DQ and MDM - The Misssing Link Survey Report 2011 -...

Data Quality and MDM – The Missing Link?

An Information Difference Research Study

April 2011

Sponsored by

Data Quality and MDM – The Missing Link? 2

Copyright © 2011 The Information Difference Company Ltd. All Rights Reserved.

TABLE OF CONTENTS

EXECUTIVE SUMMARY ................................................................................................................... 4 BACKGROUND TO THE SURVEY ...................................................................................................... 6 THE APPROACH .............................................................................................................................. 6 ABOUT THE RESPONDENTS ............................................................................................................ 7 THE DATA QUALITY PERSPECTIVE ................................................................................................... 9 THE LINK WITH MASTER DATA MANAGEMENT AND DATA GOVERNANCE ..................................... 18 CONCLUSIONS ............................................................................................................................... 27

ENTERPRISES ....................................................................................................................................... 27 VENDORS ............................................................................................................................................ 28

ABOUT THE INFORMATION DIFFERENCE ....................................................................................... 29 QUESTIONNAIRE ........................................................................................................................... 30

Media Sponsors



LIST OF FIGURES Figure 1 – Respondents by Company Revenue ................................................................................................................................. 7 Figure 2 – Respondents by Job Function ............................................................................................................................................. 8 Figure 3 – Respondents by Industry Sector ....................................................................................................................................... 8 Figure 4 – Estimates of Business Data Quality ................................................................................................................................. 9 Figure 5 – Adoption of Data Quality Initiatives ............................................................................................................................ 10 Figure 6 – Difficult Data Issues to Resolve ...................................................................................................................................... 10 Figure 7 – Scope of the Data Quality Programs ........................................................................................................................... 11 Figure 8 – Focus for Data Types .......................................................................................................................................................... 12 Figure 9 – Barriers to Adoption of Data Quality Initiatives .................................................................................................... 13 Figure 10 – Preparation of a Business Case for Data Quality ................................................................................................. 15 Figure 11 – Processes used to clean and rationalize data ....................................................................................................... 16 Figure 12 – Data Quality Tools Deployed ........................................................................................................................................ 17 Figure 13 – Business Areas having Data Quality Initiatives ................................................................................................... 18 Figure 14 – Adoption of MDM Initiatives ......................................................................................................................................... 19 Figure 15 – Success of MDM Initiatives ............................................................................................................................................ 20 Figure 16 – Respondents Views on MDM and Data Quality .................................................................................................... 20 Figure 17 – Has your organization a Data Governance Program? ...................................................................................... 21 Figure 18 – Managed Data Domains ................................................................................................................................................. 22 Figure 19 – Technologies selected to support MDM initiative ............................................................................................... 23 Figure 20 – Platform vs. "Best of Breed" .......................................................................................................................................... 24



EXECUTIVE SUMMARY

Currently, master data management (MDM) and data quality are treated as separate markets, yet any MDM project has a significant data quality component. Many authors have highlighted in the media the crucial importance of data quality initiatives to ensuring the success of MDM implementations. There is, however, little information on the approach being adopted by organizations that have implemented or plan to implement MDM. At The Information Difference, we believe it is important for organizations and vendors alike to understand the current state of data quality and master data management in organizations, as well as the degree to which these areas are becoming interdependent. In particular, we explored the link between these important areas to discover how data quality is interleaved into a master data program. We also wished to gain insight into software tools selected and the available experience to date. We have therefore conducted a survey aimed at understanding better the views of businesses regarding their current data quality and MDM initiatives. Some 192 respondents from across the world completed the survey, which was conducted over the internet. 52% were from North America (including Canada), 36% from Europe and the remainder (12%) from the rest of the world. Almost two-‐thirds (61%) of the respondents were from larger organizations having annual revenues greater than US $1 billion. The results reflect a good mix of both large and smaller organizations worldwide. The key findings from the survey are summarized below: � Only some 12% of organizations considered the quality of their data to be good or better while

39% considered it to be poor or worse. Just less than half (48%) rated their data organization-‐wide as good.

� The quality of data is a widespread problem, with lack of standardization, inaccuracy and incompleteness being the three main problem areas.

� Data quality crosses multiple data domains, as evidenced by the finding that 81% of respondents consider the issue to be wider than just name and address data.

� Data quality pervades the whole organization, with the scope of 40% of data quality programs covering the entire enterprise and a further 30% ranging across one or more lines of business.

� Generally, organizations consider that data quality is integral to MDM (61%), yet in reality a third of companies have no data quality initiative. Data quality is therefore truly a missing link.

� A major concern is that organizations are not measuring the monetary cost of data quality (70% told us they do not currently do so). Consequently, they struggle in general to engage the business on the topic and find it difficult to convince business leaders that this is a key area for benefits.

� Preparing a business case is typically viewed as very challenging with only around one-‐third of organizations that have successfully done so.

� Some two-‐thirds of organizations are at least making efforts to measure and monitor the quality of their data.

� 48% of organizations already have a master data management program. � MDM programs appear to have a mixed track record, with only 24% reporting their

implementation as successful or better and a larger group viewing it as neutral. � 54% of organizations have some form of data governance program in place, suggesting that data

governance is rapidly becoming mainstream. � About a third of the organizations surveyed have no data quality functionality/tools, and

relatively few have deployed MDM and data quality across the enterprise. � At first sight, organizations tend to prefer “best of breed” (38%) to platforms (20%).



� One organization provided us with a striking example of the monetary benefits that can result from data quality initiatives. They told us that incorrect forecasting of future manpower numbers—due to poor data used to forecast individuals’ ends of contract over the next twelve months—would have resulted in them overshooting their authorized/funded manpower limits by approximately 1000 at £40K per head. Their data quality program identified the error in time and corrective action allowed them to produce a new forecast and reduce recruitment accordingly with a savings of £40 million.



