Business Intelligence for Small Enterprises

Transcription

1 THE ROYAL INSTITUTE OF TECHNOLOGY Business Intelligence for Small Enterprises An Open Source Approach Rustam Aliyev May 2008 Master thesis at the Department of Computer and Systems Sciences at the Stockholm University / Royal Institute of Technology (corresponding to 20 weeks of full-time work).

2 Abstract. During the last decade, Business Intelligence (BI) became inevitable technological advantage of the large enterprises which could afford to buy, implement and maintain BI solutions. These days, small size enterprises which form 98% of all enterprises in the EU have realised competitive and financial benefits of the BI. However, limited IT budgets of small companies and BI s high TCO (total cost of ownership) may cause power gap between large and small enterprises persists to enlarge where latter will find it increasingly difficult to compete. This paper explores open source (OS) approach to BI, whether OS could be an alternative to the commercial solutions, and more important whether OS BI provides cost saving. It defines and evaluates grounds that used for comparison of OS and commercial BI solutions. Keywords: business intelligence, open source, small enterprise, SME, cost-benefit analysis. ii

3 Acknowledgements. I would like to thank Ms. Maria Berghotlz for her tremendous support and assistance in the preparation of this thesis. In addition, special thanks are due to Mr. George Hodosi, whose comments were very important. I also thank Mr. Johan Dahlin and Mr. Fatih Poyraz for their invaluable input. iii

4 Table of Contents 1 INTRODUCTION BACKGROUND BUSINESS INTELLIGENCE NATURE OF SMALL ENTERPRISES RESEARCH PROBLEM RESEARCH GOAL RESEARCH METHOD LIMITATIONS EXTENDED BACKGROUND OPEN SOURCE SOFTWARE INFORMATION SYSTEM EVALUATION VALUING BI SYSTEMS IDENTIFYING BI COSTS IDENTIFYING BI BENEFITS THROUGH DECOMPOSITION IDENTIFYING BI BENEFITS THROUGH ITS CHARACTERISTICS BI Functionalities BI Solution Maturity RESULTS AND FINDINGS COMMERCIAL BI SUITES OPEN SOURCE BI SUITES INTERVIEW AND SURVEY OF BI CONSULTING COMPANIES EXPERIMENTAL SETUP System Overview Setup Results iv

5 5 ANALYSIS AND DISCUSSION COMPARISON OF BENEFITS COMPARISON OF COSTS CONCLUSION FUTURE RESEARCH BIBLIOGRAPHY APPENDICES APPENDIX A. RAWAT S CRITERIA FOR ASSESSING OS BI... A-1 APPENDIX B. SURVEY QUESTIONS... B-2 APPENDIX C. INTERVIEW AND SURVEY RESULTS... C-4 APPENDIX D. BUSINESS CASE DESCRIPTION... D-5 v

6 List of Figures FIGURE 1.1. BUSINESS INTELLIGENCE DATA LIFECYCLE... 2 FIGURE 3.1. CALCULATING NET BENEFITS, ADOPTED FROM PENG MODEL... 9 FIGURE 3.2. TYPICAL COSTS FOR BI IMPLEMENTATION PROJECT FIGURE 3.3. QUADRANT GRAPH FOR GENERIC BI BENEFITS CLASSIFICATION FIGURE 3.4. CRITERIA CATEGORIES FOR BI SUITE COMPARISON FIGURE 4.1. ENTERPRISE TYPES MAINLY TARGETED BY THE BI CONSULTING COMPANIES FIGURE 4.2. CUSTOMERS OF THE BI CONSULTING COMPANIES FIGURE 4.3. COMPLETE BI SOLUTION FUNCTIONALITIES FIGURE 4.4. AVERAGE COSTS OF THE COMMERCIAL BI SUITE IMPLEMENTATION FIGURE 4.5. DATA FLOW AND COMPONENTS OF THE EXPERIMENTAL SYSTEM FIGURE 4.6. SAMPLE TRANSFORMATION AND JOB IN PENTAHO DATA INTEGRATION FIGURE 5.1. TCO OF THE COMMERCIAL AND OS BI OVER A FIVE-YEAR PERIOD FIGURE D.1. DOMAIN MODEL OF OPERATIONAL DATABASE... D-5 List of Tables TABLE 2.1. COMPARING OPEN SOURCE SOFTWARE LICENSE TYPES... 7 TABLE 2.2. COMPARISON BETWEEN EXISTING BENEFIT MEASUREMENT MODELS... 8 TABLE 3.1. BI FUNCTIONALITIES CRITERIA GROUP FOR BI EVALUATION TABLE 3.2. SOLUTION MATURITY CRITERIA GROUP FOR BI EVALUATION TABLE 4.1. COMPARISON RESULTS OF THE COMMERCIAL BI SUITES TABLE 4.2. COMPARISON RESULTS OF OS BI SUITES TABLE 4.3. ACQUISITION COSTS FOR EXPERIMENTAL SETUP TABLE 5.1. COMPARISON OF BENEFIT COMPONENTS FOR THE COMMERCIAL AND OS BI TABLE 5.2. COMPARISON OF COST COMPONENTS FOR THE COMMERCIAL AND OS BI vi

