DRIVERS AND BARRIERS TO BUSINESS INTELLIGENCE ADOPTION: A CASE OF PAKISTAN

European and Mediterranean Conference on Information Systems 2010 (EMCIS2010)

April 12-13 2009, Abu Dhabi, UAE

Agha Khan et al

Drivers and Barriers to Business Intelligence Adoption: A Case of Pakistan

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DRIVERS AND BARRIERS TO BUSINESS INTELLIGENCE ADOPTION: A CASE OF PAKISTAN

Agha Muhammad Ali Khan, Westminster Business School, University of Westminster, UK [email protected]

Nadia Amin, Westminster Business School, University of Westminster, UK [email protected]

Nick Lambrou, Westminster Business School, University of Westminster, UK [email protected]

Abstract

This research project deals with the Drivers and Barriers to Business Intelligence Adoption. The Drivers and Barrier Methodology (DBM) is a useful tool for measuring how a particular technology such as Business Intelligence (BI) brings tangible benefits to a business enterprise such as a Consumer Bank or Telecommunications Company. This research helps us determine the key factors which force a company to adopt a certain technology and at the same time points out likely impediments and road blocks on the path to successful project completion. A previous body of work both on DBM and on the vast amount of research done in the domain of Business Intelligence and Data warehousing is used as foundation for this research project. A case of Pakistan is used apply the aforementioned methodolgy. The research concludes that depending on a secific user type such as a Power user, IT User or Business user a ’peculiar preference’ in drivers and unique aversion to barriers exist. The challenges and problems faced at each step of the adoption cycle are also highlighted.The findings provide valuable recommendations to application designers, IT Vendors, business analysts, marketers and solution providers.

Keywords: Drivers and Barriers, Business Intelligence, Information Technology Adoption Cycle, IT Case Study, CIO Interviews, Information Technology Strategy, Business Analytics, Data warehousing, Data –Driven Decision making, Business Applications, IT Solution Selling, Marketing, Customer Insight, Data Mining.

1. INTRODUCTION

More and more organizations are turning to data driven decision making to address the challenges of complex business decisions. Firms are piling huge amounts of data with little insight into the behaviour of their customers. Vast amounts of data presents problems of complexity and is hard to analyse by business managers unless a Business Intelligence (BI) application is in place aggregating, analysing and storing mountains of corporate data collected by organizations on their customers (Turban, Sharda, Aronson and King, 2008) .

The paper explains investigates the key factors that force a company to adopt a certain technology. It also explores the barriers that come in the way of new IT motions such as Customer Relationship Management (CRM) implementation or other projects.

The objectives of the research are threefold: • To conduct a partial and academically neutral research that does not favor any specific vendor or

industry analyst.

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• to carry out an exhaustive literature review around the BI universe to lend depth to the research. • to validate previous research on Drivers & Barriers of Business Intelligence and at the same time

discover ‘new’ Drivers & Barriers that have come into play in the dynamic data environments of today.

The findings will bring to light the forces that govern buying decisions around BI applications in large organizations suffering from data overload and lack of faster decision making capability. The results also show which drivers and barriers are most significant and stand in the way of a successful adoption in various organizational structures.

A portion of the survey instrument validate responses from an earlier DBM research and the results are checked to see if they conform, providing further solidity to the study approach. The survey also makes an attempt to collect useful demographic data. Given the nature of this project being in the B2B domain, an inquiry into corporate data and opinion is always a challenge and this type of information is seldom shared for academic purposes. Therefore throughout this research care has been taken to maintain accuracy, integrity and quality of primary data acquired from different companies. Any sensitive information shared by corporate user has been kept strictly confidential. Finally, these survey findings offer startling discoveries and also re-enforce old myths about success and failure of IT projects. After tabulation, graphic representation and summarization of collected data, a list of recommendations has been drafted which gives clear guidelines to all industry stakeholders to develop better functionality in their BI application and improve project success at implementation and beyond.