BACKGROUND TO THE SURVEY

Master data management (MDM) and data quality are currently treated as separate markets, yet any MDM project has a significant data quality component. Last year we reviewed these two key markets in detail in our Data Quality and MDM Landscapes. Many authors are highlighting in the media the crucial importance of data quality initiatives when it comes to ensuring the success of MDM implementations. There is, however, scant information available on the approach being adopted by organizations that have implemented or plan to implement MDM. At The Information Difference, we believe it is important for organizations and vendors alike to understand the current state of data quality and master data management in organizations, as well as the degree to which these areas are becoming interdependent. In particular, we explored the link between these important areas to discover how data quality is interleaved into a master data program. We also wished to gain insight into software tools selected and the available experience to date. We have therefore conducted a survey, sponsored by Informatica and Talend, which was aimed at understanding better the views of businesses regarding their current data quality and MDM initiatives. In particular, we wanted to gain deeper insight into the following questions: � How satisfied are organizations with their current implementations? � How reliable is master data (customer, product, location, asset, etc.) today in large

organizations? � What steps are these organizations taking to measure this reliability, in order to improve their

master data and ensure that it stays at the highest quality level? � To what extent are organizations using master data repositories and data quality tools, to help

themselves? � How widespread are these technologies deployed within organizations, and how effective are

they? � What policies are being put in place to support MDM and data quality? � Are companies relying on data quality tools from their MDM vendors, or do they prefer “best of

breed” data quality tools? � What benefits are organizations seeing from improved master data quality?

THE APPROACH

The survey “Data Quality and MDM – The Missing Link?” was conducted over the Internet during the period March to mid-‐April 2011. The participants were selected by email invitations originating either directly from The Information Difference or from our media sponsors DAMA, DataQuality Pro, Information Management (formerly DM Review), IT-‐Director and TechTarget. Participation was also possible via a link from The Information Difference Ltd. website. The survey was mainly targeted at senior business and IT leaders worldwide, drawn from larger organizations (with revenues greater than US $1 billion annually). The participants were provided with the following information prior to completing the survey: “This survey investigates the relationships between master data management (MDM) and data quality (DQ). Many authors are highlighting in the media the crucial importance of data quality initiatives when it comes to ensuring the success of MDM implementations. There is, however, scant



information on the approach being adopted by organizations that have implemented or plan to implement MDM. In this Information Difference survey, we aim to explore the linkage between master data and data quality, and to discover how organizations are tackling this area in practice. We also wish to understand the scale, scope and success rates of master data programs. All information provided will be used in aggregate form only and will be kept strictly confidential. The survey has about 25 questions on the topic and should not take more than 10-‐15 minutes to complete. In return for a fully completed survey, you will receive a free summary of the analysis of the survey results. Additionally, your name will be entered in a prize draw and the first five winners will receive a free vendor profile (worth $495) of their choice.” The full questionnaire is appended in the section headed Questionnaire.

ABOUT THE RESPONDENTS

192 respondents from across the world completed the survey. 52% were from North America (including Canada), 36% from Europe and the remainder (12%) from the rest of the world. Almost two-‐thirds (61%) of the respondents were from larger organizations having annual revenues greater than US $1 billion. 13% were from companies having annual revenues last year greater than $50 billion, and 31% from companies with revenues greater than $10 billion. 39% of the respondents were from companies with annual revenues of less than $1 billion. The results consequently reflect a good mix of both large and smaller organizations worldwide. A detailed analysis is shown in Figure 1.

Figure 1 – Respondents by Company Revenue

Around 37% of the respondents were from a business background, with the remainder (63%) having been drawn from an IT background. 17% had job titles at the Director or General Manager level and 21% had the title of Enterprise Architect or similar. The results are presented in Figure 2.



Figure 2 – Respondents by Job Function

A wide range of industries was represented, with the largest participation (28%) drawn from the banking, insurance and financial services sector. Only some 8% came from the pharmaceuticals and health care sector and 7% from government. Perhaps surprisingly, manufacturing also accounted for only 7%. The full results are summarized in Figure 3.

Figure 3 – Respondents by Industry Sector



The analysis of the results from the survey is presented below. The questions referred to in the text are indicated as [Qn] and are set out in full in the appendix headed Questionnaire. Analysis of the results from the survey for regional dependencies, for example, comparisons between Europe and North America, did not yield any statistically significant differences or trends.

THE DATA QUALITY PERSPECTIVE

We opened the survey by asking respondents to share with us their best estimate of the quality of data across their organization [Q1, please refer to the Questionnaire section]. Only about 12% considered their data to be very good or better while 39% reported that they believed the quality of their data to be poor or worse. Just less than half (48%) viewed their data organization-‐wide as good. The results are much in line with our previous study from July 20091. At that time, about half of the respondents (51%) believed their data to be of good quality with a further 15% considering it to be very good or better. About one-‐third (32%) rated their business data quality as poor or worse. So the perception of data quality has apparently not changed too much over the past two years, despite much hype in the media and increased regulatory attention in a number of industries. The full results are shown in Figure 1.

Figure 4 – Estimates of Business Data Quality

Overall, we can conclude that there is wide belief that the quality of business data is relatively decent. We shall, however, return to this provisional conclusion later. With this background, we then asked respondents to tell us whether they currently have or are planning to have a data quality program or initiative [Q2]. Only two-‐thirds of organizations currently have a data quality initiative. A further 19% told us they plan to start a program within the coming year. Encouragingly, only 6% had no plans to address data quality. However, this means that for fully one-‐third, the data quality program is definitely a “missing link” at this time. This represents a

1 “The State of Data Quality Today”, July 2009, Information Difference Research Survey Report.



clear opportunity for the vendors and systems integrators (SIs). The full results are set out in Figure 5.

Figure 5 – Adoption of Data Quality Initiatives

This shows significant improvement upon the results from our earlier 2009 study where some 17% reported they had no plans at all to start a data quality initiative. In that study, only 37% currently had some form of data quality initiative in place. So what are the most difficult data quality issues which businesses have to resolve? We asked the respondents to share their views with us [Q3]. Note this was a multiple-‐choice question so percentages are not additive. The results are shown in Figure 6.

