Data Migration in the Global 2000 Research, forecasts and survey results

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2 This survey shows the market for data migration is very significant and cost and time overruns can be reduced, if not eliminated Philip Howard & Carl Potter

3 Executive summary page 1 Introduction The need to migrate data from one (or many) systems to another is a major headache for many organisations, large and small. While the need to have the right data in the right place to support new application initiatives is fundamental, getting it there on time and on budget can all too easily become a recurring nightmare upon which enterprises spend vast sums of money. Moreover, it must be borne in mind that all data migrations are undertaken as a part of broader application projects (consolidations, upgrades, application migrations and so forth); this means that data migration cost and time overruns can severely impact on the overall project. Ultimately, a failed data migration project means a failed application project. Hitherto, data migration is an area that has been ignored by industry analysts but our view going into this research is that it is time that data migration was recognised as a market in its own right. In particular, the status of those involved with data migration needs to be raised: it is often regarded as a dead-end task with no prospects. This is not conducive to effective and timely data migration and the balance needs to be redressed if higher calibre, focused and enthusiastic individuals are to be attracted to this as a discipline. In addition, the very fact that these people are not attracted to data migration means that other members of the IT department are less likely to entrust important project decisions to the staff that are doing these jobs, thus dis-empowering them even further. In other words there is a vicious circle that leads to late and overspent projects that reflect adversely on both the IT department and the business as a whole. As we shall see, there is a huge annual spend on data migration. We can think of nothing else of even a remotely comparable size that is not already regarded as a market in its own right and we believe that it is time that data migration was thought of in this way also. Indeed, we believe that it is the establishment of data migration as a market that will break the vicious circle referred to in the paragraph above. However, it is not just the establishment of a market that will bring this about. It will also require the development of best practice models and agreed methodologies that can be used to inform the process of data migration and help the establishment of data migration as a discipline in its own right. The intention of this research then, was first of all to size the data migration market and, secondly, to discover where companies were having problems with data migration, as a first step towards drawing up a best practice guide. This report presents our findings and conclusions. A separate white paper from Bloor Research, which discusses the issues involved in data migration in more depth, is being published simultaneously with this report. In so far as our approach was concerned, our research involved interviewing well over 700 of the Forbes Global 2000 companies with respect to projects wherein the overall budget for the project was $1m or more. However, of that 700+ we were only able to get detailed responses from 43 organisations (though rather more provided high-level information). This in itself deserves comment. We believe that the reason behind this low number of respondents is primarily because people do not like talking about their failures (and our research indicates that the overwhelming majority of such projects do fail to come in on time and on budget). And this always assumes that such people are still in position for us to talk to in the first place! Indeed, we applaud those brave souls amongst our respondents who were prepared to admit that their projects took three times as long as they had budgeted for, or which cost twice as much. We believe that even with this small sample size we can make realistic deductions and predictions about the data migration market. However, this has meant that we have had to normalise our results so that potential outliers do not unduly influence the results. While this is the statistically correct way to treat the data it may mean that we have under-estimated some of our results. For example, three companies had projects running that each exceeded $100m, while none of the other projects exceeded $20m. What we cannot know is whether these very large projects are typical or not but we have to assume the latter. Since there can be no downside anomalies in this particular statistic (projects had to exceed $1m) any possible skew is therefore only in one direction. Throughout our analysis we have used normalisation where appropriate, and our results are likely to be conservative as a result. This report Depending on the reader s interest he or she may wish to skip sections of this report so it is worth briefly describing the contents of this paper, which are as follows: 1. Executive summary this section introduces the report and summarises the highlights of the report. 2. Objectives/methodology describes what we set out to research and how we did it. 3. Issues with data migration discusses where data migration projects fell down and why. 4. Conclusions and recommendations with respect to data migration best practices. 5. Data migration spend forecasts discusses the size of the market and where most spending will occur. This section may be skipped by those more interested in the issues surrounding data migration as opposed to market size. 6. Appendices provide further statistical drill down on the issues discussed in sections 3 and 5. Definition of data migration You can define data migration by its characteristics: Data migration is a one-time process. This is not to say that it may not take place over an extended time period, but once it has been completed it stops. This is in contrast to many data integration tasks, which continue on an on-going basis. Data migration involves the re-structuring of data in some way: this may mean fields being merged, or formats being changed, or the data being transformed in various other ways. Note that if no-restructuring takes place then we would call this data movement. Data migration maintains consistency of usage. Here we define usage as being either operational or analytic. Data migration never takes data from one environment and puts it into the other.

