Description
factors and reasons for failure of business intelligence system & how to avoid them.
BUSINESS INTELLIGENCE
Reasons for Failure of Business Intelligence
Submitted to Prof. Mukesh Patel
Table of Contents
Title of the Project .................................................................................................................................. 1 Literature Review .................................................................................................................................... 1 Expectations from a BI system ................................................................................................................ 5 Critical Success Factors for Business Intelligence ................................................................................... 6 Organization Dimension ..................................................................................................................... 6 Process dimension .............................................................................................................................. 6 Technological dimension .................................................................................................................... 7 Reasons for Failure of Business Intelligence and how to avoid them .................................................. 10 References ............................................................................................................................................ 13
Title of the Project
Reasons for failure of Business Intelligence
Literature Review
Definition of Business Intelligence Business Intelligence is a widely used term. For example on 15-Nov-2011 a Business Intelligence search on Google generates 169,000,000 hits as compared to 51,800,000 hits for a Data Warehouse search and 83,000,000 hits for Data Mining. The phrase is generally attributed to Howard Dresner of the Gartner Group, who in 1989 discussed a set of concepts and methods for improving business decision making through the use of fact-based support systems (Hayes, 2002; Martens, 2006). Gartner defines business intelligence (BI) as the general ability to organise, access and analyze information in order to learn and understand the business. BI is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance. Decision makers and organisations predominantly associate BI with organisational implementation of specific philosophy and methodology that would refer to working with information and knowledge, open communication, knowledge sharing along with the holistic and analytic approach to business processes in organisations (Reinschmidt, & Francoise, 2002). BI systems are assumed to be solutions that are responsible for transcription of data into information and knowledge and they also create some environment for effective decision making, strategic thinking and acting in organisations (Olszak, & Ziemba, 2004). Value of BI for business is predominantly expressed in the fact that such systems cast some light on information that may serve as the basis for carrying out fundamental changes in a particular enterprise, i.e. establishing new cooperation, acquiring new customers, creating new markets, offering products to customers (Chaudhary, 2004) The origin of Business Intelligence dates back to the biblical times. Joseph, Jacob’s son sold by his brothers to Egypt, was the only one who could interpret pharaoh’s dream about 7 fertile and 7 infertile years that were drawing near. He could use his supernatural knowledge while interpreting the information on the basis of which pharaoh came to a decision of putting some crops aside during the fertile years as reserves of food for the infertile years. Without this information, the decision of gathering the food would have been unjustified. However, knowing the information, Egypt not only could wade through this situation but also made money from selling the food to its neighbours. The conception of Business Intelligence is similar. In order to benefit from business, we should make
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strategic decisions based on analyzed information we have, which is not commonly known. The difference concerns only the data sources. For many years experience was the only source of information. Then its place was taken by mathematics, mathematical, statistical and economic models and currently also the Internet. For the current times, the development of Business Intelligence systems can be traced with the following chart.
Report, data visualization Complexity Level EIS ES Data models, interface DSS MIS Databases, algorithms of processing Reasoning base Knowledge base
BI
Data Mining, OLAP, data warehouse
Exhibit 1: Evolution of Business Intelligence
Time
Source: (Olszak, & Ziemba, 2004) Business Intelligence encompasses various techniques such as Data Mining, Text Mining, Online Analytical processing (OLAP), Query and Reporting systems and Knowledge management to name a few. Today’s BI architecture typically consists of a data warehouse (or one or more data marts), which consolidates data from several operational databases, and serves a variety of front-end querying, reporting, and analytic tools. The back-end of the architecture is a data integration pipeline for populating the data warehouse by extracting data from distributed and usually heterogeneous operational sources; cleansing, integrating and transforming the data; and loading it into the data warehouse
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Exhibit 2: Traditional Business Intelligence Architecture This architecture may be viewed as an information supply chain (Exhibit 2). Data from distributed, often heterogeneous, sources such as online transaction processing (OLTP) systems is periodically extracted, cleansed, integrated, transformed, and loaded into a data warehouse (DW), which in turn is queried by analytic applications. (Sometimes, organizations choose to construct Data Marts, each of which contains information on some subset of the subject areas represented in the DW.) Traditionally, the back-end of the information supply chain is a one way batch process (a data pipeline) usually implemented by home-grown code or extract-transform-load (ETL) tools. (Dayal et. all 2009) Observation of different cases of BI Systems allows for stating that the systems in question may support data analyses and decision making in different areas of organisation performance, particularly including the following (Hsu, 2004; Olszak, & Ziemba, 2003): ? financial analyses that involve reviewing of costs and revenues, calculation and comparative analyses of corporate income statements, analyses of corporate balance sheet and profitability, analyses of financial markets and sophisticated controlling; • marketing analyses that involve analyses of sales receipts, sales profitability, profit margins, meeting sales targets, time of orders, actions undertaken by competitors, stock exchange quotations; • customer analyses that concern time of maintaining contacts with customers, customer • profitability, modelling customers’ behaviour and reactions, customer satisfaction, etc.; production management analyses that make it possible to identify production ‘bottlenecks’ and delayed orders, thus enabling organizations to examine production dynamics and to compare production results obtained by departments or plants, etc.;
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• ?