Figure 6 – Difficult Data Issues to Resolve



Non-‐standard, incorrect and incomplete are the most frequently cited issues with data and all scored above 60%. Interestingly, timeliness appeared to be less of an issue. About 13% listed other issues including: � Data in internal systems is disparate and not aligned. � Data is duplicated. � Data is not in line with business rules. � Legacy systems allow entering of invalid data. � Users taking ownership of the data (identifying data stewards). � Lack of field management support of data quality. � Missing data owners, missing data quality metrics, missing data metadata. � Lack of ownership to fix, i.e., budget to fix issues. � Don't have an effective manner to speak to the quality we have, to be able to pinpoint problem

areas. Interestingly, the top three problem areas cited in our earlier study were also: “Data is non-‐standard and needs to be standardized”, “Data is missing and needs to be enriched” and “Data is incorrect and needs to be corrected”. We can conclude that these still remain the main issues for most organizations. What levels or resources are organizations devoting to resolving data issues and improving the quality [Q4]? We found a wide range of resource levels (expressed as full time equivalents, FTEs) with a mean of 8.7 and median of 3. This latter is perhaps the more representative number since there was quite wide variation roughly related to the size of the organization. This appears somewhat low given the complexity of resolving data quality problems and the relatively high level of manual input required. It also is unchanged from our earlier study where the median was again reported as 3 FTE. What is (or will be) the scope of the current and planned data quality initiatives? Are they mainly focused on a small area of the organization or enterprise wide [Q5]? The results are summarized in Figure 7.

Figure 7 – Scope of the Data Quality Programs



It is encouraging to note that where there are data quality programs in place, these are quite widespread covering either the entire enterprise (40%) or several lines of business (30%). In our earlier study, around two-‐thirds of respondents planned for, or currently had, data quality activities spanning either the entire enterprise or one or more lines of business. At that time, only one-‐third was focusing the initiative across the entire enterprise. Under the heading “other” the approaches included: � As the need arises. � Evolving but starting with Finance. � Pilot phase at present. � Specific MDM domains. � By subject area. � Within one department of one business unit. So what are the key data domains that organizations need to address in terms of improving data quality? We asked respondents to share their views as to what is/will be the focus of their data quality initiative in terms of data types [Q6]. The results are set out in Figure 8.

Figure 8 – Focus for Data Types

Note that the heading “name and address data” refers specifically to customer and supplier name and address data. It is perhaps not surprising that name and address of customers and suppliers is most frequently selected. However, the 50% selecting product data and the 40% seeking support for financial data will certainly struggle in the present data quality tools market to find many tools to help them. The overall picture is one of a broad multi-‐domain requirement for managing data quality. This is in sharp contrast with the current position in the data quality software industry where most vendors are focused on the single domain of name and address data. Here is a clear wake-‐up call for many vendors in the market.



Given that most software tools (>90%) in the data quality space focus at present on name and address data, we wanted to understand further the business requirements. We therefore asked the respondents to tell us whether they considered the data quality in their organization to be mainly about name and address or whether they saw a need for data quality improvement across all data types [Q7]. A huge 81% told us that it is not only about name and address but encompasses all data types. Only 16% reported that for their organization it was mainly about name and address! This closely parallels the results from one of our earlier studies where 81% reported that their data quality is focused wider than just “name and address”. This clearly is a key area that vendors need to address urgently. So what are the main barriers to the adoption of data quality initiatives? We asked the respondents to share their views on this issue [Q8]. Their views are summarized in Figure 9.

Figure 9 – Barriers to Adoption of Data Quality Initiatives

The most frequently cited barriers were, as in our earlier studies, “It’s very difficult to present a business case” and “Management does not see this as an imperative”. It is also interesting that “No one is prepared to lead the initiative” scores highly. Perhaps this suggests that potential business leaders do not perceive data quality as trendy, sexy or career enhancing. Other barriers suggested included: � The business case exists, but putting a believable monetary value on it is tough. � Changing behavior toward the monitoring of data quality. � Company culture. � DQ needs to be seamlessly integrated to user's natural processes. � Difficult to prioritize over other initiatives. � Don't have the right balance of technology and end-‐user adoption. � Historically, no one was prepared to lead. But now, very few barriers. � IM management doesn't care. � In a bank, the task is just massive and difficult to get an overview of the situation. � Information owners. � Limited impact.



� Limited resources. � Many other initiatives take precedence. � Multi-‐year funding is required. � Need technology for profiling and metrics build. � No data governance committee (VP level). � Not enough resources available to identify and fix root causes. � Proper tools/technology to measure data quality. � The value is qualitative rather than quantitative. � Complexity of organization and no clear governance. � Different perceptions of data quality. � Multiple languages and countries. � Data works just fine for each line of business, only when attempting to analyze horizontally do

problems arise. � Defining and justifying the DQ problem has been easier compared to how to tackle the problem

as the selected data types are spread across multiple business processes, org functions and applications/stores without a significant change in the architecture.

� Somewhat amazingly, it's taken very seriously and already has significant money and support, so no barriers really.

� We have now a complicated data-‐cleansing program but we are not getting much support from the vendor on data issues.

� The first problem to solve is that of being able to measure the problem and provide visibility before it is possible to get anyone to own the problem.

It is likely that “It’s very difficult to present a business case” and “Management does not see this as an imperative” are highly related. Surely the most effective way to engage the attention of management is to show them, based upon a well-‐documented business case, that a data quality initiative will yield clear benefits. We were curious to discover how many organizations had tackled what is clearly regarded as a difficult area to address, namely, the creation of a business case. We asked respondents to share with us the position in their organization in regard to the business case [Q9]. The results are set out in Figure 10.



Figure 10 – Preparation of a Business Case for Data Quality

It is interesting to note that only 32% actually have prepared a business case. Perhaps it’s therefore not too surprising that most respondents and organizations complain that management does not see this as an imperative. Maybe this is a sound reason to develop a business case? It is also interesting that 32% of organizations combined data quality under their data governance initiative. Hopefully, their data governance initiative was supported by a sound business case. Some other observations from respondents on this topic included: � Is it impossible to talk about data quality without Data Governance? � It is on an as-‐needed basis. � We are preparing a business case for data quality. � We combined it under the umbrella of our master data management initiative. � We have pieces for a business case for data quality. � We showed potential costs avoided and revenue enhancements due to fixing poor quality data,

made a business case, and combined into data governance. � We see the need (mostly IT and a few key users), so we are currently trying to put together a

business case. � We started a customer data governance initiative—and have got as far as reporting on the

quality of the data. The next step will be passionate business people realizing that they need to do something about the data!