4 Executive summary page 2 Data migration may or may not involve contextual change. If you are consolidating ERP applications then you are maintaining the same context, if you are implementing a CRM system for the first time then that will mean contextual change for the source data. Data migrations involving contextual change are typically more complex than those that retain the same context. In other words, data migration does not mean simply copying the data (data movement). A good analogy here is the distinction between moving (permanently) from Boston to New York versus Boston to Paris. The former is simply moving house while the latter is migration: changing culture, environment and language. Similarly, data migration is not the same as conventional ETL (extract, transform and load) processes where the usage of that data changes (from an operational environment to an analytical one) and it differs from data integration processes where the data is moved on an on-going basis. In practice, data migration is always a subset of a larger application initiative, whether that is for application consolidation, application migration, application upgrade, the implementation of master data management, outsourcing or some other purpose. Successful data migration eliminates the need to re-key any data that exists in current source systems. In addition, it will be the case that the process of data migration includes the validation and cleansing of the data to ensure that the data being migrated is correct and in the proper format for use by the new application. Key findings The current success rate for the data migration portion of projects (that is, those that were delivered on time and on budget) is just 16%. 64% of all data migration projects were delivered late and 37% were over-budget. These figures seem contradictory. We will discuss later why we believe that the latter figure is an underestimate. Where there were time overruns these averaged in excess of 40% of the total budgeted time. Where there were cost overruns these averaged in excess of 30% of the total budget. More than half of respondents that overran their budgets blamed, at least in part, a failure to properly scope the project in advance. More than two thirds of respondents that overran their timescales blamed, at least in part, a failure to properly scope the project. Only 1 in 10 companies used data profiling tools to understand their data. We suspect that there is a strong correlation between this point and the previous one. Almost 20% of respondents did not have a separate budget or time-scale for data migration as opposed to the project as a whole. Overspending on DM budgets could add an additional $562 million globally in 2007, rising to $906 million by Again, we suspect that this is an underestimate. The market for data migration The data migration (DM) market is large and growing. The total budget for DM projects in 2007 is set to break the $5 billion mark, rising to $8 billion by Note that this is a very conservative estimate: there are projects that do not have a budget that is $1m or more; and there are companies outside the Global 2000 who have major projects that involve data migration. Using the principle of the long tail (lower budgets but lots more of them) we would expect the total spend on data migration per annum to be at least double this figure. Note that these figures refer only to the total IT budgets for DM projects: they do not include any budget for the time spent by business users in enabling these projects. Figure 1: Global DM forecast and overruns Banking, Diversified Financials and Utilities are the vertical sectors predicted to drive most spend on a global basis. However, there are some variations to this picture by country and region, which we will discuss in due course. Industry Sector Banking Diversified Financials Utilities Table 1: Global DM budget forecast leading 3 industry sectors in $m: Regionally, most investment in DM projects is expected to take place in North America ahead of APAC and Western Europe. The USA is predicted to maintain its status as the leading investor in DM projects throughout the forecast period with $1.7bn in Following the United States will be Japan with $809m in 2007, this figure representing more than half of the total spend in the Asia Pacific region. The UK ($316m in 2007) will be the largest market in Europe followed by France, Germany and Italy. Summary On the basis of our findings there are two things that are clear. The first is that the market for data migration is very significant: at least $5bn in the current year and quite possibly as much as twice this. While this includes manpower, services and consulting as well as software and hardware costs it is surprising that a market of this size has no recognition as a market in its own right. We would advocate that such a market (with all of its corollaries such as standards, best practices and so on) needs to be established in order to ensure that appropriate disciplines are in place so that the second thing which is obvious from this survey that of cost and time overruns can be reduced, if not eliminated.