2. BUSINESS INTELLIGENCE (BI)

A global BI software vendor called The SAS Institute provides the following definition ”Business Inteligence (BI) as Business Intelligence is getting the right information to the right people at the right time to support better decision making and gain competitive advantages” (Waite, 2006). The definition of BI above should be re-worded to include: “the information should also be in the ‘right format’ which will facilitate the receiver of information into making better business decisions.”. Turban et al broaches the subject in the following way (Turban et al 2008):“BI has many capabilities, including reporting and querying, complicated analysis, data mining, prediction, forecasting, and much more. These capabilities emerged from the tools and techniques on which BI is based, and especially from Executive Information Systems (EIS), Decision Support Systems (DSS), querying, visualizations, workflow, operations research/management science, and applied artificial intelligence.”. In the same vein Turban informs that BI applications stretch computing power to the highest levels and pose challenges to integration with other tools such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM).

Data explosion is a pre curser to BI. Another alarming phenomenon at play here is the ‘rate’ at which the data is growing. Let’s turn our attention to the driving force behind BI motions in organization. By and large companies are suffering from unprecedented data growth rates. According to one estimate from the CEO from SAS Institute Jim Goodnight (Goodnight, 2009): “By 2010 the amount of digital information in the world is expected to double every 11 hours.” This can no longer be dismissed as marketing hype. The SAS Institute does extensive research on ‘data growth’. Goodnight’s claim is also thoroughly merited as according to the company website 22% of SAS’s revenue is pumped back into Research and Development. SAS’s CEO bases his statement on an earlier study done by IBM which came out with the same startling statistics about data growth (Lamonica 2007). This leads to one big problem. How to manage and analyze such enormous amounts of data? This is where BI data integration, analysis, reporting and management solutions come to our help (Goodnight, 2009). Data ‘stockpiles’ build up in all industry verticals. To make sense of all that data certain tools are used by BI analysts. BI helps detect fraud at insurance companies to flag fraudulent claims. The retail sector uses Data Analysis tools in BI to find profitable places to put stores and products within those stores. The banking and financial industry use BI to check customer credentials over and above routine credit reports. Using sophisticated BI tools banks can detect money laundering, as mandated by the US



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Patriot Act and Basel II Compliance Regulations. Data Analytics are used to sniff out fraud and to score credit applications as well (Goodnight 2009). No matter what industry one picks, there are companies using some data tool or another. It might not be easy to realize but BI has indeed had a huge impact on our lives in the Twentieth Century.

For people with some BI experience it is natural to have the urge to investigate and explore subjects that are considered problem areas or pain points within the BI industry. The drivers and barriers to BI adoption is a research that addresses exactly that raw cord. Most companies are struggling with the idea of whether or not to start a BI motion in their organization (Khan, 2009). The ones that do take the leap of faith into BI would sooner or later run into implementation problem, not to mention cost and time overruns. To explore what compelling reasons drive (Drivers) an organization towards adopting a BI strategy is the necessary first step towards understanding the BI needs of an organization. To study the Barriers to adoption will give an idea of why projects fail. Nonetheless both angles have good value for a ‘BI consultant’ to investigate.

3. DRIVERS AND BARRIERS TO ADOPTION

3.1 The Drivers

Drivers in this study imply pressure and motivation to adopt. The Business Dictionary (2009) defines ‘drivers’ as “People, knowledge, and conditions (such as market forces) that initiate and support activities for which the business was designed are known as business drivers.” A Partner White Paper by Microsoft (2009) holds a slightly different view: “A Business Driver is a brief statement that defines clearly and specifically the desired business outcomes of the organization along with the necessary activities to reach them.” The Drivers in BI are numerous. Two works on Drivers or business benefits are worth mentioning. One is the Business Pressure-Responses Support Model which postulates that there are four components that put pressure on organizations from the external environment. These so called ‘drivers’ include (Turban et al 2008): • Market related factors such as competition • Consumer demand elements such as speed of delivery • Technology inputs such as innovation • Societal pressures such as government regulation

The list above shows the preliminary set of drivers that any business faces. More focused BI drivers such as Organizational Strategy, Organizational Goals, Commitment to Profitability, Shareholder Value Maximization come in to play at a later stage (Ramamurthy et al 2008).