7 1 Introduction 1.1 Background Information is considered the most valuable asset of any organization regardless of the size of that organization. Every operation that organizations perform generates lots of raw data. For instance, a simple sale of any product could generate huge amounts of data, like date of sale, price, discount, customer name, address, other demographic details like age, gender, which sales representative sold the product, when the product was manufactured, raw materials, supplier information, and so on. This raw data has to be converted into useful information for the decision makers in order to improve performance of the organization. Considering the fact that there are numbers of different business processes within any organization, there is a definite need of a sophisticated information system. Secondly, the availability of the right information on the right time to the right person is another most challenging goal for any organization. Information systems researchers and technologists have built and investigated different decision support systems (DSS) for approximately 40 years. History of developments in this area began with data collection techniques in the late 1960s. Later, in the 1970s, there were theory developments and the implementation of financial planning systems followed by spreadsheet DSS in the early and mid 1980s. At that point what the system provided was simple data collection that involved historical data referred to as information discovery. Ever-increasing amounts of data resulted in the creation of Data Warehouses, Executive Information Systems (EIS), OLAP (Online Analytical Processing) and Business Intelligence (BI) in the late 1980s and early 1990s. Finally, the chronicle ends with knowledge discovery techniques such as data mining, knowledge-driven DSS and the implementation of Web-based DSS in the mid-1990s (Power 2007). 1.2 Business Intelligence There exist many professional definitions of BI; however none of them is a standard. Business Intelligence is rather an umbrella term for a broad category of applications and technologies for gathering, storing, analyzing, and providing access to the data (Turban, et al. 2006, 423). Definitions usually encompass personal and group DSS, EIS, data warehousing, and knowledge management systems. 1

8 Data Sources Data Storage Data Analysis Results Internal Data External Data Personal Data Data Warehouse Metadata Data Marts Data Marts OLAP, Queries, EIS, DSS Data Mining Data Visualisation Decision Support Knowledge and its Management Figure 1.1. Business Intelligence data lifecycle, adopted from (Turban, et al. 2006, 410) In Business Intelligence, transformation of data into information and knowledge is generally accomplished through the process depicted on figure 1.1. It starts with raw data extraction from sources. These data are transformed into new formats (usually defined according to metadata) and loaded into data warehouses or data marts. These first two steps are usually served by ETL (extraction, transformation, load) software. Analysis tools access the warehouse and data marts to get pieces of data they need. The analysis is done with data analysis and mining tools which look for patterns, and with intelligent systems, which support data integration. Finally, the result of these activities are presented to decision makers using different visualisation techniques or stored in organization s knowledge base (Turban, et al. 2006, 410). Throughout this thesis work we refer to the components mentioned above as a BI components or BI functionalities. Software product which contains a number of the BI components is referred as BI suite, BI system or BI solution. Finally, by BI implementation project (sometimes simply BI project or BI implementation) we consider complete BI implementation life-cycle from business case assessment to production release and maintenance. According to Gartner s (2007) annual survey of 1400 CIOs worldwide business intelligence became no.1 technology priority in It is important to mention that BI is a multi billion dollar market dominated by giant vendors such as Oracle, SAP, SAS and Microsoft. Another recent trend in the BI area has been observed in the adoption of open source software. In the past few years a number of open source players have entered the BI market. With the success of open source business models, many commercial organisations have elaborated strategies to capitalize on them (Lerner and Tirole 2000). A very recent research conducted by Ventana (Ventana 2006) shows that 83% of organizations are considering, are in the process of deploying, or have already implemented an open source BI solution. 2