?
logistic analyses that enable to identify partners of supply chain quickly; analyses of wage related data including wage component reports made with reference to the type required, reports made from the perspective of a given enterprise, wage reports distinguishing employment types, payroll surcharges, personal contribution reports, analyses of average wages, etc.; and Personal data analyses that involve examination of employment turnover, employment types, presentation of information on individual employee’s personal data, etc.
Though Business Intelligence has been in discussion since past few years but still importance has not been given to implementation of Business Intelligence systems in organizations. Little has been said about how an organization should go about implementing a Business Intelligence system.
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Expectations from a BI system
According to a survey by Gartner for ranking the strategic use of BI the following order was observed 1. Corporate Performance management 2. Optimizing customer relations, monitoring business activity, and traditional decision support 3. Packaged standalone BI applications for specific operations or strategies 4. Management reporting of business intelligence The implication of this ranking is that ordinary reporting of your own and your competitors’ performance, which is the strength of many existing software packages, is not enough. A second implication is that too many firms still view business intelligence (like DSS and EIS before it) as an inward looking function (Nagesh et. all 2003). According to a report by Deloitte some of the criteria that should be evaluated while comparing various vendors providing a Business Intelligence package include (Deloitte 2008): 1. Support for industry specific processes 2. Technical capabilities 3. Vendor’s market strategy, stability, and market share 4. Ease of integration with the existing IT infrastructure and standards, including ERP and/or CRM applications 5. Support of the business case and ability to meet ROI objectives This clearly shows that apart from IT compatibility what is more important is the business compatibility of the Business Intelligence solution. The synergies that the Business Intelligence tools are expected to have with business are important and strategic in nature.
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Critical Success Factors for Business Intelligence
In their study “Critical Success Factors for Business Intelligence Systems” (Yeoh et. all 2009) have developed a framework which is a combination of process performance and infrastructure performance. Process performance is how well the process of a BI system implementation went, and infrastructure performance is the quality of the system and the standard of output. The framework lists down the factors necessary for the success of BI implementation, the absence of which would lead to failure of the system. Some of the Critical Success Factors (CSFs) recognized by (Yeoh et. all 2009) are enlisted below.
Organization Dimension
Committed management support and sponsorship Committed management support and sponsorship has been widely acknowledged as the most important factor for BI system implementation. It is important particularly in breaking down the barriers to change and states of mind within the organization Clear vision and well-established business case A substantial business case should identify the proposed strategic benefits, resources, risks, costs, and timeline. More significantly, it is important to understand that a BI system implementation is not a project; it is a process. That is, BI systems are organic in nature. They evolve dynamically and in directions that are not necessarily finite and predictable.
Process dimension
Business-centric championship and balanced team composition Most participants believed that having the right champion from the business side of the organisation is critical for implementation success. They expressed the view that a champion who has excellent business acumen is always important since he/she will be able to foresee the organisational challenges and change course accordingly. Business-driven and iterative development approach Business-oriented project scoping and planning allow the BI team to concentrate on the best opportunities for improvement. Scoping helps in the selection of clear parameters and develops a common understanding among all business stakeholders as to what is in scope and what is excluded
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User-oriented change management Having an adequate user oriented change management effort was deemed critical by most Delphi participants. They reported that better user participation in the process of change can lead to better communication of their needs, which in turn can help ensure successful introduction of the system.
Technological dimension
Business-driven, scalable and flexible technical framework Turning now to technological issues, a key factor emphasised by many Delphi respondents was that the technical framework of a BI system must be able to accommodate scalability and flexibility requirements in line with dynamic business needs. That is, flexible and scalable infrastructure design allows for easy expansion of the system to align it with evolving information needs Sustainable data quality and integrity In regard to the important factor of sustainable data quality and integrity, the Delphi findings indicate that the quality of data, particularly in the source systems, is crucial if a BI system is to be implemented successfully. According to most interviewees, a primary purpose of a BI system is to integrate ‘silos’ of data for advanced analysis so as to improve the decision-making process.