Organizational Commitment in fact is the key element for an innovation to be adopted and subsequently used (Ramamurthy et al 2008). This includes two aspects. Is there support and buy-in from senior management? And second, is their vision building at the senior management level? BI Adoption is higher if the two stakeholders are on board from the onset of major projects.

Some additional research on drivers is also explored apart from the main studies discussed earlier. A Database and Network Journal article ‘Demand for Business Intelligence Technology Set to Soar’ predicts that the BI license revenue market will double in 2012 to $8 billion growing at a compound annual growth rate of 12.5%. The study attributes the positive future outlook of BI to the digital enterprise phenomenon as the main driver behind this growth rate.

An International Data Corporation study discovered that the BI market moves in 15 year cycles. The first was the main frame era, then came the modern era characterized by end users friendly tools (Vesset and McDonough 2006). The study predicts that the next wave of BI drivers is compliance, competitive pressure, intercompany connectivity and the changing nature of BI projects.

Finally Lowell Brian’s (2007) article New Wall Street Drivers for Business Intelligence in Telecommunications cites his earlier Mckinsey Study titled: The new metrics of corporate performance: Profit per employee, to predict the emergence of new driver sets of BI as the practice to



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calculate profit per employee gains traction. This is a measure of the value intangible assets such as the value-add from a knowledge worker. The report reveals that such is the need for information of these knowledge workers that a complete set of ‘shadow IT departments’ have arisen to cater to them alone (Brian, 2007). Due to this need BI apps have gone from a ‘nice to have’ to a ‘must have’. Information needs are driving the BI band wagon (Brian, 2007).

3.2 The Barriers

Barriers ‘restrict’ while drivers ‘encourage’ organizational adoption of IT systems (Chaffey, 2008). Most of the literature review available in academic databases concerning barriers centres on the discussion around ‘barriers to entry’ and trade barriers which it was felt were not relevant to this discussion. In addition a lot of work has been done in the field of organizational barriers with regards to communication problems. This again is not really in our domain of business or managerial barriers.

Let’s discuss the role of barriers in the specific BI domain. Business Intelligence Guide (2009), an aggregation of all accumulated research papers on BI points out that even though Business Intelligence it is the most highly desired technology spanning a $10 billion a year market and growing at 10% a year, it still suffers from a ‘relative inability to prove its value’.

As if this is not enough the Guide (2009) announces that the main barriers to BI adoption are ‘cost’ and ‘complexity’. This is further compounded by the fact that a 2007 study by Information Week cited in the Guide reveals that in a survey of 388 business technology professionals, over 30 percent of respondents claimed that BI vendors were ‘unable to demonstrate the benefits of BI to internal stakeholders’. Most companies considering BI are being hounded by a certain business problem which invariably lies in a ‘specific’ business unit. The resulting BI/data storage initiative then creates business silos which prevent cross company examination of data sets residing in disjointed IT systems (Guide, 2009). This lack of cross organizational data analysis capability is explained by the fact that there is no single vendor that excels in all areas of business intelligence leaving it up to the customer to pull together various business components. The result is that client organizations excel only in their specialty areas such as in managing customer churn or in predictive analytics (Guide, 2009). The Guide (2009) further notes that 40% of the cost involved in developing sophisticated analytics and modelling for BI projects comes from ‘moving data between systems’. This means that data migration and integration becomes the single most potent ‘barrier’ to BI adoption. Suggestions on how to overcome these barriers are found in the work of Ocampo titled ‘Overcoming Barriers to Business Intelligence Success’ which underscore the need for having simplicity in analytical tools (Ocampo, 2007).

