Employ Next-Generation Business Intelligence For More Insightful And Rapid Decisions

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1 A Forrester Consulting Thought Leadership Paper Commissioned By Hewlett Packard Employ Next-Generation Business Intelligence For More Insightful And Rapid Decisions October 2012

2 Table Of Contents Executive Summary... 2 Why Is BI On Top Of Everyone s Agendas?... 3 Why Do Many BI Initiatives Pose Numerous Challenges?... 5 Move Forward With Successful BI Initiatives Using A Combination Of Best Practices And Next-Generation Technologies... 7 Key Recommendations Appendix A: Methodology Appendix B: Study Demographics Appendix C: Endnotes , Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. Forrester, Technographics, Forrester Wave, RoleView, TechRadar, and Total Economic Impact are trademarks of Forrester Research, Inc. CDRFor additional information, go to [1-JLUYR7] About Forrester Consulting Forrester Consulting provides independent and objective research-based consulting to help leaders succeed in their organizations. Ranging in scope from a short strategy session to custom projects, Forrester s Consulting services connect you directly with research analysts who apply expert insight to your specific business challenges. For more information, visit Page 1

3 Executive Summary To put it bluntly, there is no management without measurement. Unless you have been asleep at the wheel, you have been using numerous variations of so-called business intelligence (BI) tools and solutions for the past 20 years. No matter what the name management information systems (MIS), reporting applications, executive information systems (EIS) or the tools custom coded, embedded into ERP or based on desktop tools like spreadsheets were, we all have been and are using them to understand where our businesses were, where our operations are today, and where our strategy and tactics will take us in the future. Multiple conversations with Forrester clients show that most firms use only 1% to 5% of the data available to them. What if that number doubled or tripled or quadrupled? The possibilities of the new insights would be virtually endless. There s a treasure chest of truly amazing examples out there, such as saving lives by real-time monitoring of millions of hospital patients data points, saving natural resources by sophisticated analysis of smart grid data, or extending single views of customers from 360 to 720 degrees by including customer interactions with their friends in social circles. But these new insights and intelligence require new approaches. The time for using one-off homegrown BI applications has passed. There s just too much complexity, too much data, and too many regulations in the modern word. In March 2012, HP commissioned Forrester Consulting to investigate and analyze the trends, patterns, and best practices in the modern BI environments. To further explore this market, Forrester developed and tested a hypothesis the combination of best practices and next-generation technologies can significantly contribute to successful BI implementations. In-depth surveys with 291 senior business and IT professionals revealed that these companies achieved only moderate success in their BI initiatives, with many reporting much-needed room for improvement. This study also confirmed findings from other extensive Forrester research that a combination of best practices and nextgeneration technologies can significantly boost the chances for delivering efficient and effective BI solutions. Key Findings Forrester s study yielded four key findings: BI is now a key corporate asset. Most enterprises have graduated from using BI as just another back-office application and now are investing in and leveraging information as a key corporate asset for competitive differentiation. The road to successful BI environments remains perilous. The majority of factors, including complexity and poor applicability of earlier-generation approaches and technologies, lead to multiple BI challenges. In most cases, companies have more chances of success when they work with an experienced, successful, trusted partner with deep and broad BI specialization. BI success can be within your reach. Enterprises can increase their chances for successful BI implementations by following best practices and leveraging next-generation technologies. Open standards are important. It s important to work with vendors that embrace open standards, not just for software, but for the entire BI infrastructure as well. For example, technologies that can improve the integration of storage and network virtualization and increase integration of the systems-level virtualization capabilities with higher level management software can provide clients with lower operating expenses, which contributes to more successful and more agile BI initiatives. Page 2