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In a research study (Arnott 2008) has listed down 10 factors which are considered important for the success of BI. Factor Committed and informed executive sponsor 2. Widespread management support 3. Appropriate team skills Discription A senior executive should be responsible for overall guidance of the project, allocating resources and representing the project to the executive team and board. DW/BI should be business driven with widespread management support. This helps manage the change process and overcome resistance. Staff in the client organization and external suppliers should have appropriate knowledge, skills and experience. There should be a high degree of organizational fit with the DW/BI hardware and software. There should be adequate funding of hardware, software and human resources. Operational data sources should be available. ETL applications should ensure currency, consistency, and accuracy. The data model should be flexible and extensible. The project should have a clear link with the business’s strategies and be economically justified in terms of its business value Despite the difficulty of defining executives’ requirements, the project should have an accepted definition of what is required from the system. A successful DW/BI system should be developed iteratively with strong user involvement, evolving towards an effective application set. The scope of a project can increase significantly. This can stretch project resources.
4. Appropriate technology 5. Adequate resources 6. Effective data management 7. Clear link with business objectives 8. Well-defined information and systems requirements 9.Evolutionary development 10. Management of project scope
Exhibit 3: Critical Success Factors for a BI (Arnott 2008) Source: http://www.bsec.canterbury.ac.nz/acis2008/Papers/acis-0007-2008.pdf Further research with these 10 factors in prospective has suggested that the achievement of these critical success factors has lead to the development of a successful Business Intelligence System. Also inability to achieve most of the factors has lead to failure of Business Intelligence implementation
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In a survey Hwang, (Hwang 2008) has come up with certain findings about the success of Business Intelligence systems. Below are some of the findings. 1. Greater management understanding is related to higher BI success 2. The perceived importance of BI to a company’s success is also critical. BI is not likely to thrive until it is viewed as vital to a company’s overall success. 3. Companies with more BI success in the past are more likely to increase its use. 4. Companies that excel in enterprise technologies are more successful in BI as well 5. The results support the assumption that companies good at utilizing external data sources are more successful 6. The results support the assumption that companies good at utilizing external data sources are more successful
The survey proposes the model as shown in Exhibit 4 for success of BI.
Technical feasibility
Enterprise technologies
Project Feasibility
Project initiation level Business drivers Prioritization criteria Funding sources Project size Evaluation criteria Member credentials Use of consultants Number of projects(
Use of external data Dedicated hardware Use of external hosting Operational feasibility Senior management’s understanding of BI Importance of BI Current BI involvement Future BI involvement BI structure Barriers to BI
BI success
Number of successful projects
Exhibit 4: Research Model for Business Intelligence Success Source: (Hwang 2008), Available at http://www.myacme.org/ACMEProceedings09/p44.pdf
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Reasons for Failure of Business Intelligence and how to avoid them
From the discussions above it is clear that for a Business Intelligence Solution in an organization to succeed, it must have certain conditions fulfilled. Enumerated below is a list of conditions which are mostly lead to failure of Business Intelligence. Lack of upfront planning A common assumption in BI projects is that "If we build it, they will come". Inconsistent implementations, lack of executive sponsorship, lack of cooperation and intra-departmental conflicts cause slow adoption and abandonment of BI projects. The success of a BI Project is directly related to consideration of business, user & training requirements, because the value of a BI deployment is NOT that obvious that all users would be lining up to learn to use the system, despite the sales pitch that BI vendors make. Organizations should start with a solid business case for why they want BI, carefully considering requirements and strategically aligning BI with business problems. Too often, BI systems are built for the power user and thus only a handful of employees use it. Instead the BI systems should appeal to the mass majority of users and once these users have what they are looking for to make their lives simpler, the power user capabilities should be considered. Lack of Business Support for the BI Initiative This is closely related to the previous point (lack of upfront planning). The BI solution should be seen as an integral part of the business and not as an IT initiative. License Fees are the focus instead of TCO When we take into consideration the infrastructure cost and the professional services cost, we are starting to get an idea of the true cost. But this is still not the true total cost. Pass the RACT test before we start The RACT test asks: Is this solution: Relevant? (As in: "Who takes care of the deliverable?", "What exact business problem are we solving here?" and “What is the cost of the problem?") ? Accurate? (If we get this one wrong, stop! Go back the start, do not pass BEGIN .....) Inaccurate reports / dashboards / information breeds distrust which means the project is already a failure ? Consistent? It better be! ? Timely? (Are we getting the information when I need it?)
?