Finally an Economist Intelligence Unit Study (2007) lists the following barriers or problems quoted below: • Departmental silos remain the biggest barrier to data sharing with 63% of executives agreeing • New obstacles such as data access and clean data are also causing problems with 41% respondents

agreeing • Employee resistance to adoption of new technology, fear of misinterpretation of data with 78% still

using old spreadsheet technology • Lack of CIO participation in decision making process with only 22% companies who allow CIO

involvement

Another set of barriers to adoption are the organizational efficiency issue which plague many companies. R.L. Fielding (2006) in a Guide to the Top 5 Issues That Misguide Business Intelligence Decisions lists the following obstacles or issues that BI implementations face with his very practical suggestions: • Usability verses feature mismatch: too many feature too few being used • Enough already about metadata: Do not invest too much in costly metadata management • Do not pay for expensive BI consulting

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• Licensing, Upgrades and Maintenance should all be in a flat fee model • Resume Building: Don’t go for the brand name only

R. Faley (2004) in his barriers study Difficulty with Data Warehousing tries to establish a link between gender and ease of use perceptions. Faley’s study was not able to find significant differences between men and women in terms of success with BI implementations. The study however did establish that women were more likely to associate data warehouse flexibility with ease-of-use than men. This means that in contrast to conventional wisdom women CIO may have a better chance at successful BI implementations.

3.3 Adoption

Since our research is about measuring ‘levels of adoption’ let us turn our attention to the word adoption for a moment. Shoemaker and Rogers (1971) who are the most quoted researchers on the subject by adoption analysts define the term adoption as: “Making full use of a new idea as the best course of action available.” The quoted below have to be true for this definition to hold solidly (Shoemaker and Rogers, 1971): • The defined idea or meaning holds in different settings • A comparison is possible in terms of what use the idea is put to in different settings • There has to be a yard stick to judge if the idea is the best course of action

Item 1 and 2 above help stake the claim of Generalizability of Adoption related measurements and item three holds a value judgment on which our analysis rests. Generalizability of survey findings and clarity of values are necessary for quality research (Eveland, 1979). Adoption may have variants such as ‘active’ or ‘passive’ adoption (Eveland, 1979). In measuring adoption there are two requirements (Eveland, 1979) • There should be a single decision that testifies to adoption having occurred. • There should be a ‘measurement scale’ of commitment to measure level of adoption

This helps explain why a Likert scale was used in the survey instrument DBM of our research as it measures both commitment level and degree. The process of Organizational Adoption (OA) is a complex phenomenon according to Deshpande (2005): “An organization needs a process to build the capacity, capability, and commitment of the internal user community to move from an organization’s current state to its future state with the introduction of the new solution.

Adoption issues have been studied by Information systems (IS) researchers to gain better insight into management perspectives. Hwang’s (2005) work which was based on ERP systems is unique in the sense that it uses ‘informal controls’ to study adoption issues. Informal controls can be many such as cultural control and self control. Hwang used ‘uncertainty avoidance’ and ‘perceived enjoyment’ as informal controls in his research paper which he then linked to the ‘technology acceptance’ variables to investigate the relationships among them (Hwang, 2005).

Adoption researchers also focus on ‘diffusion of adoption’ within organizations. Adopters are of various types, early, late and non adopters all have different attitudes and time frames for accepting and learning about new technologies (Ramamurthy et al 2008). This diffusion of adoption has been the main focus of Fichman (1992) in his extensive review of empirical research titled Information Technology Diffusion. Fichman (1992) proposed a typology of IT innovations based on Types of Technology. According to him a Type 1 technology is that which imposes relatively low knowledge burden and fewer user interdependencies. Type 2 innovations impose high knowledge burden and high user interdependencies. The key driver to Type 1 adoption is merely a ‘willingness to adopt’. Type 2 is complicated. It actually requires an ‘ability to adopt’ (Fichman, 1992).