4 Why Is BI On Top Of Everyone s Agendas? Change is the only constant in the modern, fast-moving global economy. Within days a few months at most incoming C-level and all other strategic, tactical, and operational decision-makers must identify ways to improve their enterprise performance by boosting profitability, raising market share, leapfrogging competitors, and/or accomplishing key objectives. But achieving these objectives is not as simple as just looking at the numbers. What about non-financial measures (e.g., customer loyalty and employee satisfaction) that don t show up in financial accounting? How do you quickly and efficiently get the 360-degree view of your business? The answer is seemingly simple: There s no management without measurement. To execute on enterprise strategy, business and IT executives need a business-focused, strategic, and pragmatic way to measure their finances and operations. Without such measurements most often achieved by enterprisewide BI deployments businesses won t be able to link operational results to strategy (37% of survey respondents still do not use BI at an enterprise level). Organizations will also find it difficult to get a coherent view of their internal and external processes, customers, logistics, operations, and finances. Multiple conversations with Forrester clients show that most firms use only 1% to 5% of the data available to them. What if that number doubled or tripled or quadrupled? The possibilities of the new insights would be virtually endless. There s a treasure chest of truly amazing examples out there, such as saving lives; preserving natural resources; fighting crime; and improving profitability, customer satisfaction, and employee productivity all based on better and faster insights. Saving lives. A provider of healthcare BI and data solutions runs customized predictive modeling algorithms to identify those patients that could benefit most from a targeted management program. By utilizing agile DBMS technology tuned for analytics, the solution queries that used to take a day now return results within milliseconds. This significantly shortens the cycle of adjusting patient management programs, which can sometimes mean a difference between life and death. Understanding and improving customer profitability. A telephony vendor was able to improve customer service via transparent call detail record visibility into the more than 2 million connections they manage every day. Faced with customer demands that were outpacing the application, the vendor needed a database that would let customers directly query the raw CDRs (Call Data Records) for any period of time. The database had to let the data warehouse store at least two years worth of CDRs or about 1.2 billion records, each containing about 200 pieces of information and deliver responses in 10 seconds or less for 90% of queries. By implementing state-ofthe-art analytics, the vendor was able to run queries up to an order of magnitude faster and perform near realtime analysis. The new system eliminates the need for aggregate data and enables faster, more granular analysis and to cost-effectively store and query two years worth of CDRs (versus 90 days). For example, it has cut the time to perform a critical monthly cost analysis of toll-free call traffic from hours to seconds. Improving employee productivity. A pharmaceutical company uses BI for stretching its IT resources and increasing employee productivity sometimes even by an order of magnitude. This company realizes these gains because its BI platform automates the capture of critical research information and processes high volumes of data accurately with minimal ongoing maintenance required. The ability to design questionnaires quickly and easily has helped this business roll out new research projects even faster with some studies taking only a few weeks to launch. The value of accelerating the launch of a major, multiyear medical research project is significant, particularly for study sponsors in the pharmaceutical industry where blockbuster drugs can generate millions of dollars a day. Page 3

5 Expanding a 360-degree single view of a customer into 720 degrees. Studies have found that if one person defects from a product or service, other customers with social connections to that person may also. To capitalize on this study, a financial services firm is mining point-of-sale transactions at a massive scale to identify these social relationships based on card usage patterns to conduct targeted retention campaigns. As a result, it is increasing revenue and profit. But these new insights and intelligence require new approaches. The days of one-off, so-called homegrown BI applications are gone. Modern large, complex, heterogeneous, global enterprises need industrial strength BI platforms and applications to help them handle: Increasing data and content volumes. It s not just that we generate large volumes of data in our transactional applications like enterprise resource planning (ERP) as well as from smart devices like utility meters and cell phones, social media outlets like blogs and wikis, but we also replicate this data many times over. We replicate data for backup and disaster recovery, analytical applications (so that they do not interfere with the operational apps), and regulatory purposes such as the 30-year record retention requirement for US-based financial institutions. As a result, analysis that could be performed in spreadsheets or homegrown desktop-based BI applications a few years ago now requires petabyte-size data warehouses and industrial-strength BI applications. Complex regulatory reporting requirements. The number of regulations required to keep the increasingly complex global economy transparent to ensure legal compliance will continue to skyrocket. BI applications, reporting, and analytics are the key enablers to support regulatory requirements like the Sarbanes-Oxley Act (SOX), Basel III, and International Financial Reporting Standard (IFRS) in the financial services sector; the Health Insurance Portability and Accountability Act (HIPAA) and pay-for-performance in healthcare; and hydrocarbon accounting in oil and gas, transportation, and manufacturing industries. Like it or not, most enterprises have no choice but to comply and implement these tools. Increasing complexity of global operations. The days of operating in a niche market have come and gone for enterprises. Most are expanding into global markets, diversifying their products and service lines to spread the risk and increase the number of lucrative business opportunities. As these businesses grow, both organically and via M&A, it gets increasingly more challenging without enterprise-grade BI platforms for management to keep a bird s eye view on diversified, global, heterogeneous, multifaceted operations. But even more importantly, for many large enterprises, BI remains and will continue to be the last frontier of competitive differentiation. No one said it better than Walter Wriston, chairman of Citigroup in the 1980s, that: information created from a financial transaction will be more valuable than the execution of the actual transaction itself. Indeed, while a transaction occurs only once, the information about that transaction can be leveraged and reused numerous times for better insights into customer-facing processes such as sales and marketing activities and in internal processes such as capacity, product, and resource planning (see Figure 1). Page 4