9 1.3 Nature of Small Enterprises According to the European Commission (EC) definition (EC 2003) small business are defined as an enterprises employing less than 50 people and having less than 10M annual turnover or annual balance sheet total. At the same time, small enterprises represent 98% of all enterprises in the EU and employ 40-65% of the workers in the private sector of the Member States (EC 2007). Consequently, small enterprises can be considered as economically and socially important players in the EU countries. Another EC report (EC 2006) states that only about 10% of small firms used specific ICT (Information and Communications Technology) solutions for marketing, sales and procurement compared with 20% of medium-sized and almost 30% of large firms. The same report says: SMEs (Small and Medium Enterprises) still suffer from limited understanding of ICTs and their potential, limited budget for ICT investments and difficulty in recruiting ICT professionals. Even though the majority of small businesses use ICT solutions daily, this usage usually engage mainly internet and access. Very few small enterprises use computers in decision support roles (Gibson and Arnott 2003). 1.4 Research Problem There are many considerations and risks that organizations have to evaluate before the adoption of BI solutions and the most important factor is the cost. This is due to the fact that BI solutions are very expensive, both to purchase and maintain (Raden 2007). Although, there is no exact figure because the implementation of BI depends upon number of factors like number of end users, functionalities, and so on. In fact, mostly large organizations can afford BI implementation where the investment cost could be millions of dollars and still fit in budget. This huge amount of investment is certainly beyond the reach of medium and especially small size organizations. According to Gibson and Arnott (2003), small businesses are often faced with limited access of finances to support the purchase of business intelligence. Now, on one hand, we have a large number of small businesses who cannot afford implementation of expensive BI solutions from industry leaders. On the other hand, these BI industry giants cannot offer entry level solutions without changing their business models. The exploitation of BI technology is important in the development of the small enterprise sector. In their research, Gibson and Arnott (2003) concluded that: If large organizations are going to continue to exploit the latest decision-supporting 3

10 technologies, and small businesses continue to tread wearily in terms of adopting modern business intelligence, the power gap will only continue to widen. If the power differential between large and small businesses persists to enlarge, small businesses will find it increasingly difficult to compete in a modern economy with resulting significant social and economic destabilization. This is to say that there is a strong need to develop an understanding of opportunities which open source Business Intelligence can bring to small businesses. 1.5 Research Goal The purpose of this research is to find out whether an open source (OS) BI suites can facilitate for small enterprises to remain competitive. We will consider that OS BI can be an alternative for small enterprises if the questions below answered positively: 1. Does an OS BI provide enough business value for small enterprises to be considered as an alternative to the commercial BI suites? 2. Does an OS BI solution provide cost savings in comparison with commercial BI suites? 3. Does an OS BI actually fit into small businesses ICT budget? 1.6 Research Method A comparative analysis of open source and commercial BI suites has been chosen as a primary method in order to reach the goal and answer the questions stated in section 1.5. To answer first and second questions we used divisive ("top-down") approach to find grounds for the comparison of a BI suites value. Thus, the top business criterion the business value of the BI solution is divided into smaller clusters unless the level of measurable and comparable criteria is reached (for instance, we cannot estimate total cost of the system as an atomic criterion while one of its sub-criterion license fee can be easily evaluated). Based on a number of investigated bottom level criteria the most comprehensive OS BI suite is chosen for further business value evaluations and comparisons with the value of commercial BI suites. To gather background information for the comparison, extensive literature review of the latest on-going trends related to BI and business value evaluation methods were conducted; published information about the BI products and formal BI system documentation were also consulted. 4

11 This research project also used the most common qualitative research method employed in information systems research, the case study. An experimental setup of a chosen OS BI within a real small enterprise was used to provide complementary information to answer research questions 1, 2 and 3 of section 1.5. Finally, a number of BI integrators and solution providers were interviewed in order to capture an average business value of a commercial and an open source BI suites. Interviewees were asked to answer generic questions regarding BI market as well as provide cost estimation for the case study mentioned above. In order to increase accuracy of the results, more companies have been approached through the webbased survey form where case study cost estimations were not included. Interview and survey results mainly address questions 1 and 2, as well as helping to answer question Limitations We already mentioned that a Business Intelligence implementation is a very time consuming initiative and includes many steps of data analysis and design. Thus, due to lack of time and resources the experimental setup provided in this paper has been limited to only one business process of one small enterprise. Medium and even large enterprises could also benefit from OS BI. However, we did not include these types of enterprises in the scope of our research. Yet another limitation is BI components. As it has been mentioned above, BI is an umbrella term for a broad category of applications. In our case we identified broadly accepted components and defined subjective BI scope. 5