5 Objectives/Methodology page 3 Bloor Research has set out to discover the scope and scale of data migration (DM) projects across the Forbes Top 2000 companies. We entered into dialogue with well over 700 of these companies and detailed interviews were carried out by telephone. In this report we present our preliminary findings based on detailed discussions with 43 respondents who were all either wholly or partly responsible for managing data migration within their company. Our aim was both to form an estimate of the size of the market for data migration and to discover the key challenges faced by those involved with data migration projects. The study examined data migration projects in the context of wider enterprise application projects (see Figure 2) where the overall budget was at least $1 million (and some were measured in hundreds of millions). The projects discussed were executed mainly in North America and Europe (detailed respondent demographics are presented in Appendix A). Outsourcing Figure 2: Types of application project In so far as the size of the market is concerned, key metrics are summarised in Table 2. Average number of major application installations M & A Legacy Retirement Application Upgrade App Instance Consolidation Application Implementation completed annually Average budget allocated to DM portion ($m) Percentage of projects experiencing an overrun on DM budget Average percentage DM budget overrun % 30% Table 2: Key Metrics 2% 7% 13% 20% 0% 10% 20% 30% 40% 50% 60% 54% By overlaying the information in Table 2 on the market segmentation and geographic distribution of the Forbes top 2000 companies, we have devised a market sizing and forecast model for the DM market amongst the world s largest companies. We have used this model to generate budget forecasts for the DM market in North America and Western Europe. We have also adapted the findings of the study to produce budget estimates for Asia/Pacific (APAC), Eastern Europe and the Rest of the World (RoW). In this report we have confined our budget forecasts and estimates to the top 2000 global companies with all figures given in US dollars. Our figures represent the expected amount of money budgeted for the DM portion of large scale application projects where the overall budget is at least $1 million. Needless to say, there many smaller projects that involve data migration, as there are indeed projects within many smaller organisations. As a result the overall market for data migration will be significantly larger than the figures indicated here. Forecast estimates are given over five different geographic regions: APAC, W. Europe, E. Europe, North America and the RoW with further breakdowns for selected key countries in APAC (Japan, China/Hong Kong, S. Korea), Western Europe (UK, Germany, France) and North America (USA, Canada). We provide further segmentation by 27 industry sectors for each geographic location. Forecasts have been calculated using a constant Compound Annual Growth Rate (CAGR). We realise that in reality this will fluctuate as different vendors, such as SAP, IBM, Oracle and so forth release various upgrades to their enterprise application software. We have not attempted to model these fluctuations precisely but have assumed that they will even out over the course of the forecast period. Overall we have assumed a CAGR of 10.00% for all the forecasts and estimates in this report. This is a composite value derived from Bloor s on-going research into the enterprise applications market. Additionally we also make use of the above key metrics to provide comment in the Conclusion section as to how much money is likely to be spent unnecessarily due to project/budget over-runs.

6 Issues with data migration page 4 So far in this report we have simply quantified data migration projects on the assumption that all will run smoothly within these projects. However, our research indicates that this is very far from being the case and in this section we present the results that indicate this. Further, more detailed responses are provided in Appendix C. Project delivery We started by asking respondents about their experience of large scale projects. Only 16% of these had not experienced either time or cost overruns. 19% of projects did not have a separate budget or timescale for data migration, so these could not be measured. Of the remainder: 3% went over budget but were delivered on time. 46% went over time but not over budget. 51% went over both time and budget. The interesting question here is how you can run over time but not go over budget? There are several possible answers to this. The most obvious is that resources were diverted elsewhere either within the grander application project or to other tasks that are deemed to be more urgent. Another is that some projects have time windows for delivery: once you have overshot that window then the urgency behind the project is lost and, again, resources are diverted elsewhere. Of course, if you overshoot a time window in this way then the project could be deemed to have failed rather than simply overrun. It is also interesting to note that it is apparently much easier to exceed timescales and stay within budget than to do the reverse. Budgets and timescales We asked respondents about the size of their overall budgets and the proportion of those budgets allocated to data migration with responses as indicated in the following two charts ( DK = don t know ). $300m 2% We then drilled down into details of the budget overrun, with results as follows: Other Lack of methodology Unrealistic scoping Inexperienced Team First time project Of those that responded Other : 14% 14% 0% 10% 20% 30% 40% 50% 60% 70% Figure 5: Reasons for budget overrun 46% referred to time overruns as being a cause of their budget issues. A further 38% referred to problems that we, at least, would describe as scoping issues. Several companies mentioned problems with either using no tools or not properly understanding how to use the tools that they did have. In addition to the strong relationship between time and cost overruns, which we have already discussed, the most interesting point here is the impact of scoping. While only 14% of respondents were prepared to admit to unrealistic scoping, comments in the Other category of responses included statements such as unclear engineering requirements, the scope was not defined properly and complexity only emerged after the project started all of which we would ascribe to scoping in general. Put these together and more than half of all respondents blamed poor, inadequate or unrealistic scoping for their budget overruns. Finally, we asked respondents how much their projects overran by, with results as follows: 62% $100m $20m 7% 7% 100% $18m 2% 60% $10m 9% 50% $5-9.99m 12% 30-40% $4-5m 20-30% $3-4m 14% 10-20% $2-3m 12% 5-10% $1-2m 21% < DK 9% Other 0% 10% 1 20% 2 Figure 3: Overall budgets DK 0% 10% 1 20% 2 DK Other < 5-10% 10-20% 20-30% 30-40% 50% 60% 100% Total 24% 6% 6% 6% 24% 12% 6% 6% 6% 6% >40% 9% Figure 6: Budget overrun percentages 30-40% 20-30% 10-20% 12% 16% After the above data was normalised, it was calculated that the average percentage budget over-run was 30.42% for those companies that experienced an over-run on budget. 26% 5-10% 12% < 2% Missing 19% DK 0% 10% 1 20% 2 30% Figure 4: Data Migration portion of budget In total, 37% of DM projects experienced an over-run on budget, and the average of those over-runs was more than 30% of the DM budget. If we take this information into consideration and apply it to the overall findings about market sizing provided in section 3 of this report we find that the average global overspend due to missed budgets could amount to as much as $562 million in 2007, rising to $906 million by 2012 (bearing in