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If the BI implementation does not pass and continue to pass this test, it must be reconsidered
Data Quality issues The GIGO equation fits well here. Garbage In = Garbage Out. Bad Data leads to bad decisions. Too many bad decisions or just one crucial one will cause immediate distrust and abandonment. Where a data warehouse is used, it is important to filter out bad data at the ETL (Extract, Transform & Load) stage. Good data governance is a separate but linked project to ensure a good data warehouse with clean, high-quality data. Data Quality issues should ideally be fixed at their source, which is the source systems from which information is gathered. Not Anticipating change Most of the requirements that drove the implementation of the BI project will change within a year. BI systems evolve and as users adopt it more readily, new requirements will surface. One should ensure that the organization is prepared for (is flexible enough to handle) evolutionary change and choose a product that will allow you the flexibility of rapidly changing what has been delivered and ensure that your BI project budgets reflect these allowances. BI projects should never be managed using a waterfall methodology, but always spiral / cyclical. Every "round" of a BI project should deliver more insight, some new aspect to the business which ought to trigger more questions to be answered. Differences in Perceived Need Some people don't really want a single version of the truth, thus the proliferation of "spread marts" in an organization. Some people are happy to work with common assumptions and manipulate the numbers in meetings because this allows them to assert political power. That might be an extreme pessimistic example, but ignoring the cultural challenges in an organization can threaten the success of a BI deployment. The "single version of the truth" mantra must be embraced and propagated throughout the organization from the CEO on down. Too often, organizations are led to believe that the best solution for BI would be to purchase their existing ERP, CRM or other vendor's BI / Analytics product. This is not correct. Almost 100% of these organizations find much further down the track that having to integrate the rest of their organization into the BI solution from the ERP / CRM vendor is a very costly exercise, much more so than if they performed a thorough
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evaluation of the available solutions and matched these up against their real requirements before they got started.
Dashboards as a generic cure Graphical dashboards are appealing and offer great visualization insight but they also need the same amount of planning and careful consideration for what goes into them as any other project would. The data behind the dashboard needs to verified and checked for consistency and accuracy, otherwise it is just a pretty picture without any value. Dashboard implementation needs to be part of the strategic plan. Back to the RACT test! Outsourcing The most crucial factor to the success of any BI (or any other software project for that matter) is the knowledge of how the company works and what is stored where. Business Analysts and Data Analysts who understand these aspects of the organization are worth their weight in gold, as they are the ones who will validate or refute the success of the BI implementation. Thus an intimate knowledge of the organization's policies, business practices, history, user demographics, customer demographics are the things that can never be outsourced and yet these are the crucial elements that ensure success of a BI project. Performance Considerations The typical engagement starts off with a demonstration of the product running against some data that might be up to a few hundred thousand records in size from the main source system. Roll on to production implementation where there are hundreds of million or even billions of records. Suddenly the scalability considerations become very evident. Refer to the last point of the RACT test. Ensure that the choice of product will scale to support data volumes, user volumes and concurrency.
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References
Gartner, available at http://www.gartner.com/technology/it-glossary/#bi, Accessed on 10Nov-2011 Olszak, C. M., & Ziemba, E. (2004), Business intelligence systems as a new generation of decision support systems. Proceedings PISTA 2004, International Conference on Politics and Information Systems: Technologies and Applications. Orlando: The International Institute of Informatics and Systemics. Chaudhary, S. (2004), Management factors for strategic BI success, In Business intelligence in digital economy. Opportunities, limitations and risks, IDEA Group Publishing Reinschmidt, J., & Francoise, A. (2000), Business intelligence certification guide, IBM, International Technical Support Organization Dayal U., Castellanos M., Simitsis A. & Wilkinson K. (2009) Data Integration Flows for Business Intelligence, Published by Extended Database Technology Association Business Intelligence 101, Available at http://www.deloitte.com/assets/DcomIreland/Local%20Assets/Documents/ie_ConsultingEA_BusIntelligence_09.pdf, Published by Deloitte Consulting 2008, Accessed on 12-Nov-2011 Olszak, C. M., & Ziemba, E. (2003), Business intelligence as a key to management of an enterprise, Proceedings of Informing Science and IT Education Conference, 2003, Retrieved December 1, 2005 from http://proceedings.informingscience.org/IS2003Proceedings/docs/109Olsza.pdf Hsu, J. (2004). Data mining and business intelligence: Tools, technology and applications. In M. Raisinghani (Ed.), Business intelligence in the digital economy. London: Idea Group Publishing Hayes, F. (2002), The story so far, Published by Computerworld. Martens, C. (2006), Business intelligence at age 17, Published by Computerworld. Nagesh S. & Paul G. Business Intelligence, Published at Americas Conference on Information Systems (AMCIS) 2003 Deloitte (2008) Business Intelligence 101, Available at http://www.deloitte.com/assets/DcomIreland/Local%20Assets/Documents/ie_ConsultingEA_BusIntelligence_09.pdf, Accessed on 20-Nov-2011 Arnott D. 2008, Success Factors for Data Warehouse and Business Intelligence Systems,
Published by Center for Decision Support System and Enterprise Systems Research, Monash University Hwang M. 2008, SUCCESS FACTORS FOR BUSINESS INTELLIGENCE: PERCEPTIONS OF
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BUSINESS PROFESSIONAL, Published by Hwang, Business Information Systems Department, Central Michigan University, 2008
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doc_610821610.docx
factors and reasons for failure of business intelligence system & how to avoid them.