12 2 Extended background 2.1 Open Source Software Open Source Software (OSS) is primarily defined as software which is freely redistributable and includes the source code (Varner 1999). This is vastly different from the mainstream software industry where source code is highly guarded and programs are only distributed in their binary, non-modifiable format. The development process of OSS also differs as it involves large number of software developers at many different locations and organizations sharing code to develop and refine software programs. While the attention of businesses to the phenomenon of OSS has been recent, the basic behaviours are much older in their origins. According to Lerner and Tirole (2000), the tradition of sharing and cooperation in software development began in early 1960s mainly with cooperative development efforts of the UNIX operating system where programmers in different organizations shared source code. The General Public License (GPL) was a noticeable innovation introduced by the Free Software Foundation (FSF) in response to intellectual property right enforcements by commercial companies in 80s. Users had to agree to make the source code freely available and not to impose licensing restrictions on others, in exchange for being able to use and modify GPL software. The widespread diffusion of Internet access in the early 1990s led to a dramatic acceleration of open source activity. As we will mention below, interactions between commercial companies and the open source community also became commonplace in the 1990s. These interactions created demand to bundle the cooperatively developed software with proprietary code and led to adoption of more flexible licenses. The licenses under which OSS is released today vary greatly, but two points that we mentioned in the beginning (freely redistributable and available source code) remain consistent. Some of these licenses and their characteristics are shown in table 2.1. In the past years growing interest has brought large market diffusion and capital investment to open source software. A number of open source products, such as the Apache web server, dominate in their product category, while major corporations, including Hewlett Packard, IBM, Sun and Microsoft have lunched own open source projects (Lerner and Tirole 2000). Today, major enterprises are running mission-critical functions on open source, big vendors have lined up to support it, and reliable applications have emerged. In a survey of 375 information executives, 54 percent said that within five years open source would be their dominant server platform. 59 percent of respondents said a 6

13 lower total-cost-of-ownership (TCO) is open source s primary strength and those who have implemented it confirm huge TCO reductions (Koch 2003). License Type Code protected by copyright? Can code be used in Closed Source Project? Can project that uses code, be sold? Must Source Code be released? Provides for patents? Public Domain No Yes Yes No No BSD/MIT Yes Yes Yes No No GPL (v2) Yes No No Yes No LGPL Yes Yes Yes Yes No MPL/CDDL Yes Yes Yes Yes Yes CPL/EPL Yes Yes Yes Yes Yes Table 2.1. Comparing Open Source Software License Types (Corbett and Ward 2006) However, OSS has its own disadvantages. The same survey of IT executives shows that 52 percent mentioned a lack of vendor support as open source s primary weakness. To fill this gap, major vendors such as Dell, HP, IBM, Oracle and Sun recently have announced in various ways that they would begin supporting open source products (Koch 2003). 2.2 Information System Evaluation Limited budgets of small enterprises led project sponsors to demand an estimate of financial benefits before approving funding for any IT project. IT funding approaches are designed to recover the cost of building and maintaining the information system in an enterprise (Pearlson and Saunders 2006). The main goal is to at least cover the cost of the information system. The variety of IT investment areas from warehouse automation to decision support systems has created demand for different investment evaluation methods. IT capital investment decisions can be analyzed by traditional and IT specific investment evaluation methods. Organizations often use traditional (non-it specific) methods like Net Present Value (NPV), Return on Investment (ROI), and Activity Based Costing (ABC) for IT investment evaluation (Turban, et al. 2006, ). Nevertheless, some researchers argue that traditional evaluation techniques are not suitable for evaluating projects with significant strategic benefits such as BI projects (Irani and Love 2001). IT projects in many cases generate intangible benefits such as faster time to market, employee and customer satisfaction, grater organizational agility, and improved 7

14 control (Turban, et al. 2006, ). Ignoring value of intangible benefits may lead the organization to reject IT investments. Therefore, financial analyses need to consider tangible and intangible benefits. The most straightforward method for evaluation of intangible benefits is to make rough estimates of monetary values for all intangible benefits. However, putting monetary value on the IT investment is not an easy task (Turban, et al. 2006, ). Additionally, in the past years the focus of industries has shifted from cost reduction alone to maximizing both: IT benefits and business benefits (Shields and Bharucha 2003). Growing demand for benefit measurement tools and complexity of calculation methods has resulted in a wide range of IT specific investment evaluation frameworks. Some of these specific frameworks and their summaries are shown in table 2.2. Model Source Model Summary Economic Added Value (EVA) Stern & Stewart Accurate measure of post-tax return. Uses retrospective analysis which is not suitable for prior estimation of returns in IT. Total Value of Ownership (TVO) Gartner Comprehensive view of costs and benefits. Total Economic Impact (TEI) Giga Comprehensive view of costs, benefits and risks. Rapid Economic Justification Microsoft Cost-benefit analysis, multiple stakeholder view and risk assessment. Business Value Index Intel Cost-benefit analysis, multiple stakeholder view and risk assessment. Table 2.2. Comparison between existing benefit measurement models, adopted from (Shields and Bharucha 2003) 8