6 Figure 1 The Majority Of BI Users Are Using BI For Better Insights And More Effective Decisions Base: 291 IT decision-makers Source: A commissioned study conducted by Forrester Consulting on behalf of HP, March 2012 Why Do Many BI Initiatives Pose Numerous Challenges? However, a perfectly successful BI environment with only few glitches often remains an elusive goal (see Figure 2). One of the main causes: Earlier-generation BI approaches and technologies seem to have a serious side effect a constant backlog of BI requests (30% of survey respondents reported that their BI requirements are not addressed on time). It s a classical FIFO (first in, first out) queue problem, where new BI requests come in at a higher rate than they can be addressed and fulfilled. Here are some of the reasons why: Implementing BI requires using best practices and building upon lessons learned. Using best practices and learning from past mistakes make a significantly greater contribution to successful BI implementations than technology and architecture alone, for several reasons. First, end-to-end BI architecture and implementations require closely coordinated integration efforts to put together multiple components like data sourcing, integration, modeling, metrics, queries, reports, dashboards, portals, and alerts. Second, it s tricky for anyone to define future BI requirements, as the business and regulatory climate may change significantly. Last, but not least, if you get three people in a room, you ll typically get five opinions on how best to derive, calculate, and use metrics like customer profitability. As a result, creating successful BI strategies, processes, and applications takes years of experience and, alas, learning from failed implementations. Business and IT BI stakeholders are not always perfectly aligned. Just get it done is the new mantra in the 21st century, when delaying or hesitating even for a few minutes can mean a lost deal or an unhappy client. Therefore, business users of BI applications mostly care about their clients; they need to react to the clients needs, even when and if it means getting the answers or getting the job done with whatever means they have at their disposal. And whether these means are bypassing enterprise standards well, that often takes secondary priority remember, customers first! While IT workers obviously understand and fully support the priorities of their business partners, they often have conflicting priorities, goals, and objectives, such as requirements to standardize and rationalize tools and platforms, minimize operational risk, and plan for the future. All enterprises are different. If one believes in the point made earlier that information provides a key competitive differentiating edge, then using an off-the-shelf canned BI solution would not provide that advantage. Hence, very few enterprises use packaged BI applications without at least some kind of customization Page 5

7 and modification, and many still build their BI applications from scratch custom coding (89% of survey respondents moderately or heavily customize their BI applications). BI technologies and processes have not kept pace with business realities. In the past 10 years, enterprises pretty much solved the problems that plagued typical BI implementations in the 1990s: data and information silos and unstable, poorly scalable BI technologies. But while earlier generation BI technologies have matured into industrial-strength solutions function-rich, scalable, and robust they have largely failed to address one simple, pragmatic business reality: the need for flexibility and agility. In the past few years, businesses have begun to realize that their enterprise-standard BI approaches, while suited to addressing most current business requirements, are neither flexible nor agile enough to react and adapt to information requirements that seem to change with ever-increasing speed. Additionally, on the infrastructure side, it is only in the past two years Forrester has seen a considerable churn in converged infrastructure (CI) technology. Forrester expects this pace to continue in Vendors will focus on improving the integration of storage and network virtualization and increasing integration of the systems-level virtualization capabilities with higher level management software. This combination of improved integration and lower operating expenses will benefit future analytics and BI initiatives. The BI architectural stack remains quite complex. In an average large enterprise operating several business lines in multiple regions, the number of components that need to be cobbled together to build complete end-toend BI solutions sometimes reaches a few dozen. All of these components rarely come from the same vendor, and even when they do, chance are, some of the components were recently acquired and are not seamlessly integrated. Integration challenges first start with having to extract, integrate, reconcile, and aggregate data from dozens, sometimes hundreds literally data sources (85% of survey respondents use more than 10 data sources, 48% more than 50, and 24% more than 100). Next, even if a centralized enterprise data warehouse (EDW) is part of your current and future strategy, it s always a light at the end of the tunnel, never completely finished (39% of survey respondents have four or more data warehouses/data marts using four or more different platforms). And last, but not least, using the best BI tool for each specific use case is still a prevalent strategy in many enterprises, and therefore IT has to deal with supporting and integrating multiple BI platforms (see Figure 3). All of the above contribute to one undisputable fact: BI requirements change faster than IT can keep up. Even if you create and deploy BI applications by the book, following all known best practices, it still can be an unattainable goal to enable your BI application to react on a dime to frequently changing business requirements. Whereas one can expect at least several years life span out of ERP, CRM, HR, and financial applications, with some major and minor enhancements along the way, a BI application can become outdated the day it is rolled out. Even when it takes mere weeks to design, build, and implement a BI app, that might still be too long. Just within that short period of time, the world may have changed completely. A sudden M&A event, a new competitive threat, new management structure, and new regulatory reporting requirements are but just a shortlist of the reasons why the traditional BI application s life span can be days and weeks, as opposed to months or years. Page 6