We believe that production of a sound business case for data quality (or in combination with a data governance initiative) is the only effective route to ensuring a successful data quality improvement program. Guidance on the creation of a business case is available from The Information Difference in the form of a recent white paper.2 We asked respondents to tell us whether the quality of the data itself was measured and monitored in their organizations [Q14]. For example, we asked whether they tracked the percentage of errors in their data. Some 37% told us that they did not measure the quality of their data. Encouragingly,

2 “The Business Case for MDM”, Andy Hayler, February 2010, The Information Difference



21% measure data quality at the departmental level and a further 37% at the enterprise level. So 58% actually measure the quality of their data in some form. This is comparable to the results from our earlier studies in which 42% reported that they had made no effort to measure or monitor the quality of their data. This latest result, while showing a modest improvement, underlines the claim that it’s hard to build a business case. Key to being able to present a sound business case is to have in place some form of monetary measurement of the cost to the organization of poor data quality. We asked respondents to tell us about the position in their organization [Q15]. Discouragingly, 70% admitted that they had no form of monetary measurement of the cost to their business of poor data. Sadly, this has not improved on our earlier results where 63% had no idea what poor data quality may be costing them. Only 23% had put measures in place.

Against this background, i.e., not measuring the quality of data and not counting the cost of poor data to the organization, it is unsurprising that organizations tell us that it is hard to produce a business case and that management does not see this area as a priority. We strongly recommend that organizations take steps to rectify this position urgently. What form of process do organizations have in place to clean, standardize, de-‐duplicate and rationalize their data? Do they have some form of automated system or is this operation carried out largely manually? We asked respondents about this [Q16]. The results are shown in Figure 11.

Figure 11 – Processes used to clean and rationalize data

Currently only 9% have an automated system for their data cleaning. The majority (48%) have a hybrid system while a further 18% have a manual system. So 75% have some form of system for data cleaning in place but 22% have nothing. This is a slight improvement over our earlier 2009 results, where 68% told us that they had some form of process in place to clean up data. It is encouraging that fully three-‐quarters of organizations would appear to be putting systems in place to clean up data.



We asked organizations to tell us which data quality tools they have deployed to support their data quality initiatives [Q17]. Note that respondents were allowed to make multiple selections so the percentages are not additive here. The first ten results are shown in Figure 12.

Figure 12 – Data Quality Tools Deployed

Respondents were able to select from a total list of some 45 data quality tools. The results should not be taken as an indication of the market share of the various vendors but as representing those tools selected and deployed by the organizations surveyed. Only the first ten results (together with the category “Other”) are shown for simplicity. The percentages selected for the remainder all fell well below the 5% cut off. The category “Other” includes mainly in-‐house developments. It is indeed surprising in this area, given the plethora of vendor products available, that in-‐house build is so popular. This may well be because of the need for multi-‐domain data quality maintenance which is unavailable from many vendors. Interestingly, the mean number of tools selected was 1.6 (median = 1) suggesting that a number of organizations have more than one tool. Around one-‐fifth had two tools. IBM and SAP, perhaps unsurprisingly given their customer base and market penetration, were most frequently deployed among the organizations surveyed. Informatica is also well represented. Interestingly, Microsoft (presumably with MSFT) appears to be a very popular choice of tool. This is difficult to reconcile with the fact that they do not as yet have a specialist data quality offering, so presumably implies some custom coding built on their technology. Lastly, we were interested to understand which parts of organizations were undertaking data quality initiatives [Q18]. The results from this multi-‐selection question are shown in Figure 13.



Figure 13 – Business Areas having Data Quality Initiatives

The results suggest that those areas of business having data quality initiatives are largely centered around business intelligence. Given their key role in business consolidation and reporting this is unsurprising. It is encouraging that marketing and CRM are clearly realizing the importance of having a data quality program. Other departments involved with data quality included: � All customer-‐facing units. � Asset Management. � Back-‐office functions. � Case Records Management. � Customer Care. � Finance/Risk/HR/IT and others. � Fundraising. � Public Works and Engineering. � Risk . � Shared Services and Procurement. � Supply Chain and Engineering.

THE LINK WITH MASTER DATA MANAGEMENT AND DATA GOVERNANCE

Turning to the link between data quality and MDM, we asked respondents to tell us whether they had a master data management program or initiative [Q10]. The responses are set out in Figure 14.



Figure 14 – Adoption of MDM Initiatives

Very encouragingly, there is a lot of MDM already in place, with 48% of organizations having an MDM initiative. A further 26% plan to start MDM with the year, with 10% following in the next three years. Only 11% noted that they had no interest in an MDM program. Clearly, MDM is gaining attention and has moved from the largely pilot stage and/or information gathering to live implementation. This is an improvement over our earlier studies3 where only 34% had adopted MDM. So how successful are these MDM implementations? We asked respondents to tell us how successful their master data management initiative has been in their view [Q11]. We show the overall results in Figure 15. Curiously, some 21% reported that they do not have a live MDM initiative, yet in the previous question [Q10] 48% claimed to have a live initiative. Perhaps this is more a reflection of the aspirations for the various organizations (possibly currently with limited scope initiatives or ones close to going live). Of more concern, however, is the observation that only 8% consider their implementation to be highly successful. Broadly, the results indicate some 26% successful, with 14% failures and a larger group reporting undecided or neutral. This is not really a very positive track record and we suggest that vendors and enterprises would do well to undertake post-‐implementation reviews of their MDM projects. It may well be that some of these initiatives lacked an effective data quality component.

3 “Styles and Architectures for MDM”, March 2009, The Information Difference Research Survey.