15 3 Valuing BI Systems In order to compare different BI suites, comparison grounds should be defined first. In this chapter, criteria for comparison have been defined using a divisive ( topdown ) approach. We start from the most important, top business criterion the value of the BI solution brought to the enterprise. Then we continue by dividing business value into smaller criteria categories until we reach the level where it is possible to compare BI systems. We will consider this level reached when it is possible to measure criteria. Executives are constantly evaluating the cost versus the benefit of different business decisions. A typical question is: Which initiative will yield the greatest benefit to the organization? Organisations often use cost-benefit analysis approaches which compare the total value of the benefits with the associated costs (Turban, et al. 2006, 561). Gross Benefits Net Benefit Costs Net Benefit = Gross Benefits - Costs Figure 3.1. Calculating Net Benefits, adopted from PENG model (Dahlgren 1997) The effect on profit (net benefit) consists of gross benefits minus costs of Information System (Dahlgren 1997) (figure 3.1). Thus, in order to calculate net benefits and prove its profitability we need first to calculate costs and gross benefits of a BI system. In the next two sections we are choosing comparison grounds for costs and total benefits. 3.1 Identifying BI Costs The most basic method for calculating the cost is to add up the costs of all the components. Many companies calculate initial and ongoing maintenance costs in this way (Pearlson and Saunders 2006, 256). In our case, summing up the initial and maintenance costs does not provide an entirely accurate total cost for BI initiatives since it includes other costs such as user 9

16 trainings which span all over a BI lifecycle. Therefore, a method known as Total Cost of Ownership (TCO) is used. This technique was introduced by the Gartner Group in late 1980s and used to calculate more accurate IS costs. Besides initial and maintenance costs, TCO includes costs associated with technical support, administration, and training (Pearlson and Saunders 2006, 256). Cost components of IT projects can be classified into three categories: acquisition cost, operations cost, and control cost (Turban, et al. 2006, 567). Since we evaluate integrated BI suites, control costs such as consolidation and standardisation are not applicable in our case and excluded from further cost calculations. Based on literature study (Turban, et al. 2006, 567) typical cost components of BI implementation project are reflected in figure 3.2. The TCO method described above aimed to calculate overall cost of BI system for a defined period of exploitation as a part of overall benefit calculation. These costs are used as grounds for further comparison of BI suites. Software licenses Acquisition Costs RDBMS or other backend system licenses Installation and configuration BI Costs Hardware and other infrastructure Maintenance Operations Costs Support Upgrades User Trainings Figure 3.2. Typical costs for BI implementation project 3.2 Identifying BI Benefits through Decomposition While it is easy to quantify all BI costs, benefits are much more complex and difficult to quantify and calculate. Soft benefits, such as the ability to make future decisions, are making it difficult to measure the payback of the investment. The BI initiative is a long term strategic information system investment where it is hard to evaluate all benefits brought to the enterprise. Below we try to show most obvious, well known 10

17 and general benefits of BI. Nevertheless, and important, a detailed analysis in relation to the specific industry might identify more benefits. Better identification and understanding of BI benefits can be reached through categorisation. Knightsbridge (2005) proposes to simplify the process of identifying benefits by separately considering the two primary categories of benefits: revenue enhancements and cost savings. Cost savings are defined as the difference in the costs associated with the new BI initiative versus the costs associated with maintaining the existing information environment. Revenue enhancements are defined as the beneficial activities that result from decisions individuals make by using information from the BI solution. Steve and Nancy Williams (2003) identify two another dimensions of BI benefits in their article: the business value of BI lies in its use within management processes that impact operational processes that drive revenue or reduce costs, and/or in its use within those operational processes themselves. Revenue Enhancements Management Process Improvement Identification of new business opportunities and markets Improved opportunity recognition Improved time to market Improved decision making Increased organisational agility Identification of under-performing product lines or products Operating Process Improvement Enhanced speed of new product development Increased ability to content with competitors Improved customer service/satisfaction Intangible Cost Saving Improved information dissemination Improved analysis (e.g. reduced customised reporting requests) Informed decision making Reduced operating costs Automation of manual processes Improved operating processes Intangible Tangible Figure 3.3. Quadrant graph for generic BI benefits classification 11