8 Figure 2 The Majority Of BI Key Stakeholders Feel That Their BI Maturity And Success Leave Room For Improvement Base: 291 Business intelligence decision-makers Source: A commissioned study conducted by Forrester Consulting on behalf of HP, March 2012 Figure 3 Adopting The Best Tool For The Job Can Trump Enterprise Standards; Most Enterprises Use Multiple BI Platforms Base: 291 Business intelligence decision-makers Source: A commissioned study conducted by Forrester Consulting on behalf of HP, March 2012 Move Forward With Successful BI Initiatives Using A Combination Of Best Practices And Next-Generation Technologies Forrester recommends a radical shift in BI strategies to make BI implementations more successful so that they can effectively support all business decisions strategic, tactical, and operational. While Forrester will never state that agility will cure all of BI s current ills, it certainly provides the most important best practices and leverages a key capability of the underlying BI technology to help close the gap that earlier-generation BI processes and technologies create. Don t misconstrue, however, Forrester s recommendation for just Agile software development methodology, which is nothing new. But Agile development by itself is not enough for BI, so Forrester also recommends adopting multiple best practices and next-generation technologies to make BI more flexible. Forrester defines Agile business intelligence as: Page 7

9 An approach that combines processes, methodologies, organizational structure, tools, and technologies that enable strategic, tactical, and operational decision-makers to be more flexible and more responsive to the fast pace of changes to business and regulatory requirements. 1 While creating a successful earlier-generation BI environment is challenging, ensuring that your Agile BI has all of the right key success factors requires significant expertise. Before charging full speed ahead into the Agile BI journey, Forrester recommends engaging a partner that has a proven track record of successful Agile BI implementations. Start By Adopting Agile BI Best Practices Start by adapting your organizational structures and enterprise culture for agility. No technology or processes can address BI challenges if a company s organizational structure and enterprise culture are not already on firm, agile ground (see Table 1). Once the organization is aligned for agility, the next step is to consider and implement Agile BI processes (see Table 2). Table 1 Agile BI Organization Best Practices Best practice Why it is important Recommendations Insist on business ownership and governance of BI. Business ownership of BI initiatives often translates into more successful BI environments. Demonstrate BI ROI to business leaders. 2 Take Forrester s BI maturity self-assessment and benchmark against peers and competitors. 3 Emphasize organization and cultural change management. Humans innately resist change. Forcing decision-makers and knowledge workers to step outside of their comfort zones e.g., using familiar BI applications like spreadsheets is a big change. Foster a culture that makes change easier: set expectations upfront, communicate often, collect feedback, etc. Make BI usage part of individual performance metrics, and even link it to compensation incentives. Decouple data preparation from data usage processes in endto-end BI cycles. Data preparation requires more planning and control than data usage; the two do not necessarily have to be tightly coupled. Create separate, loosely integrated organizational structures: put one in charge of data preparation, another in charge of data usage. 4 Emphasize IT ownership of data preparation processes rather than business ownership of data usage processes. Approach and treat front- and back-office BI requirements and users differently. Front- and back-office BI applications have different tolerance levels for risk, latency, planning, and data accuracy. Create different sets of policies and guidelines for approaching BI projects in the front and back offices. Create special policies and guidelines for approaching BI projects that span front- and back-office processes, especially untamed processes. Page 8