BUSINESS INTELLIGENCE
Reasons for Failure of Business Intelligence
Submitted to Prof. Mukesh Patel
Table of Contents
Title of the Project .................................................................................................................................. 1 Literature Review .................................................................................................................................... 1 Expectations from a BI system ................................................................................................................ 5 Critical Success Factors for Business Intelligence ................................................................................... 6 Organization Dimension ..................................................................................................................... 6 Process dimension .............................................................................................................................. 6 Technological dimension .................................................................................................................... 7 Reasons for Failure of Business Intelligence and how to avoid them .................................................. 10 References ............................................................................................................................................ 13
Title of the Project
Reasons for failure of Business Intelligence
Literature Review
Definition of Business Intelligence Business Intelligence is a widely used term. For example on 15-Nov-2011 a Business Intelligence search on Google generates 169,000,000 hits as compared to 51,800,000 hits for a Data Warehouse search and 83,000,000 hits for Data Mining. The phrase is generally attributed to Howard Dresner of the Gartner Group, who in 1989 discussed a set of concepts and methods for improving business decision making through the use of fact-based support systems (Hayes, 2002; Martens, 2006). Gartner defines business intelligence (BI) as the general ability to organise, access and analyze information in order to learn and understand the business. BI is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance. Decision makers and organisations predominantly associate BI with organisational implementation of specific philosophy and methodology that would refer to working with information and knowledge, open communication, knowledge sharing along with the holistic and analytic approach to business processes in organisations (Reinschmidt, & Francoise, 2002). BI systems are assumed to be solutions that are responsible for transcription of data into information and knowledge and they also create some environment for effective decision making, strategic thinking and acting in organisations (Olszak, & Ziemba, 2004). Value of BI for business is predominantly expressed in the fact that such systems cast some light on information that may serve as the basis for carrying out fundamental changes in a particular enterprise, i.e. establishing new cooperation, acquiring new customers, creating new markets, offering products to customers (Chaudhary, 2004) The origin of Business Intelligence dates back to the biblical times. Joseph, Jacob’s son sold by his brothers to Egypt, was the only one who could interpret pharaoh’s dream about 7 fertile and 7 infertile years that were drawing near. He could use his supernatural knowledge while interpreting the information on the basis of which pharaoh came to a decision of putting some crops aside during the fertile years as reserves of food for the infertile years. Without this information, the decision of gathering the food would have been unjustified. However, knowing the information, Egypt not only could wade through this situation but also made money from selling the food to its neighbours. The conception of Business Intelligence is similar. In order to benefit from business, we should make
Page | 1
strategic decisions based on analyzed information we have, which is not commonly known. The difference concerns only the data sources. For many years experience was the only source of information. Then its place was taken by mathematics, mathematical, statistical and economic models and currently also the Internet. For the current times, the development of Business Intelligence systems can be traced with the following chart.
Report, data visualization Complexity Level EIS ES Data models, interface DSS MIS Databases, algorithms of processing Reasoning base Knowledge base
BI
Data Mining, OLAP, data warehouse
Exhibit 1: Evolution of Business Intelligence
Time
Source: (Olszak, & Ziemba, 2004) Business Intelligence encompasses various techniques such as Data Mining, Text Mining, Online Analytical processing (OLAP), Query and Reporting systems and Knowledge management to name a few. Today’s BI architecture typically consists of a data warehouse (or one or more data marts), which consolidates data from several operational databases, and serves a variety of front-end querying, reporting, and analytic tools. The back-end of the architecture is a data integration pipeline for populating the data warehouse by extracting data from distributed and usually heterogeneous operational sources; cleansing, integrating and transforming the data; and loading it into the data warehouse
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Exhibit 2: Traditional Business Intelligence Architecture This architecture may be viewed as an information supply chain (Exhibit 2). Data from distributed, often heterogeneous, sources such as online transaction processing (OLTP) systems is periodically extracted, cleansed, integrated, transformed, and loaded into a data warehouse (DW), which in turn is queried by analytic applications. (Sometimes, organizations choose to construct Data Marts, each of which contains information on some subset of the subject areas represented in the DW.) Traditionally, the back-end of the information supply chain is a one way batch process (a data pipeline) usually implemented by home-grown code or extract-transform-load (ETL) tools. (Dayal et. all 2009) Observation of different cases of BI Systems allows for stating that the systems in question may support data analyses and decision making in different areas of organisation performance, particularly including the following (Hsu, 2004; Olszak, & Ziemba, 2003): ? financial analyses that involve reviewing of costs and revenues, calculation and comparative analyses of corporate income statements, analyses of corporate balance sheet and profitability, analyses of financial markets and sophisticated controlling; • marketing analyses that involve analyses of sales receipts, sales profitability, profit margins, meeting sales targets, time of orders, actions undertaken by competitors, stock exchange quotations; • customer analyses that concern time of maintaining contacts with customers, customer • profitability, modelling customers’ behaviour and reactions, customer satisfaction, etc.; production management analyses that make it possible to identify production ‘bottlenecks’ and delayed orders, thus enabling organizations to examine production dynamics and to compare production results obtained by departments or plants, etc.;
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• ?