7 Issues with data migration page 5 mind that we are only talking about Global 2000 projects worth over $1m). That would represent a severe drain on corporate finance, and firms planning large scale DM projects would benefit greatly from reducing the risk of DM project over-runs. Below are shown four bar charts which quantify the expected consequences of budget over-runs in terms of increased costs globally and for each of the main geographical regions. Timescales Just 33% of respondents reported that their projects were delivered on time. Where that was not the case we wanted to explore the extent of the overruns and the cause of those overruns, the results of which are shown in the next two charts. 200% 4% 100% 7% 1, % % 11% 800 Additional cost ($m) % 20-30% 10-20% 5-10% 4% 11% 1 30% 200 DK 4% Over Budget Year Figure 7: Predicted spend over budget globally due to DM project over-runs 0% 10% 1 20% 2 30% 3 Figure 11: Extent of overruns After normalisation the average overrun as a percentage was 40.87% Inexperienced Team First time project 6% 6% Additional cost ($m) Software tool problems Poor data quality Lack of methodology Unrealistic scoping 8% 8% 11% 2 50 DK 3% Over Budget Year Other 0% 10% 1 20% 2 30% 3 40% 33% Figure 8: Predicted spend over budget in North America due to DM project over-runs Additional cost ($m) Over Budget Figure 9: Predicted spend over budget in APAC due to DM project over-runs Over Budget Figure 10: Predicted spend over budget in W. Europe due to DM project overruns Year Year Figure 12: Cause of overruns Of the Other responses to the question as to why there were time overruns 42% were associated with scoping in some way (as per the discussions in the previous section), making a total of 67%. One respondent blamed a lack of tools. Wider implications Before proceeding to discuss what went wrong it will be worthwhile to consider the wider implications of the issues that we have just discussed. As we have mentioned before, data migrations always occur within the context of broader application projects and these, in turn, take place within a wider business context. The question then becomes: what impact does a cost or time overrun on data migration have on the application project and the business? Clearly, if the data migration project fails, or technically succeeds but is found to be unacceptable by users, then the application project itself will have failed with direct costs to the business in terms of wasted effort and indirect costs with respect to wasted opportunities (and a loss of faith in IT). There may also be costs associated with not meeting customer expectations. We did not ask (because we did not think respondents would know) how problems (delays and project overruns rather than out and out failures) with the data migration impinged upon the larger project. However, we would expect data migration issues to be symptomatic of the project as a whole, not to mention the impact on the business as a whole. In this respect it is worth reflecting that relatively small overruns can have large impacts

