the state of equilibrium in their MDM strategy.

23 Page 23 Chapter Three: Recommended Actions Whether a company is trying to move its performance in managing its master data from Laggard to Industry Average, or Industry Average to Bestin-Class, the following actions will help spur the necessary performance improvements: Laggard Steps to Success Secure senior management support. No MDM initiative will get off the ground without approval from on high. Convincing management of the business value of improved data quality can be a difficult task, but getting such an executive champion for MDM is a baseline capability. Fifty-two percent (52%) of the Best-in-Class and 40% of the Laggards have such a support network in place. Implement a formal MDM initiative. Forty-five percent (45%) of all organizations still rely on spreadsheets, individual databases or separate applications to manage their master data. Having a master data plan that encompasses broad organizational goals and end-user needs is one of the first steps along the path to MDM success. Fast Facts: Not only where the Best-in- Class more likely to have an MDM system, they were more likely to have had one longer: 45% of the Best-in-Class had an MDM system for >5 years 22% of the Industry Average had an MDM system for >5 years 16% of Laggards had an MDM system for >5 years Industry Average Steps to Success Implement standardized training. This business capability was one of the main differentiators of success, being 4-times more likely to be in place at Best-in-Class companies. Simply instructing employees on data discipline and how to use master data systems can have a dramatic impact on the quality of business data. Set and enforce data policies. Whether these policies involve standardized naming conventions, who can access data, or who has the authority to change master data, organizations can benefit from them being in place. Making sure these policies stick, through tools that place boundaries on behavior, was another key differentiator for top performers. Best-in-Class Steps to Success Invest in automation. The companies that are leading the way in mature master data systems are also the ones seeing the largest growth in data volume and the complexity of their data environment. In order to scale up to meet these new demands, Best-in-Class companies are quickly turning to automated data capture, indexing and cleansing. Enable remote access. With the growing adoption of mobile technology like smartphones and tablets, it is important to provide remote and distributed employees with access to the data they need. Best-in-Class companies were 3-times and 7-times more likely