?
logistic analyses that enable to identify partners of supply chain quickly; analyses of wage related data including wage component reports made with reference to the type required, reports made from the perspective of a given enterprise, wage reports distinguishing employment types, payroll surcharges, personal contribution reports, analyses of average wages, etc.; and Personal data analyses that involve examination of employment turnover, employment types, presentation of information on individual employee’s personal data, etc.
Though Business Intelligence has been in discussion since past few years but still importance has not been given to implementation of Business Intelligence systems in organizations. Little has been said about how an organization should go about implementing a Business Intelligence system.
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Expectations from a BI system
According to a survey by Gartner for ranking the strategic use of BI the following order was observed 1. Corporate Performance management 2. Optimizing customer relations, monitoring business activity, and traditional decision support 3. Packaged standalone BI applications for specific operations or strategies 4. Management reporting of business intelligence The implication of this ranking is that ordinary reporting of your own and your competitors’ performance, which is the strength of many existing software packages, is not enough. A second implication is that too many firms still view business intelligence (like DSS and EIS before it) as an inward looking function (Nagesh et. all 2003). According to a report by Deloitte some of the criteria that should be evaluated while comparing various vendors providing a Business Intelligence package include (Deloitte 2008): 1. Support for industry specific processes 2. Technical capabilities 3. Vendor’s market strategy, stability, and market share 4. Ease of integration with the existing IT infrastructure and standards, including ERP and/or CRM applications 5. Support of the business case and ability to meet ROI objectives This clearly shows that apart from IT compatibility what is more important is the business compatibility of the Business Intelligence solution. The synergies that the Business Intelligence tools are expected to have with business are important and strategic in nature.
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Critical Success Factors for Business Intelligence
In their study “Critical Success Factors for Business Intelligence Systems” (Yeoh et. all 2009) have developed a framework which is a combination of process performance and infrastructure performance. Process performance is how well the process of a BI system implementation went, and infrastructure performance is the quality of the system and the standard of output. The framework lists down the factors necessary for the success of BI implementation, the absence of which would lead to failure of the system. Some of the Critical Success Factors (CSFs) recognized by (Yeoh et. all 2009) are enlisted below.
Organization Dimension
Committed management support and sponsorship Committed management support and sponsorship has been widely acknowledged as the most important factor for BI system implementation. It is important particularly in breaking down the barriers to change and states of mind within the organization Clear vision and well-established business case A substantial business case should identify the proposed strategic benefits, resources, risks, costs, and timeline. More significantly, it is important to understand that a BI system implementation is not a project; it is a process. That is, BI systems are organic in nature. They evolve dynamically and in directions that are not necessarily finite and predictable.
Process dimension
Business-centric championship and balanced team composition Most participants believed that having the right champion from the business side of the organisation is critical for implementation success. They expressed the view that a champion who has excellent business acumen is always important since he/she will be able to foresee the organisational challenges and change course accordingly. Business-driven and iterative development approach Business-oriented project scoping and planning allow the BI team to concentrate on the best opportunities for improvement. Scoping helps in the selection of clear parameters and develops a common understanding among all business stakeholders as to what is in scope and what is excluded
Page | 6
User-oriented change management Having an adequate user oriented change management effort was deemed critical by most Delphi participants. They reported that better user participation in the process of change can lead to better communication of their needs, which in turn can help ensure successful introduction of the system.
Technological dimension
Business-driven, scalable and flexible technical framework Turning now to technological issues, a key factor emphasised by many Delphi respondents was that the technical framework of a BI system must be able to accommodate scalability and flexibility requirements in line with dynamic business needs. That is, flexible and scalable infrastructure design allows for easy expansion of the system to align it with evolving information needs Sustainable data quality and integrity In regard to the important factor of sustainable data quality and integrity, the Delphi findings indicate that the quality of data, particularly in the source systems, is crucial if a BI system is to be implemented successfully. According to most interviewees, a primary purpose of a BI system is to integrate ‘silos’ of data for advanced analysis so as to improve the decision-making process.