8 Issues with data migration page 6 on the business. For example, one company that had spent many months on just such a migration project had a cutover weekend to complete the project. Unfortunately, the new implementation was not working by the end of the weekend. It was decided to persevere, iron out the problems, and perform the cutover as soon as possible. This took two weeks: not a huge overrun in either time or costs for the project per se, particularly when taken in context of the project as a whole. However, the system being migrated was the company s billing application and no invoices could be sent out for two weeks! In other words the knock-on costs of even a relatively small delay in one aspect of a project can have devastating consequences elsewhere. been in practice. In the case of collaboration within the DM project 43% said that it was not difficult or not very difficult while only 30% felt the same way about collaboration between the data and application aspects of the project. In so far as tools were concerned, the following chart shows the types in play: Other Data profiling DQ Tools 8% 10% 11% What went wrong? Application loading 13% We followed up our questions about budgets and timescales with a series of questions about scoping, methodologies, project management, the use of tools, challenges and what people would do differently next time all designed to try and discover where problems were occurring. The responses to some of these questions were more revealing than others. Some of the more interesting findings (other details can be found in Appendix C) were as follows: Almost three quarters of respondents had a formal, documented approach (methodology) that they used to support their data migration efforts. Of these, more than three quarters had been developed in-house. Given the budget and timescale failure rates already discussed this suggests either that these methodologies weren t very good or weren t adhered to. When asked if they would be interested in adopting a general-purpose best practices methodology, again about three quarters of respondents replied that they would be interested, very interested or extremely interested in such a thing. They were roughly equally split between this being delivered as consulting services, via a collaborative web site or through documentation. ETL Hand coding 0% 10% 1 20% 2 30% 3 Figure 13: Types of tools used As can be seen, hand coding is still widely used. In particular, note the low figure for data profiling. This suggests that 90% of projects do not include data profiling. How do you adequately scope a data migration project if you do not profile the data? Doing data profiling by hand will always fall short and, while data profiling tools do not represent a panacea we believe that the use of such products is one of the key elements in ensuring realistic scoping, preferably before timescales and budgets are set. 28% 30% Despite the results to other questions suggesting that scoping was an issue, more than 8 of respondents thought that their scoping activities were successful, very successful or extremely successful. This may be because respondents are taking a more narrow view of scoping than we would or it may be a case of wishful thinking. We followed this question up with one about what scoping and estimation services respondents would like to use in future projects. More than a third wanted to build in-house skills and in total almost half of the respondents were concerned with internal resources. 22% were interested in using the services of external consultants for this purpose. When we asked what people would do differently next time we got a variety of responses of which the most common centred around the use of tools, methodologies, outsourcing and data quality/analysis. Almost 2 mentioned the first of these as important in some way and a similar percentage referred to the need for data analysis, profiling and scoping. Details of the project management tools used are in Appendix C. However, when we asked which aspects of the project could have been enhanced by better project management tools the two leading responses (at approximately 20% each) were improved collaboration within the DM project and improved collaboration between the DM and overall projects. We then went on to ask how difficult these collaborations had

9 Conclusions and recommendations page 7 There are a number of major conclusions that we can draw from our results: 1. The market for data migration is very significant and growing (see next section). 2. A substantial proportion of data migration projects are delivered late and/or over budget. 3. The methodologies used for data migration are, by and large, either inadequate or not properly adhered to. 4. Project scoping and estimating is a serious issue. 5. The use and proper understanding of tools is a major concern. 6. Collaboration, both within DM projects and with the wider application project, needs to be improved. In theory there are two reasons why DM projects run over time and/or budget: either the project has not been scoped properly, so that you underestimate the time and resources required for the task in hand; or unexpected problems occur during the course of the migration. These latter problems might include such things as the entire migration team going down with flu and might also include business users changing their minds about want they want to do. However, many unexpected problems should not be unexpected and, in fact, arise, from an incomplete and inadequate understanding of the task in hand: in other words, scoping. To fully understand what is required during any data migration process you must first know about all the data sources that have to be migrated (not a trivial task: it is often the case that data is included in Excel spreadsheets or Access databases that the IT department doesn t know about) and you have to fully understand those data sources and the data within them. This requires data profiling and analysis of your data sources before timescales and budgets are set. However, only relatively few companies make use of the tools that are necessary to do this profiling and analysis. Thus, we are not surprised that so many projects overrun: we would strongly advocate the use of appropriate tools, not just at this stage but throughout the project to ensure good data quality and the transformation and movement of the data. Finally, note that across the Global 2000 there are, on average, 4.48 major application implementations completed annually. In other words, we are not simply discussing one-off projects: the techniques learnt and any tools used in any one can project can be redeployed to the next project. More generally, we believe that preventative action is the best way to ensure that data migration runs smoothly, rather than applying the reactive measure we have discussed hitherto. If data governance principles are implemented across the enterprise then that should mean that the location and nature of data across the company is properly understood, and that it is maintained with a high level of data quality. In other words, a robust data integration infrastructure will be in place. If that was the case then the problems highlighted in this survey should not recur except, perhaps, after an acquisition: in that case we would recommend the extension of data governance to the acquired organisation prior to any major migration efforts. Following on from these discussions (and based on our experience) we can make the following recommendations about what you should do prior to commencing your project: A. Preventative action: i. ii. Implement data governance Have an on-going data quality programme B. If data governance is not in place then do the following before allocating either budgets or timescales (and note that we consider it essential that data migration does have its own budget and timescales): i. Select or define a data migration methodology which will be adhered to throughout the project. ii. Ensure that you talk to all relevant parties (business and IT) to ensure that you know where all relevant data has to come from. iii. Where the same data is in multiple sources, determine which source to use. iv. Undertake gap analysis so that you understand if there is any data that you need that you do not currently have then decide where you are going to get it from. v. Profile and analyse all of your data sources (this probably means using a tool) so that you understand the scope of whatever issues you will have with the data, so that you can produce accurate forecasts about timescales and budgets. vi. Leverage tools that support your chosen methodology. vii. Whatever tools you choose to use, make sure that you are fully acquainted with them and trained in their use before beginning the project.