Figure 15 – Success of MDM Initiatives

Against this background, what are the views of respondents regarding data quality and master data management [Q12]? The results are presented as Figure 16.

Figure 16 – Respondents Views on MDM and Data Quality

Very encouragingly, a substantial 61% expressed the view that data quality and master data management are closely connected and both need to be implemented. Overwhelmingly, data



quality and MDM are perceived as being closely linked but one-‐third of the respondents [Q2] told us that they have no data quality tools or initiative in place. Here clearly lies the missing link. If we consider this result together with the conclusions from the previous question, it is clear that some MDM implementations may well lack effective data quality initiatives. This is an area to be addressed urgently by software vendors, systems integrators and enterprises. A further positive observation is that a mere 6% apparently still regard data quality and MDM as separate areas. In the past4, we have underlined the importance of implementing a data governance program alongside data quality and MDM, since these three areas need to be closely interlinked to ensure a successful program. Accordingly, we asked respondents to indicate whether they had a data governance program live in their organization [Q13]. The results are given in Figure 17.

Figure 17 – Has your organization a Data Governance Program?

Around one-‐third of organizations reported that they have a live data governance program which encompasses data quality and MDM. Overall, 54% have some form of data governance program incorporating either data quality or MDM or both. Less than one-‐third (27%) have no program or are not interested. This is an encouraging result showing that the recommended approach for delivering successful data quality and MDM programs is becoming accepted in practice. Which data domains are organizations managing under their data governance or MDM program [Q19]? We asked respondents to select from a list and also to add other domains they regarded as key. The results are shown in Figure 18. Note that this question allowed multiple selection so percentages are not additive.

4 The Information Difference Data Governance Benchmarking Survey Report, November 2010.



Figure 18 – Managed Data Domains

Unsurprisingly, customer and product top the list but the results do reflect the need for a multi-‐domain world and vendors should take note of this. Other domains listed included: � Entitlement, Contracts. � Material. � Pharmacy Benefit Plan. � Provider. � Raw and Packaging Material. � Unstructured data. � Digital assets and documents. � Subject is the data used to buy, schedule and deliver energy to our customers. It is not customer

or product related. We then asked respondents to share with us the technologies which they were using within their organizations to support their master data management initiatives [Q20]. Respondents were allowed to make multiple selections, so again here the percentages are not additive. Further, the results reflect the use of these technologies by our sample and do not necessarily indicate the general market position or share of the vendors. The results are shown in Figure 19. There is a wide spread of technologies being used to support MDM initiatives with only Oracle, IBM and SAP having become fairly established. It is interesting to note that these three are followed up by DataFlux, which in itself is really a data quality rather than MDM tool.



Figure 19 – Technologies selected to support MDM initiative

Interestingly, the mean number of tools selected was 1.1 (median = 1) with a range of 0 to 7. Around one-‐fifth had selected two tools. There is currently a split in the market with some vendors offering “best of breed” solutions while others are focused on offering a fully integrated packaged approach incorporating data quality, MDM and (in a few cases) data governance tools. We asked whether organizations were relying on the data quality tools provided by their MDM vendor or whether they preferred to choose “best of breed” [Q21]. The outcome is summarized in Figure 20. Despite the current market direction towards a platform solution (which packages data quality tools with MDM), the majority of respondents told us that they preferred the “best of breed” approach. Only one-‐fifth preferred to use the tools provided by their MDM vendor.



Figure 20 – Platform vs. "Best of Breed"

We then asked respondents to tell us what they consider to be the most important benefit that they expect or have achieved from their MDM and data quality initiative [Q22]. The main benefits reported included: � Awareness of (bad) data usages and willingness to do something about it. � Better more reliable data; more efficient processing. � Business Process Improvement. � Clean Data that can be used for forecasting. � Clean customer and product data shared across the enterprise. � Cleansed data, standard data, controlled data. � Consistent eCommerce experience for customers. � Consistent data and ability to shared data between business units. � Correct Product Content and Customer information. � DWH as a single source for investment steering and reporting. � Data Integration for Predictive Analytics. � Data quality, Enrichment, Translation. � De-‐duplication of CRM contacts and products. � ERP readiness. � Faster cash collection through efficient order management processes. � Financial return greater than 1300%. “Single version of the truth.” � Less wasted calls and cost reduction resulting in less data problems. � MDM project failed. � More consistent data across our organization, which is also of a higher quality. � More timely delivery of correct data to reduce work around, improve efficiency and reduce risk. � One source of the truth used by all reporting systems. � Public and officer safety. � Reduced compliance errors, improved business intelligence. � Reduction of scrap and rework. � Seamless global views both regionally and across enterprise. � Share products across regions (countries). � Single client view and linked data. � Standardized process and policy regarding data management. � The main benefit is taxing our customers correctly.



� Trust in information. � Being able to realize business strategy and improved customer experience. � Better decision making and improved productivity. � Better margin and cost management within organization. � Can accurately see business across the globe for all business units. � Cleansed and standardized data and business management procedures to support data

maintenance. � Cost reduction, knowing clients, fast development, more competiveness. � Customer consolidation, de-‐duplication and valid contact information. � Customer satisfaction. � Decisions supported by reliable information. � Improvement in the business process. � More effective decision making. � Reliable and credible data, cheaper and faster data maintenance. � Single version of truth for our customers. � Trust and transparency behind the information. � Single record for sharing among multiple applications, and preventing anyone from trying to

create a new master record. � To increase correctness and efficiency by maintaining one set of master data instead of many

“lists” of the same information. � Publish “fit-‐for-‐use data” to the firm; we have the data in our data warehouse but it is not

published to everyone that needs the data. Finally, we asked respondents if they had an anecdote that they were prepared to share with us which illustrated some of the business problems encountered directly resulting from poor data quality and lack of master data management [Q23]. We present a selection of the most striking examples below: � Addressing people in campaigns who are deceased! � Customer complaints of misspelling of surnames and unable to view all their accounts online. � Lawsuit for unlawful search and seizure. � Payment to vendors delayed. � Poor data quality creates a lack of confidence in the business users and impedes adoption. � Unable to identify product substitutes, overpayment of invoices, incorrect selection of suppliers. � Wrong invoices, missing rebates. � Duplicate mailings to same person. � Improper supply chain planning and execution. � Inconsistent views of the business. � Incorrect distributions and disbursements to customers. � On-‐line directories don't match printed directories. � Same product is managed several times: brought to several suppliers and stored separately even

if it is exactly the same product. � Changing a location code in one system caused a manager to be unable to see his production

anymore because data was integrated based on this location code. � Inability to assess which customers are the largest across the world because of different

variations of customer names and addresses, different language/character set, hierarchy of customer companies. Impacts where sales/marketing/support resource focus is placed.