Page | 7
In a research study (Arnott 2008) has listed down 10 factors which are considered important for the success of BI. Factor Committed and informed executive sponsor 2. Widespread management support 3. Appropriate team skills Discription A senior executive should be responsible for overall guidance of the project, allocating resources and representing the project to the executive team and board. DW/BI should be business driven with widespread management support. This helps manage the change process and overcome resistance. Staff in the client organization and external suppliers should have appropriate knowledge, skills and experience. There should be a high degree of organizational fit with the DW/BI hardware and software. There should be adequate funding of hardware, software and human resources. Operational data sources should be available. ETL applications should ensure currency, consistency, and accuracy. The data model should be flexible and extensible. The project should have a clear link with the business’s strategies and be economically justified in terms of its business value Despite the difficulty of defining executives’ requirements, the project should have an accepted definition of what is required from the system. A successful DW/BI system should be developed iteratively with strong user involvement, evolving towards an effective application set. The scope of a project can increase significantly. This can stretch project resources.
4. Appropriate technology 5. Adequate resources 6. Effective data management 7. Clear link with business objectives 8. Well-defined information and systems requirements 9.Evolutionary development 10. Management of project scope
Exhibit 3: Critical Success Factors for a BI (Arnott 2008) Source: http://www.bsec.canterbury.ac.nz/acis2008/Papers/acis-0007-2008.pdf Further research with these 10 factors in prospective has suggested that the achievement of these critical success factors has lead to the development of a successful Business Intelligence System. Also inability to achieve most of the factors has lead to failure of Business Intelligence implementation
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In a survey Hwang, (Hwang 2008) has come up with certain findings about the success of Business Intelligence systems. Below are some of the findings. 1. Greater management understanding is related to higher BI success 2. The perceived importance of BI to a company’s success is also critical. BI is not likely to thrive until it is viewed as vital to a company’s overall success. 3. Companies with more BI success in the past are more likely to increase its use. 4. Companies that excel in enterprise technologies are more successful in BI as well 5. The results support the assumption that companies good at utilizing external data sources are more successful 6. The results support the assumption that companies good at utilizing external data sources are more successful
The survey proposes the model as shown in Exhibit 4 for success of BI.
Technical feasibility
Enterprise technologies
Project Feasibility
Project initiation level Business drivers Prioritization criteria Funding sources Project size Evaluation criteria Member credentials Use of consultants Number of projects(
Use of external data Dedicated hardware Use of external hosting Operational feasibility Senior management’s understanding of BI Importance of BI Current BI involvement Future BI involvement BI structure Barriers to BI
BI success
Number of successful projects
Exhibit 4: Research Model for Business Intelligence Success Source: (Hwang 2008), Available at http://www.myacme.org/ACMEProceedings09/p44.pdf
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Reasons for Failure of Business Intelligence and how to avoid them
From the discussions above it is clear that for a Business Intelligence Solution in an organization to succeed, it must have certain conditions fulfilled. Enumerated below is a list of conditions which are mostly lead to failure of Business Intelligence. Lack of upfront planning A common assumption in BI projects is that "If we build it, they will come". Inconsistent implementations, lack of executive sponsorship, lack of cooperation and intra-departmental conflicts cause slow adoption and abandonment of BI projects. The success of a BI Project is directly related to consideration of business, user & training requirements, because the value of a BI deployment is NOT that obvious that all users would be lining up to learn to use the system, despite the sales pitch that BI vendors make. Organizations should start with a solid business case for why they want BI, carefully considering requirements and strategically aligning BI with business problems. Too often, BI systems are built for the power user and thus only a handful of employees use it. Instead the BI systems should appeal to the mass majority of users and once these users have what they are looking for to make their lives simpler, the power user capabilities should be considered. Lack of Business Support for the BI Initiative This is closely related to the previous point (lack of upfront planning). The BI solution should be seen as an integral part of the business and not as an IT initiative. License Fees are the focus instead of TCO When we take into consideration the infrastructure cost and the professional services cost, we are starting to get an idea of the true cost. But this is still not the true total cost. Pass the RACT test before we start The RACT test asks: Is this solution: Relevant? (As in: "Who takes care of the deliverable?", "What exact business problem are we solving here?" and “What is the cost of the problem?") ? Accurate? (If we get this one wrong, stop! Go back the start, do not pass BEGIN .....) Inaccurate reports / dashboards / information breeds distrust which means the project is already a failure ? Consistent? It better be! ? Timely? (Are we getting the information when I need it?)
?