18 These four dimensions of BI benefits (i.e. Revenue Enhancements, Cost Savings, Management Process Improvement, and Operating Process Improvement) are combined together in the quadrant graph on figure 3.3 in order to classify and find the tangibility level of the common BI benefits as identified by Moss and Atre (2003). The top-left (red) square on the picture identifies the BI solution s impact on revenue enhancement through management process improvement which is difficult to evaluate because there is usually no direct attribution. For instance, monetary value of the benefit identification of new business opportunities and markets cannot be estimated at all since we don t know in advance whether new markets exist or not. The bottom-right (green) square contains benefits of operational process improvement associated with cost savings. In contrast to red benefits, these benefits are easy to identify because of the ability to compare the new BI solution to the old operational environment. Finally, bottom-left and top-right (yellow) boxes contain intangible benefits which could be estimated through complicated evaluation methods (e.g. improved customer satisfaction). It could be clearly seen from figure 3.3 that 12 of 15 benefits are either completely intangible (red square) or could only be calculated through complex evaluation methods (yellow squares). This classification method can be used for tangibility level identification of more specific benefits by placing them in one of the four squares in figure 3.3. As already mentioned, BI is a strategic tool and its primary role is the support of business strategy and business processes. As can be seen from figure 3.3, the BI initiative does not contribute as much in terms of being used as a process automation (operational) tool. The four dimensions of figure 3.3 could also be used to claim that instead of evaluating BI separately, it should be analyzed together with the business processes it is supposed to enhance. To additionally support that claim we would like to refer to authors Luftman and Muller (2005), who also conclude that the benefits come not from the latest and greatest IT solution, but from how a business modifies its practices based on its new technology. In spite of that, BI applications still provide some level of pure operational functionality like KPI (Key Performance Indicators) monitoring, automatic reporting and invoicing. A number of benefit measurement models has been discussed in this section as well as in section 2.2. One observation regarding the various ways of assessing BI benefits discussed so far is that reaching a precise valuation may take longer than is reasonable to make an investment decision. This is due to the fact that most of them are complicated and require assigning monetary value to the intangible benefits. Therefore, in this thesis we try to avoid these comprehensive BI benefit calculations based on assigning of monetary values. Instead, in the next section, we explore an alternative approach where a number of criteria influencing benefits brought by BI 12

19 solution are defined, criteria that are not dependent on monetary value assessment per se. 3.3 Identifying BI Benefits through its Characteristics Yielded benefits are directly related to the characteristics of the BI suite. In this section we describe alternative approach of BI benefits identification through the influencing characteristics. This approach suits our research method (section 1.6) since it allows comparing of different BI suites benefits without assigning monetary values. Rajeev Rawat s (2007) criteria for assessing Open Source BI alternative has been adopted to define the characteristics influencing gross value. In our research some irrelevant criteria have been eliminated, since the original list contains criteria for complete evaluation of BI project (including business needs, risk assessment, etc.) while we only looking for BI suite comparison grounds. Rawat s original list of criteria for Open Source BI alternative assessment can be found in Appendix A. Three key criteria categories shown below have been extracted from Rawat s list of criteria for further BI suite comparison: 1. BI Functionalities 2. Solution Maturity 3. Price It could be clearly seen that first and second criteria categories address benefits of BI system, while third category price overlaps with the costs which has been discussed in section 3.1. Identified criteria categories are hierarchically brought together in figure 3.4. Value (Net Benefit) Gross Benefits Costs BI Functionalities Solution Maturity Acquisition Costs Operation Costs Figure 3.4. Criteria categories for BI suite comparison 13