10 Data migration spend forecasts page 8 In this section we will provide an overview of the spend forecasts and estimates produced as a result of our research by region and industry. Detailed country forecasts are provided in Appendix A, without commentary. 3,500 3,000 2,500 9,000 8,000 7,000 6,116 6,728 7,401 8,141 DM Budget ($m) 2,000 1,500 DM Budget ($m) 1,000 DM Budget ($m) 6,000 5,000 4,000 3,000 5,055 5, NAM APAC WEurope ROW EEurope ,903 1,506 1, ,093 1,657 1, ,303 1,823 1, , ,533 2,005 1, ,786 2,205 1, , ,065 2,426 2, Figure 14: Global DM budget forecast $m: As shown in the above graph, we expect the overall sum budgeted on DM projects amongst the top 2000 global companies to be around $5,055m in 2007, rising to $8,141m by the end of This assumes a CAGR of 10.00%. Industry Sector Banking Diversified Financials Utilities Materials Oil & Gas Operations Insurance Retailing Transportation Construction Food, Drink & Tobacco Consumer Durables Telecommunications Services Capital Goods Chemicals Media Technology Hardware & Equipment Business Services & Supplies Health Care Equipment & Services Drugs & Biotechnology Household & Personal Products Hotels, Restaurants & Leisure Conglomerates Semiconductors Software & Services Food Markets Trading Companies Aerospace & Defense Grand Total ($m) 5,055 5,560 6,116 6,728 7,401 8,141 Table 3: Global DM budget forecast by industry sector $m: Figure 15: Global DM budget forecast by geographic region $m: Our forecast predicts that North America will be the biggest spender on DM projects throughout the forecast period, maintaining its lead ahead of APAC and Western Europe. In 2007 budgets for DM projects in North America are expected to top $1.9 billion compared with $1.5 billion in APAC and 1.3 billion in Western Europe. At the end of the forecast period in 2012 budget North America is expected to top $3 billion. Investment in DM projects by firms in RoW and Eastern Europe is expected to trail well behind the leading regions for the foreseeable future. Industry sector spending Most spend on DM projects is expected to be driven by the financial sector, particularly Banking and Diversified Financial services. In 2007 we expect budgeted spending in these combined sectors to reach $1.2 billion globally, rising to almost $2 billion by the end of the forecast period. Least spending is expected to be generated by Aerospace & Defence as well as Trading and Food Markets. The total budgeted spending in these three sectors in 2007 is only expected to reach $182 million, rising to $293 million by 2012.

11 Data migration spend forecasts page 9 Figure 16: Regional DM budget forecast in selected vertical sectors $m: This graph considers the contribution of selected verticals to global spend on DM projects, such as Banking, Utilities, Insurance, Oil & Gas Operations, and Telecom Services. We find that the highest proportion of budget in the banking sector will be spent in APAC followed by Western Europe and then North America. In contrast there will be proportionately higher spending in North America and W Europe in sectors such as Utilities, Insurance and Oil & Gas. 2,500 2,000 1,500 1,000 DM Budget ($m) W. Europe E. Europe , , , , , , Figure 17: European DM budget forecast $m: We expect spending on DM projects to remain at around 15 times higher in Western Europe throughout the forecast period, compared to Eastern Europe. Country UK France Germany Italy Switzerland Spain Netherlands Sweden Finland Belgium Greece Denmark Austria Norway Ireland Portugal Luxembourg Iceland Total ($m) 1,289 1,418 1,560 1,716 1,887 2,076 Table 4: Western Europe forecast DM budget by country According to our forecast model, the UK will be the highest spending W. European nation on DM activity amongst the top 2000 companies. Budgets will hit $316 million in 2007 rising to $509 million by That is at least 1.8 times higher than France, Germany and Italy. We expect the UK to maintain its lead throughout the forecast period.