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If the BI implementation does not pass and continue to pass this test, it must be reconsidered
Data Quality issues The GIGO equation fits well here. Garbage In = Garbage Out. Bad Data leads to bad decisions. Too many bad decisions or just one crucial one will cause immediate distrust and abandonment. Where a data warehouse is used, it is important to filter out bad data at the ETL (Extract, Transform & Load) stage. Good data governance is a separate but linked project to ensure a good data warehouse with clean, high-quality data. Data Quality issues should ideally be fixed at their source, which is the source systems from which information is gathered. Not Anticipating change Most of the requirements that drove the implementation of the BI project will change within a year. BI systems evolve and as users adopt it more readily, new requirements will surface. One should ensure that the organization is prepared for (is flexible enough to handle) evolutionary change and choose a product that will allow you the flexibility of rapidly changing what has been delivered and ensure that your BI project budgets reflect these allowances. BI projects should never be managed using a waterfall methodology, but always spiral / cyclical. Every "round" of a BI project should deliver more insight, some new aspect to the business which ought to trigger more questions to be answered. Differences in Perceived Need Some people don't really want a single version of the truth, thus the proliferation of "spread marts" in an organization. Some people are happy to work with common assumptions and manipulate the numbers in meetings because this allows them to assert political power. That might be an extreme pessimistic example, but ignoring the cultural challenges in an organization can threaten the success of a BI deployment. The "single version of the truth" mantra must be embraced and propagated throughout the organization from the CEO on down. Too often, organizations are led to believe that the best solution for BI would be to purchase their existing ERP, CRM or other vendor's BI / Analytics product. This is not correct. Almost 100% of these organizations find much further down the track that having to integrate the rest of their organization into the BI solution from the ERP / CRM vendor is a very costly exercise, much more so than if they performed a thorough
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evaluation of the available solutions and matched these up against their real requirements before they got started.
Dashboards as a generic cure Graphical dashboards are appealing and offer great visualization insight but they also need the same amount of planning and careful consideration for what goes into them as any other project would. The data behind the dashboard needs to verified and checked for consistency and accuracy, otherwise it is just a pretty picture without any value. Dashboard implementation needs to be part of the strategic plan. Back to the RACT test! Outsourcing The most crucial factor to the success of any BI (or any other software project for that matter) is the knowledge of how the company works and what is stored where. Business Analysts and Data Analysts who understand these aspects of the organization are worth their weight in gold, as they are the ones who will validate or refute the success of the BI implementation. Thus an intimate knowledge of the organization's policies, business practices, history, user demographics, customer demographics are the things that can never be outsourced and yet these are the crucial elements that ensure success of a BI project. Performance Considerations The typical engagement starts off with a demonstration of the product running against some data that might be up to a few hundred thousand records in size from the main source system. Roll on to production implementation where there are hundreds of million or even billions of records. Suddenly the scalability considerations become very evident. Refer to the last point of the RACT test. Ensure that the choice of product will scale to support data volumes, user volumes and concurrency.
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References
Gartner, available at http://www.gartner.com/technology/it-glossary/#bi, Accessed on 10Nov-2011 Olszak, C. M., & Ziemba, E. (2004), Business intelligence systems as a new generation of decision support systems. Proceedings PISTA 2004, International Conference on Politics and Information Systems: Technologies and Applications. Orlando: The International Institute of Informatics and Systemics. Chaudhary, S. (2004), Management factors for strategic BI success, In Business intelligence in digital economy. Opportunities, limitations and risks, IDEA Group Publishing Reinschmidt, J., & Francoise, A. (2000), Business intelligence certification guide, IBM, International Technical Support Organization Dayal U., Castellanos M., Simitsis A. & Wilkinson K. (2009) Data Integration Flows for Business Intelligence, Published by Extended Database Technology Association Business Intelligence 101, Available at http://www.deloitte.com/assets/DcomIreland/Local%20Assets/Documents/ie_ConsultingEA_BusIntelligence_09.pdf, Published by Deloitte Consulting 2008, Accessed on 12-Nov-2011 Olszak, C. M., & Ziemba, E. (2003), Business intelligence as a key to management of an enterprise, Proceedings of Informing Science and IT Education Conference, 2003, Retrieved December 1, 2005 from http://proceedings.informingscience.org/IS2003Proceedings/docs/109Olsza.pdf Hsu, J. (2004). Data mining and business intelligence: Tools, technology and applications. In M. Raisinghani (Ed.), Business intelligence in the digital economy. London: Idea Group Publishing Hayes, F. (2002), The story so far, Published by Computerworld. Martens, C. (2006), Business intelligence at age 17, Published by Computerworld. Nagesh S. & Paul G. Business Intelligence, Published at Americas Conference on Information Systems (AMCIS) 2003 Deloitte (2008) Business Intelligence 101, Available at http://www.deloitte.com/assets/DcomIreland/Local%20Assets/Documents/ie_ConsultingEA_BusIntelligence_09.pdf, Accessed on 20-Nov-2011 Arnott D. 2008, Success Factors for Data Warehouse and Business Intelligence Systems,
Published by Center for Decision Support System and Enterprise Systems Research, Monash University Hwang M. 2008, SUCCESS FACTORS FOR BUSINESS INTELLIGENCE: PERCEPTIONS OF
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BUSINESS PROFESSIONAL, Published by Hwang, Business Information Systems Department, Central Michigan University, 2008
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