� Gaining statistics about how many products we have or how many people work in the organization usually results in a manually driven initiative.



� Inability to provide our client base with the capability for end-‐to-‐end customer interaction views. � Incorrect forecasting of future manpower numbers due to poor data used to forecast individuals

end of contract over the next 12 months. Our data quality program identified the error in time (we would have overshot our authorized/funded manpower limits by approx. 1000 at £40K per head). Corrective action allowed us to produce a new forecast and reduce recruitment accordingly—£40M saved.

� The address is one county/municipality but we say it should be in another county/municipality. That means we apply the incorrect sales tax/franchise tax.

� In 3 systems, we have 2-‐character country codes, 3-‐character country codes, free-‐text, etc. (Example: Netherlands, Nederlanden, Holland, the Netherlands, and so on.)

� It's true that our customers don't know what to order and we don't know what to supply them. Getting it right is more down to chance than design.

� When data issues are detected by measuring tools, customers don't have to call the customer services call center and this will result in less so-‐called waste calls.

� Multiple versions of the same customer results in confusion and lack of visibility to key information.

� One account can have 4 CRM entries and 3 billing entries while being an account to not sell to due to poor credit ratings.

� Goods stopped at borders due to incorrect master data appearing in printed customs paperwork. Consequent delay in order fulfillment plus loss of face with customer. A problem made worse by LEAN and JIT warehousing.

� Incorrect and incomplete data can impact our safety, reliability, reputation, productivity and business decisions. Improving our data quality provides an environment where you can trust that data, make decisions, and take actions that will benefit your work and the company.

� Top Ten Signs of Information Integrity Issues: 1. Spreadsheet jockeys; 2. No “single version of the truth”; 3. Senior Management requests for information require intensive manual effort to respond; 4. Low return on technology investments; 5. Multiple databases or spreadsheets storing similar data; 6. No ownership of data; 7. Difficulty complying with regulatory requirements (Sarbanes-‐Oxley, IFRS, etc.); 8. Senior Management concerns about quality of information being used for decision making; 9. Internal audit concerns about quality of data; 10. Organization doesn’t understand why a particular initiative succeeded or failed.

� Great amount of manual work “sanity checking” the data. Wrong reports sent to financial authorities resulting in fines. Not possible to provide customers with a single business overview of their business with the bank.



CONCLUSIONS

Key conclusions and recommendations resulting from the survey analysis are summarized below. These have been split into two groups: those of direct relevance to enterprises and organizations considering or in the process of implementing (or who have already implemented) data quality and MDM initiatives, and those relating to the software vendors and systems integrators (SIs).

Enterprises � Only some 12% of organizations considered the quality of their data to be good or better while

39% considered it to be poor or worse. Just less than half (48%) rated their data organization wide as good.

� The quality of data is a widespread problem with lack of standardization, inaccuracy and incompleteness being the three main problem areas.

� The issue of data quality crosses multiple data domains as evidenced by the finding that 81% of respondents consider the issue to be wider than just name and address data. It also pervades the whole organization, with the scope of 40% of data quality programs covering the entire enterprise and a further 30% ranging across one or more lines of business.

� Enterprises should put pressure on vendors to develop support for data quality beyond customer name and address.

� Generally, organizations and people consider that data quality is integral to MDM (61%), yet in reality a third of companies have no data quality initiative. Data quality is therefore truly a missing link.

� A major concern is that organizations are not measuring the monetary cost of data quality (70% told us they do not currently do so). Consequently, they struggle in general to engage the business on the topic and convince business leaders that this is a key area for benefits.

� Preparing a business case is in general viewed as very difficult with only around one-‐third of organizations that have successfully done so.

� Some two-‐thirds of organizations are at least making efforts to measure and monitor the quality of their data.

� 48% of organizations already have a master data management program, which is an encouraging improvement on the results from our study in 2009 in which only 34% had implemented MDM.

� The MDM programs appear to have a mixed track record with only 24% reporting their implementation as successful or better and a larger group viewing it as neutral.

� On a positive note, 54% of organizations have some form of data governance program in place suggesting that data governance is rapidly becoming mainstream.

� We urge organizations to take steps to measure and monitor their data quality and also to cost it by putting some form of monetary value on errors directly attributable to poor data. This is an essential precursor to building a sound business case.

� Organizations should build a business case as a basis for engaging management and funding and ensuring effective leadership. Remember, senior managers are much more likely to be prepared to lead a venture that has clearly defined financial benefits (e.g., return on investment and payback period).

� Enterprises should link data quality, MDM and data governance initiatives since there is a clear and growing conviction that these three areas should not be implemented separately but are intimately linked and need to be tackled together to ensure success.

� 54% of organizations have some form of data governance initiative. We recommend linking data quality into this already accepted business area in order to gain buy-‐in.



Vendors � Given that a third of the organizations surveyed have no data quality functionality/tools, and

relatively few have deployed MDM and data quality across the enterprise, there is a world of untapped opportunity for vendors and systems integrators here.

� Vendors need to recognize that customers see data quality and MDM as linked, at least in their minds, so MDM vendors need to have integrated data quality offerings, either their own or seamless links to others suppliers.

� Very clearly the results show that data quality is multi-‐domain, which brings a world of opportunity in such areas as financial and product data, among others. The message from the respondents is clear: vendor products must support a broader range of data domains than just “name and address”.