12 Data migration spend forecasts page 10 Banking Diversified Financials Insurance Construction Utilities Materials Food, Drink & Tobacco Transportation Capital Goods Media Oil & Gas Operations Telecommunications Services Chemicals Consumer Durables Drugs & Biotechnology Hotels, Restaurants & Leisure Business Services & Supplies Household & Personal Products Retailing Food Markets Health Care Equipment & Services Aerospace & Defense Conglomerates Software & Services Technology Hardware & Equipment Trading Companies Semiconductors Grand Total ($m) 1,289 1,418 1,560 1,716 1,887 2,076 Table 5: Western Europe forecast DM budget by industry sector Spending on DM projects by vertical sector in W. Europe follows the global trend with Banking and Diversified Financials out in front but Insurance also provides a significant source of investment in third place. In 2007 the budget for these combined verticals is expected to reach $392 million, rising to $631 million by Spending on DM is expected to be especially weak in the semiconductor industry, rising from only $5 million in 2007 to $8 million by the end of the forecast period. Detailed forecasts for the United Kingdom, France and Germany are provided in Appendix B.

13 Data migration spend forecasts page 11 2,500 2,000 1,500 1,000 DM Estimated Budget ($m) Phillipines N. Zealand Pakistan Indonesia Thailand Singapore Malaysia China India Hong Kong Australia Taiwan S. Korea Japan ,076 1,184 1,303 Figure 18: APAC estimated DM budget by country We predict that total budgeted spend on DM projects in Asia Pacific will reach $1.5 billion in 2007, rising to $2.4 billion by Japan will account for the vast majority of expenditure with $809 million in 2007, well over half the total spend for the whole region. We expect this trend will continue to the end of the forecast period with Japan spending $1.3 billion on DM projects in 2012 out of a total spend of 2.4 billion for the region as a whole. Other prominent nations include South Korea, Taiwan, and Australia although their individual budgets will be around a sixth of what we expect in Japan.

14 Data migration spend forecasts page 12 Banking Diversified Financials Transportation Materials Consumer Durables Capital Goods Chemicals Utilities Construction Oil & Gas Operations Trading Companies Telecommunications Services Technology Hardware & Equipment Business Services & Supplies Insurance Food, Drink & Tobacco Retailing Media Semiconductors Household & Personal Products Drugs & Biotechnology Conglomerates Health Care Equipment & Services Food Markets Hotels, Restaurants & Leisure Software & Services Aerospace & Defense Grand Total ($m) 1,506 1,657 1,823 2,005 2,205 2,426 Table 6: APAC estimated DM budget by industry sector Banking and Diversified Financials are expected to be the industry sectors generating the most spend on DM projects across Asia-Pacific, particularly in Japan and Hong Kong. In 2007 these two verticals are expected to have a combined budgeted spend of $473 million. This will rise to $761 million by Aerospace & Defence is estimated to spend the least on DM projects starting from a base of $3 million in 2007 rising to only $4 million by Breakdown estimates for DM spend by country, for Japan, South Korea, and Hong Kong and China are provided in Appendix B.

15 Data migration spend forecasts page 13 3,500 3,000 2,500 2,000 1,500 DM Budget ($m) 1, Canada United States 1,751 1,927 2,119 2,331 2,564 2,821 The total budgeted spend on DM projects in the North American market is expected to reach $1.9 billion in The majority of expenditure will of course will be accounted for in the USA where we expect around 92% of DM projects to take place. The remainder will be executed in Canada (see graph at left). Figure 19: North America budget DM spend by country Banking Diversified Financials Utilities Retailing Oil & Gas Operations Insurance Health Care Equipment & Services Technology Hardware & Equipment Materials Media Food, Drink & Tobacco Business Services & Supplies Consumer Durables Transportation Chemicals Telecommunications Services Construction Household & Personal Products Drugs & Biotechnology Semiconductors Software & Services Capital Goods Food Markets Hotels, Restaurants & Leisure Conglomerates Aerospace & Defense Grand Total/$m 1,903 2,093 2,303 2,533 2,786 3,065 Table 7: North America forecast DM budget by industry sector We predict that the strongest spend on DM projects will be in the Banking and Diversified Financial Sectors, along with Utilities and Retailing. The combined budgeted spend from these verticals will reach $615 million in 2007 increasing to $991 million by the end of the forecast period. The weakest sectors are expected to be Aerospace & Defence and Conglomerates where spending will hit around $26 million each in 2007 rising to $42 million each by A detailed breakdown of the distinct figures for the United States and Canada is provided in Appendix B.