10 Establish hub-andspoke organizational models. Both extremes organizational/data silos or a totally centralized BI environment have multiple negative implications. Create a set of policies and guidelines that dictate which data entities (and their ownership and governance) belong in the centralized (hub) area and which belong in satellite organizations (spokes). Base these policies on multiple parameters, such as how mission critical a data entity is or whether multiple units across the enterprise share its use. Source: Forrester Research, Inc. Table 2 Agile BI Processes Best Practices Best practice Why it is important Recommendations Use a combination of top-down and bottomup approaches to BI design and applications. Neither approach is perfect: A bottom-up or horizontal approach requires building an enterprise data warehouse and then applying it to reporting and analytical applications. This is a monster effort that often takes years and has questionable ROI. A top-down or vertical approach clearly links strategy, goals, and metrics to data but often creates redundant efforts many of the same data entities need to support various metrics. Use a bottom-up approach for all data preparation processes: sourcing, extracting, integrating, cleansing, reconciling, and modeling. Use a top-down approach for all data usage processes: building reports and dashboards that link strategy to goals, goals to metrics, and metrics to data. Use Agile development methodologies. Traditional waterfall design and development methodologies are too slow and too inflexible for BI. Leverage Forrester s Agile development best practices. 5 Support the Agile development methodology with an Agile architecture and technologies. Enable BI self-service for business users. Even the best planning efforts can t predict future BI usage patterns. Implement self-service BI tools and technologies. 6 Ensure that at least 80% of all BI requirements can be implemented by business users themselves. Source: Forrester Research, Inc. Sound challenging? It is. That s why Forrester seldom recommends implementing these state-of-the-art, agile, nextgeneration BI environments on your own. As covered in the earlier section of the study, the BI journey is often daunting, with multiple perils. Unless you have accumulated dozens and even better, hundreds of best practices, lessons learned, pitfalls to avoid, we can guarantee that you will learn from your own mistakes. Having even a few BI initiatives under your belt is never enough. These hundreds of best practices are the realm of professional consultants and professional systems integrators who individually have accumulated dozens, and their firms cumulatively hundreds and thousands of these lessons (47% of survey respondents use systems integrators and 41% outsource all or parts of their BI initiatives). Page 9

11 Base Agile BI On Next-Generation BI Technologies While some of these best practices can stand on their own, others require the application of next-generation BI technologies. In the past, BI vendors and BI application developers focused on business and operational functionality and architectural robustness. In most cases, these features have become commoditized. BI practitioners now need to concentrate on next-generation technologies. Forrester categorizes these technologies as agile and defines four major subcategories of agility: automation, pervasiveness, unification, and BI without limitations. Each of these new technologies can stand on its own and is independent of the others, although some organizations have tackled them in the following order: Automated BI. First and foremost, firms need to automate BI processes and steps as fully as possible to eliminate manual work and free up valuable human resources for analysis and other value-added tasks. Unified BI. It s quite a paradox that, as BI initiatives attempt to bridge data and information silos, BI technology itself is not unified. Today, different BI tools address various BI use cases. Next-generation BI brings all of them together in a unified platform. Pervasive BI. After automation and unification, companies should address pervasiveness. How? Make enterprise BI applications available wherever and whenever strategic, tactical, and operational decision-makers need to analyze information, make decisions, and act. BI without limitations. Last, but not least, earlier-generation BI applications have too many limitations. For next-generation BI to be able to face the challenges of the modern business world a world that does not fit into nice, neat models it must operate on information without any borders or restrictions. 7 While it is beyond the scope of this study to cover all next-generation, Agile BI technologies, one deserves special attention. BI appliances preconfigured hardware/software combinations optimized and tuned for DW and BI address many of the Agile BI requirements (see Figure 4). Since procurement and deployment cycles are shortened, and long-term total cost of ownership can be reduced, BI appliances have a role in increasing BI pervasiveness. Preconfigured and integrated components help with BI automation and unification. And last, but not least, when BI appliances include next-generation BI technologies, like BI specific DBMS, they also address many of the limitations of earlier generation BI tools. 8 Figure 4 BI And DW Appliances Can Address All Four Requirements For Agile BI Base: 291 Business intelligence decision-makers Source: A commissioned study conducted by Forrester Consulting on behalf of HP, March 2012 Page 10