� Vendors need to establish approaches and training materials to help educate customers about identifying the monetary cost of poor data quality in the customer organization. In our view, a vendor who does a good job here will prosper.

� We believe that providing support for data governance is going to be important to customers in the near future.

� Some vendors are proclaiming the benefits of choosing an integrated platform approach (where data quality, MDM and even data governance functionality are integrated). Our research shows at first sight that organizations tend to prefer “best of breed” (38%) over platforms (20%). While there can be clear advantages in cost and implementation terms to using a platform approach many organizations fear being “locked in” to a product which may well not meet their needs. We recommend that vendors take broader approach and while still providing a fully integrated data quality tool, also facilitate interlinking with other products selected (already) by the customer organization.

� The mixed success rate to date with MDM and data quality programs may well be about both the approach to implementation as well as the selection of suitable tools. There is a clear need here for vendors to take the initiative and identify best practice and (importantly) publicize success stories.



ABOUT THE INFORMATION DIFFERENCE

The Information Difference is an analyst firm focusing primarily on master data management (MDM), data quality and data governance. Our founders are pioneers who helped shape the MDM industry with in-‐depth global project experience. We offer detailed analysis of these industries, in-‐depth profiles of the MDM and data quality vendors, assessments of the marketplace and white papers discussing key issues and best practice. Additionally, we can offer advice on strategy, vendor selection and best practice in these areas. We carry out primary market research and can help you with MDM project justification, building the business case and return on investment.



QUESTIONNAIRE

The full questionnaire used in the survey is included below. The navigation logic is not shown in the interests of clarity.

Data Quality and MDM – The Missing Link? Introduction This survey investigates the relationships between master data management (MDM) and data quality (DQ). Many authors are highlighting in the media the crucial importance of data quality initiatives to ensuring the success of MDM implementations. There is, however, scant information on the approach being adopted by organizations that have implemented or plan to implement MDM. In this Information Difference survey we aim to explore the linkage between master data and data quality, and to discover how organizations are tackling this area in practice. We also wish to understand the scale, scope and success rates of master data programs. All information provided will be used in aggregate form only and will be kept strictly confidential. The survey has about 25 questions on the topic and should not take more than 10-‐15 minutes to complete. In return for a fully completed survey you will receive a free summary of the analysis of the survey results. Additionally your name will be entered in a prize draw and the first five winners will receive a free vendor profile (worth $495) of their choice.

Please note that questions marked with an asterisk (*) are mandatory. 1.) What is your best estimate of the quality of data across your business? ( ) Excellent ( ) Very Good ( ) Good ( ) Poor ( ) Very Poor ( ) Disastrous ( ) Don't know 2.) Do you have currently or are you planning a data quality program or initiative? ( ) We currently have a data quality initiative ( ) We have plans to start a data quality initiative within one year ( ) We have plans to start a data quality initiative within 3 years ( ) We do not plan to start a data quality initiative ( ) Don't know 3.) What are the most difficult data quality issues for you to resolve? [Please select all that apply] [ ] Data is non-‐standard and needs to be standardized [ ] Data is missing and needs to be enriched [ ] Data is incorrect and needs to be corrected [ ] Information is not timely [ ] Don't know



[ ] Other [Please specify] 4.) What annual level of resources (expressed as Full Time Equivalents or FTEs) do you currently devote across your enterprise to data quality? ____________ 5.) What is the scope of your data quality initiative? ( ) We don't have one ( ) Across the entire enterprise ( ) Across one or more lines of business ( ) Across one or more regions ( ) Don't know ( ) Other [Please specify] 6.) What is/will be the focus of your data quality initiative in terms of data types? [Please select all that apply] [ ] Name and address data (of customers or suppliers) [ ] Product data [ ] Financial data [ ] Supply chain data [ ] Human resources (HR) data [ ] Unstructured data [ ] All enterprise data [ ] Don't know [ ] Other data [Please specify] 7.) Is data quality in your organization mostly about name and address, or do you see the need for data quality improvement across all your data types? ( ) It's mainly about name and address ( ) All data types ( ) Don't know 8.) What in your view are the main barriers to adoption of data quality initiatives? [Please select all that apply] [ ] It's very difficult to present a business case [ ] The quality of our data is just fine [ ] It's difficult to identify where to find help [ ] We do not have the right skill sets [ ] Management does not see this as an imperative [ ] It would be too expensive [ ] Unrealistic expectations are often set [ ] No one is prepared to lead the initiative [ ] No one in the business seems to care [ ] Can't find suitable technology [ ] Don't know [ ] Other [Please specify]



9.) Please indicate which of the following statements best represents the current position regarding the business case for data quality in your enterprise? [Note: Data Governance is the exercise of decision-‐making and authority for data-‐related matters.] ( ) We have put together a business case for data quality ( ) We have not prepared a business case for data quality ( ) We combined it under the umbrella of our data governance initiative ( ) We believe it is too difficult to produce a business case for data quality ( ) We don't need a business case ( ) Don't know ( ) Other [Please specify] 10.) Do you have a master data management program or initiative? ( ) We currently have a master data management initiative ( ) We have plans to start a master data management initiative within one year ( ) We have plans to start a master data management initiative within 3 years ( ) We do not plan to start a master data management initiative ( ) Don't know 11.) How successful has your master data management initiative been? ( ) We do not have a master data management initiative. ( ) Highly successful ( ) Quite successful ( ) Neutral ( ) Somewhat unsuccessful ( ) Very unsuccessful ( ) Don't know 12.) Please indicate which of the following statements best represent your views on master data management (MDM) and data quality (DQ) ( ) Data quality and master data management are closely connected and both need to be implemented ( ) Data quality and master data management are separate areas and address separate issues ( ) Master data management is essentially data quality ( ) Data quality is a prerequisite for master data management ( ) You do not need both, only one or the other ( ) Master data management is only required for improving the quality of reporting ( ) We believe the two are both necessary and we would like to see more integrated offerings from vendors ( ) Don't know 13.) Do you have a data governance program running? [Note: Data Governance is the exercise of decision-‐making and authority for data-‐related matters.] ( ) No ( ) Yes and MDM is within its scope ( ) Yes and data quality is within its scope ( ) Yes and both data quality and MDM are within its scope ( ) We plan to introduce one within a year ( ) We plan to introduce one within 3 years ( ) Don't know