16 Data migration spend forecasts page 14 Banking Materials Oil & Gas Operations Insurance Telecommunications Services Conglomerates Diversified Financials Food, Drink & Tobacco Retailing Utilities Transportation Chemicals Technology Hardware & Equipment Media Construction Software & Services Hotels, Restaurants & Leisure Consumer Durables Household & Personal Products Drugs & Biotechnology Semiconductors Capital Goods Food Markets Aerospace & Defense Grand Total ($m) Table 8: RoW estimated DM budget by industry sector We expect that budgeted spending on DM projects in the RoW will lag well behind the main regions NAM, APAC and W. Europe for the duration of the forecast period. The total budget for DM projects is estimated to reach $278 million in 2007, increasing to $447 million by The Banking sector will be responsible for the highest proportion of budget with $54 million in Other sectors acting as significant drivers of spend include Materials, Oil & Gas, Insurance and Telecoms. Together these top five sectors will account for $234 million by the end of 2112.

17 Appendix A respondent demographics page 15 Switzerland USA/Belgium US/UK Holland /France 2% Global Holland Belgium Canada Germany France UK USA 7% 7% 9% 12% 14% 37% 0% 10% 1 20% 2 30% 3 40% Figure 20: Geographical spread showing where the application projects covered by this survey were implemented Figure 21: Industry sectors covered by this survey

18 Appendix B Total spend projections by region page 16 Figure 22: North American DM forecast and overruns Figure 23: Asia/Pacific DM forecast and overruns

19 Appendix B Total spend projections by region page 17 Figure 24: West Europe DM forecast and overruns

20 Appendix C Forecasts and estimates by country page 18 Diversified Financials Utilities Food, Drink & Tobacco Retailing Hotels, Restaurants & Leisure Banking Construction Materials Media Insurance Business Services & Supplies Transportation Telecommunications Services Drugs & Biotechnology Food Markets Oil & Gas Operations Chemicals Health Care Equipment & Services Aerospace & Defense Conglomerates Trading Companies Capital Goods Consumer Durables Household & Personal Products Software & Services Grand Total/$m Table 9: United Kingdom forecast DM budget by industry sector Construction Diversified Financials Banking Consumer Durables Utilities Insurance Transportation Materials Media Capital Goods Household & Personal Products Technology Hardware & Equipment Food, Drink & Tobacco Hotels, Restaurants & Leisure Oil & Gas Operations Chemicals Aerospace & Defense /contd.

21 Appendix C Forecasts and estimates by country page 19 Software & Services Retailing Business Services & Supplies Telecommunications Services Drugs & Biotechnology Food Markets Health Care Equipment & Services Trading Companies Grand Total/$m Table 10: France forecast DM budget by industry sector Diversified Financials Banking Consumer Durables Utilities Insurance Capital Goods Household & Personal Products Chemicals Construction Transportation Drugs & Biotechnology Materials Food, Drink & Tobacco Health Care Equipment & Services Media Hotels, Restaurants & Leisure Software & Services Retailing Telecommunications Services Food Markets Conglomerates Semiconductors Grand Total ($m) Table 11: Germany forecast DM budget by industry sector Banking Diversified Financials Consumer Durables Capital Goods Transportation Construction Chemicals /contd.

22 Appendix C Forecasts and estimates by country page 20 Trading Companies Materials Business Services & Supplies Utilities Retailing Insurance Food, Drink & Tobacco Media Technology Hardware & Equipment Household & Personal Products Drugs & Biotechnology Oil & Gas Operations Health Care Equipment & Services Semiconductors Software & Services Telecommunications Services Conglomerates Food Markets Grand Total/$m Table 12: Japan estimated DM budget by industry sector Banking Diversified Financials Transportation Materials Utilities Telecommunications Services Insurance Oil & Gas Operations Conglomerates Capital Goods Trading Companies Chemicals Consumer Durables Construction Food Markets Retailing Grand Total ($m) Table 13: Hong-Kong & China estimated DM budget by industry sector