20 In section 3.1 acquisition costs and operation costs are already been decomposed into the smaller criteria which could be defined and compared. BI functionalities and solution maturity criteria categories will be decomposed in the next sections BI Functionalities As it has been mentioned in chapter 1, BI is an umbrella term covering a set of enterprise applications. Different commercial vendors have different understanding of BI and what it should include. Nevertheless, there are some key components which exist in all commercial distributions. BI Functionalities Infrastructure Integration Reporting Dashboard Ad Hoc Query and Reporting Spreadsheet Services OLAP Server OLAP UI Data Mining ETL Alerts Repository Security Scheduling All tools in the platform should use the same security, metadata, administration, portal integration, object model and query engine, and should share the same look and feel. Reporting provides the ability to create formatted reports in different formats (e.g. PDF, Microsoft Excel, etc.) Dashboard includes the ability to publish formal, web-based reports with intuitive displays of information, including dials, gauges and traffic lights. These displays indicate the state of the performance metric, compared with a goal or target value. Also known as self-service reporting, this capability enables users to ask their own questions of the data, without relying on IT to create a report. These functions imply semantic layer which operates between business users and data sources allowing users to navigate without understanding underlying data structure. In some cases, BI platforms are used as a middle tier to manage and execute BI tasks, but office tools (particularly Microsoft Office Excel) acts as the BI client. In these cases, it is vital that the BI vendor provides integration with office suites. Enables end users to analyze data with extremely fast query and calculation performance, enabling a style of analysis known as "slicing and dicing". Front-end tools for the analysis known as "slicing and dicing". This capability enables organizations to classify categorical variables and to estimate continuous variables using advanced mathematical techniques. ETL stands for extract, transform, and load. ETL is a process that enables businesses to consolidate their data from different sources and in different formats. Event based alerts sent by , SMS, etc. Events usually triggered by business rules. The repository defines the functions and services to store BI structured data and metadata (e.g. report templates, business rules, and semantic layer data). This enables administrators to define different user roles and permissions based on business needs. Ability to schedule report generation and other actions. Table 3.1. BI Functionalities criteria group for BI evaluation, adopted from (Rawat 2007) 14

21 The list of functionalities, summarised in table 3.1, is adopted from Rajeev Rawat s (2007) BI function requirements. Some functionality such as data mining and spreadsheet services have been added based on the literature study (Gartner, Magic Quadrant for BI Platforms, 1Q ). In section 4.1 we will provide an analysis of major commercial BI vendors to support our claims about general acceptability of these BI suite functionalities. It is important to note that most of these functionalities could be decomposed further. For instance, reporting may include a number of different output formats and chart types. Since the number of these details is more than one hundred (Howson 2007) we will remain at this level BI Solution Maturity Maturity is another important criterion, especially from open source software perspective. In order to make it definable and measurable it has been broken down into sub-criteria (e.g. measurable criteria such as duration of the product availability in the market and number of customers actively using product can show how mature this product is). In addition to the standard maturity criteria defined in Rawat s (2007) list, we have added open source related criteria such as OS community size and activity. Community importance as a driving force of an open source projects described in section 2.1. Another open source related attribute number of downloads indirectly shows popularity of the product. The resulting list of solution maturity criteria is shown in table 3.2. Solution Maturity Availability duration Customer base Standards compatibility Ready-to-use out of the box ISV-ready: embeddable & extensible Localised and internationalised Training availability Open Source Community Size Open Source Community Activity Downloads (Open Source only) Indicates duration of availability in the market. Indicates number of customers using complete or partial BI solution Defines whether BI suite follows standards or use proprietary solutions. Items, functionalities, or features provided do not require any additional installations, plug-ins, expansion packs, or products. Can be embedded, adopted or extended for specific needs. Translations and localisations are available. Customers can order trainings provided by vendor. Number of registered open source community members. Number of forum and mailing list posts made by open source community members. Number of product downloads (indirectly shows popularity). Table 3.2. Solution maturity criteria group for BI evaluation, adopted from (Rawat 2007) 15

22 4 Results and Findings In the next two sections (4.1 and 4.2) benefits of commercial and open source BI suites estimated and compared based on the criteria defined in sections 3.3. Sections 4.3 and 4.4 mainly explore costs of BI implementation through the two different research methods (survey and case study) in order to increase results accuracy. Further analysis of these findings will help us to answer research question stated in section 1.5 (section 5). 4.1 Commercial BI Suites According to the Gartner s Magic Quadrant (2007), the leading BI software vendors for 1Q 2007 were Business Objects, SAS, Cognos, Oracle, and Hyperion. With recent acquisitions of Hyperion, Business Objects, and Cognos by Oracle, SAP and IBM (respectively) the number of main software vendors has become even smaller. Thus, in the analyses we have included the following commercial BI products: 1. Business Objects XI from SAP 2. Oracle BI Suite from Oracle (including Hyperion) 3. SAS BI from SAS Institute 4. Cognos BI from IBM Published information about the leading commercial BI products and their formal system documentation were consulted in order to support the claims of generality of the accepted BI suite functionalities defined in table 3.1. We found that all claimed functionalities are provided by reviewed commercial BI suites. The only exception was the BusinessObjects XI which relies on 3 rd party tools in data mining and OLAP server. Summary of these analyses based on the criteria defined in section 3.3 can be found in table 4.1. It is important to note that the portfolio of these major vendors usually stretches beyond BI and covers advanced business applications such as Business Performance Solutions (BPS) which are not included in our list of functionalities. 16