14.) Do you measure data quality (i.e. the quality of the data itself)? [e.g.: Do you track the percentage of errors in your data?] ( ) Yes – at the enterprise level ( ) Yes – at the department level ( ) No ( ) Don't know 15.) Do you have in place some form of monetary measurement of the cost to your organization of poor data quality? ( ) Yes. ( ) No. ( ) Don't know. 16.) Do you have processes in place to clean, standardize, de-‐duplicate and rationalize data? ( ) Yes, we have a fully automated system ( ) Yes, we have a hybrid system (part manual, part automated) ( ) Yes, we have a manual in-‐house system ( ) Yes, we have a manual outsourced system ( ) No ( ) Don't know 17.) Which vendors' data quality tools have you deployed to support your data quality initiative? [Please select all that apply] [ ] Address Doctor (now Informatica) [ ] AMB DataMiners [ ] Ataccama [ ] Business Data Quality [ ] Capscan [ ] Ciant [ ] Data Mentors [ ] Datactics [ ] Dataflux [ ] Datanomic [ ] DataQualityFirst [ ] Datiris [ ] Datras [ ] DQ Global [ ] Exeros (now MSFT) [ ] HelpIT [ ] Human Inference [ ] IBM [ ] Infogix [ ] Informatica [ ] Infosolve [ ] Innovative [ ] Inquera [ ] Intelligent Search [ ] Irion [ ] Ixsight



[ ] Melissa Data [ ] Microsoft [ ] MSI [ ] Netrics [ ] Omikron [ ] Pitney Bowes Software [ ] QAS [ ] SAP [ ] Satori Software [ ] Silver Creek Systems (now Oracle) [ ] Stalworth [ ] Talend [ ] TIQ Solutions [ ] Trillium [ ] Uniserv [ ] Winpure [ ] Wizsoft [ ] X88 [ ] Don't know [ ] Other [Please specify] 18.) Which of the following departments within your organization have data quality initiatives? [Please select all that apply] [ ] Marketing [ ] Business Intelligence [ ] CRM [ ] Data Warehouse [ ] Sales [ ] Call Centre [ ] Product Management [ ] Other [Please specify] 19.) Please select the data domains managed by your data governance or MDM program. [Please select all that apply.] [ ] Customer [ ] Product [ ] Location [ ] Asset (e.g.: fixed assets) [ ] Supplier [ ] Human Resources [ ] Financial [ ] Intellectual property [ ] Sales & marketing [ ] Research & Development [ ] All enterprise [ ] Other (Please specify) 20.) What technology are you using in support of your master data management initiative? [Please select all that apply.]



[ ] Ataccama [ ] Cadis [ ] Data Foundations (now Software AG) [ ] D&B Purisma [ ] DataFlux [ ] Global IDs [ ] Golden Source [ ] GXS [ ] Heiler [ ] Hybris [ ] IBM [ ] Information Builders (IBI) [ ] Kalido [ ] Oracle [ ] Orchestra Networks [ ] QAD [ ] Riversand [ ] Rollstream [ ] SAP [ ] Siperian (now Informatica) [ ] Smartco [ ] Sparesfinder [ ] Stibo [ ] Talend [ ] Teradata [ ] Tibco [ ] Visionware 21.) Are you relying on the data quality tools provided by your MDM vendor or do you prefer "best of breed"? ( ) We prefer to use "best of breed" ( ) We use the data quality tools provided by our MDM vendor ( ) Don't know ( ) Not applicable 22.) What is the most important benefit you either expect or have achieved from your MDM and data quality initiatives? ( ) We do not have one / not applicable ( ) Don't know ( ) The main benefit is: 23.) Have you any anecdote you would like to share illustrating the business problems directly resulting from poor data quality? [e.g.: incorrect delivery of goods, delivery of incorrect quantities of goods to customer, overpayment of invoices, …] If so please tell us about it below … ____________________________________________ ____________________________________________ ____________________________________________ ____________________________________________



24.) What was your company's total revenue last year? ( ) More than $50 billion ( ) $10 billion to $50 billion ( ) $1 billion to $10 billion ( ) $500 million to $1 billion ( ) $100 million to $500 million ( ) Less than $100 million 25.) Please select the main industry in which your company operates. ( ) Aerospace & Defense ( ) Agriculture ( ) Banking/Insurance/Financial Services ( ) Chemicals/Energy/Utilities ( ) Computing (Hardware and/or Software) ( ) Construction ( ) Education/Training ( ) Government-‐Federal/State/Local ( ) Leisure/Travel/Hospitality ( ) Manufacturing ( ) Media/Publishing/Entertainment ( ) Metals & Mining ( ) Non-‐Profit/Charitable ( ) Pharmaceuticals/Biotech/Healthcare ( ) Professional Services/Consulting ( ) Real Estate ( ) Retail ( ) Telecommunications Services ( ) Transportation Services ( ) Other 26.) Which of the following best describes your title or role in your company? ( ) CxO, SVP or other Executive Role ( ) VP, General Manager, Director ( ) CIO or VP of Information Technology ( ) Enterprise Architect or Chief Architect ( ) Other Business Title ( ) Other IT Title 27.) Are you willing to take part in a brief, confidential discussion on this topic with an Information Difference analyst? ( ) Yes ( ) No 28.) Would you be willing to share your contact information with our research sponsors in order to learn more about their products? [ ] Yes, Informatica [ ] Yes, Talend [ ] No



29.) Please provide your brief contact details: First Name: ____________________________________________ Last Name: ____________________________________________ Organization or Company: ________________________________ Email Address: __________________________________________ 30.) Please select your region: ( ) Africa ( ) Asia ( ) Australia and Oceania ( ) Central America and the Caribbean ( ) Europe ( ) Middle East and North Africa ( ) North America (including Canada) ( ) South America

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