12 KEY RECOMMENDATIONS Based on findings in this study and other extensive Forrester client interactions (including thousands of client inquiries and numerous surveys) Forrester finds that enterprises are faced with four options when seeking to raise their BI and DW environments and initiatives to the next level. These four options include: #1 Custom coding BI applications. Unless you are indeed in the business of analytics, custom coding BI applications is best left for the professionals. There are plenty of robust and mature off-the-shelf BI and DW tools out there. You do not need to reinvent the wheel unless your BI requirements are indeed extremely unique. #2 Leveraging BI tools embedded in your ERP applications. For smaller businesses, especially those that run their entire operations on a single integrated ERP package (including financials, HR, supply chain, sales, marketing, etc.), there s probably little reason to look beyond BI tools that come embedded with these packages. But once such businesses grow, expand, and diversify, they inevitably start using multiple, often heavily customized ERP applications, frequently supplemented by multiple custom build operational apps. At some point in that growth, the attractiveness of embedded BI tools is severely undercut. #3 Integrating best-of-breed BI components. This is an option that all large and growing enterprises eventually face. Once senior management is bought into the notion of BI as a key competitive differentiator, nothing but best-of-breed will do. And if you have recruited, trained, and retained top BI talent with many successful BI initiatives under their belt it s a great place to be. However, as described in the earlier sections, integrating all of the BI components from scratch is not a quick and easy, inexpensive proposition. Forrester recommends working with a vendor that has proven success in recommending what are the right best-ofbreed components for a particular scenario/workload, since one size fits all is never an optimal path in BI. #4 Packaged BI solutions based on solution accelerators and best-of-breed BI components. Leading consultants and systems integrators with the advantage of having built multiple BI solutions across industries, business domains and regions, often package or productize collateral accumulated in such engagements into so-called solution accelerators. These often include logical and physical data models, standard source systems connectors, typical metrics, and pre-built reports and dashboards. Other characteristic accelerators may include conversion utilities (from an older BI platform to a new one) and pre-built standard test scripts. Anecdotal evidence suggests that solution accelerators can cut 20% to 50% of initial design, architect, build, and implement efforts. Forrester recommends working with a trusted service, software, and infrastructure provider with broad and deep expertise and consistent success record in BI and strong relationships with other vendors in the data center. The following table illustrates under what circumstances Forrester Research recommends these four options: Enterprise size Business complexity Effort Time to deploy Option 1 Custom coding BI applications Any Unique High Longer Option 2 Leveraging BI tools embedded in your ERP applications Small Low Low Shorter Option 3 Integrating best-of-breed BI components Option 4 Packaged BI solutions based on solution accelerators and best-of-breed BI components. Medium, large Medium, large High High Longer High Medium Shorter Page 11

13 Appendix A: Methodology In this study, Forrester conducted an online survey of 291 respondents in the United States, United Kingdom, France, China, and India to evaluate technology and best practices surrounding business intelligence (BI). Survey participants included decision-makers in information technology and business roles. Questions provided to the participants asked respondents to discuss organizational uses of BI, tools/platforms/applications related to BI, initiatives, requirements, and future plans. The study began in February 2012 and was completed in March Appendix B: Study Demographics Figure 1 Respondent Regions And Enterprise Side Base: 291 Business intelligence decision-makers Source: A commissioned study conducted by Forrester Consulting on behalf of HP, March 2012 Figure 2 Respondent Revenues And BI Roles Base: 291 Business intelligence decision-makers Source: A commissioned study conducted by Forrester Consulting on behalf of HP, March 2012 Page 12

14 Figure 3 Respondent Roles Base: 291 Business intelligence decision-makers Source: A commissioned study conducted by Forrester Consulting on behalf of HP, March 2012 Appendix C: Endnotes 1 Source: March 31, 2011, Trends 2011 And Beyond: Business Intelligence Forrester report. 2 Source: August 25, 2009, The Business Case For BI: Now More Critical Than Ever Forrester report. 3 Source: December 21, 2011, Update 2011: Forrester's BI Maturity Assessment Tool Forrester report. 4 Source: July 22, 2011, Agile Business Intelligence Solution Centers Are More Than Just Competency Centers Forrester report. 5 Source: February 8, 2012, Justify Agile With Shorter, Faster Development Forrester report. 6 Source: October 26, 2010, Empower BI Heroes With Self-Service Tools Forrester report. 7 Source: May 27, 2011, It's The Dawning Of The Age Of BI DBMS Forrester report. 8 Source: May 27, 2011, Forrester's Business Intelligence DBMS Effort Estimation Model Forrester report. Page 13

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