23 BI Functionalities* Business Objects XI Infrastructure Integration Yes Yes Yes Yes Reporting Yes Yes Yes Yes Dashboard Yes Yes Yes Yes Ad Hoc Query and Reporting Yes Yes Yes Yes Spreadsheet Services Yes Yes Yes Yes OLAP Server No Yes Yes Yes OLAP UI Yes Yes Yes Yes Data Mining No Yes Yes Yes ETL Yes Yes Yes Yes Alerts Yes Yes Yes Yes Repository Yes Yes Yes Yes Security Yes Yes Yes Yes Scheduling Yes Yes Yes Yes Solution Maturity Availability duration (years) >10 >10 >10 >10 Oracle BI SAS BI Cognos BI Customer base * n/a Standards compatibility ** Ready-to-use out of the box Yes Yes Yes Yes ISV-ready: embeddable & extensible Yes Yes Yes Yes Localised and internationalised Yes Yes Yes Yes Training availability Yes Yes Yes Yes * Information is taken from the vendor s official web site ** Scores based on a scale of 0 (weak) to 10 (strong) and adopted from (Datamonitor 2007) Table 4.1. Comparison results of the commercial BI suites 4.2 Open Source BI Suites Our study of the available OS BI suites on the market has identified four major players based on their popularity (community size and activity): 1. JasperIntelligence from JasperSoft 2. Pentaho BI from Pentaho 3. OpenI from Loyalty Matrix 4. SpagoBI from Engineering Ingegneria Informatica Comparison results of these four open source products based on the criteria defined in section 3.3 is represented in table 4.2. In order to be considered as a comprehensive alternative to a commercial BI, the OS BI suite should provide all 17

24 functionalities previously defined. We chose Pentaho for further comparison since only Pentaho provides all defined functionalities as well as the most active community and a high number of downloads. It should be noted that JasperSoft s high number of community members and downloads can be explained respectively by mandatory registration (as community member) for all downloads and greater number of downloadable items. BI Functionalities* Jasper Intelligence Infrastructure Integration Yes Yes Yes No Reporting Yes Yes Yes Yes Dashboard No Yes No Yes Ad Hoc Query and Reporting Limited Yes No No Spreadsheet Services No Yes No Yes OLAP Server Yes Yes Yes Yes OLAP UI Yes Yes Yes Yes Data Mining No Yes Yes Yes ETL Yes Yes Yes Yes Alerts Yes Yes No No Repository Yes Yes No Yes Security Yes Yes Yes Yes Scheduling Yes Yes No Yes Solution Maturity* Availability duration (months) Customer base n/a n/a n/a Standards compatibility Partial Yes Yes Yes Ready-to-use out of the box Yes Yes Yes Yes ISV-ready: embeddable & extensible Yes Yes Yes Yes Localized & internationalized Yes Yes No Yes Training availability Yes Yes No Yes Open Source Community Size n/a n/a Open Source Community Activity Downloads > n/a * Information is taken from the vendor s official web site Pentaho BI Table 4.2. Comparison results of OS BI suites OpenI SpagoBI 18

25 4.3 Interview and Survey of BI Consulting Companies In order to provide an answer to the second research question (whether OS BI solution provides cost saving) we have conducted a number of interviews and webbased surveys. Interviews were conducted based on the questionnaire from Appendix B in order to better understanding of the real situation in the market. Unfortunately, very few companies accepted request for interview, mainly due to lack of time of the potential interviewee. In order to increase accuracy of the results, more companies have been approached through the simplified web-based survey form where we included only questions 1 4 (questions aimed to understand attitude of consulting companies regarding small enterprises) from the same questionnaire (Appendix B). Overall, two consulting companies providing BI services have been interviewed in person and four other companies responded to the web survey. Complete results of interviews and surveys are given in Appendix C. 100% Q1: What types of enterprises are your company s main target group? 100% 80% 60% 50% 40% 20% 0% 0% Small Medium Large Enterprise Types Figure 4.1. Enterprise types mainly targeted by the BI consulting companies Q2 & Q3: What types of enterprises are your company s customers? Small 3,3% Medium 14,7% Large 82,0% Figure 4.2. Customers of the BI consulting companies 19