BI technologies generally fall in Type 2 technology category. Hence a high ability to adopt is required of adopters. Ramamurthy, Sen and Sinha advocate that Type 2’s technology burden can be mitigated if an organization has high absorptive capacity (well developed knowledge management of IT systems).



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Therefore Type 2’s such as BI have a fair chance of adoption even in low ability to adopt environments. This provides a ray of hope for both researchers and vendors frustrated with challenging low adoption environments especially in large governmental organizations (Fichman, 1992).

Let’s look at the studies around drivers and barriers also. Arnott and Pervan’s (2008) insightful paper titled Eight Key Issues for the Decision Support System Discipline actually puts the blame on the weakness of research methodology applied to study DSS systems to explain the lack of quality scholarship on the discipline so far. The most noteworthy reasons exposed by the paper on why we have poor DSS research was lack of funding, lack of DSS exposure and the inertia and conservatism of DSS research agendas (Arnot & Pervan, 2008). Another set of barriers could come from IT related problem. An e-Zine article by Clare Bryan (2009) list the IT problems inherent in BI projects including data inconsistencies, poor data quality, and an ad hoc approach to installing BI systems. The same article says that only 43% of executives adopt BI as a strategic initiative. Mostly there is no centralized strategy to adoption and BI tools are used in isolation.

4. RESEARCH METHODOLOGY

A survey of pedagogical texts in BI was performed and significant trends were identified in the business intelligence field. The objectives from interviews and survey were to identify which drivers propel the business intelligence initiative within organizations and which barriers that block that adoption. One objective was to answer the question: What are the types of users and their preferences and attitudes towards BI? Another objective was to examine some of the problems faced by users.

4.1 Research Context

The research methodology used comprises of in the first instance major discovery work around e-commerce done by Dave Chaffey in his book: “E-Business and E-Commerce Management”. In the introductory Chapter of his book, “Business Adoption of Digital Technologies for E-Business and E-Commerce” Chaffey mentions a study by Booz Allen Hamilton (UNCTAD, 2002) regarding consumer perception of online purchasing. The study discovered certain barriers to adoption such as lack of trust and security problems. In contrast a Drivers Study by DTI (2002) as cited in Chaffey (2008) is of the view that the biggest motivator of e-commerce adoption is an ‘eagerness to reduce costs’ instead of ‘shortened delivery time of purchased products’. In fact that is the least important driver in the study. So the Drivers and Barriers Methodology (DBM) is a powerful analytical tool. It also helps in ranking what variables are more important and which variables can be ignored. It helps managers calibrate their priorities and roadmap their strategy initiatives. This ranking can be the basis of project implementation plans and a number of other business and strategic uses. From the technical point of view this study will help in determining what capabilities to develop first when setting up new project plans.

The DBM methodology has been applied to the Business Intelligence world and similar to how the e-commerce drivers and barriers were surveyed and tabulated to reveal a ranking the same way BI drivers and barriers ranking is revealed. The measurement system used in the ranking was the Likert scale with strongly agree, somewhat agree, neutral, somewhat disagree and strongly disagree as the ordinal scale points.

In order to choose a methodology a review of prior research was performed. The anchor piece in this was the (Ramamurthy et al 2008) research on the Key Determinants of Data Warehouse Adoption. In fact one of the objectives of the research was to validate previous research done on this subject and the work of Ramamurthy, Sen and Sinha provided exactly that. Most of the measurable drivers laid out in the above research were taken quite literally in the survey questionnaire and tested to check for two result sets. First was to see if the results of the survey are coming out to be the same now as they did



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before. Second was too see if the results were the same when tested in the Pakistan market since the original Ramamurthy, Sen and Sinha research was conducted in the more sophisticated US market?

In the developing the parameter on which attitudes are judged it was felt that ‘nature of use’ the candidates derived from a BI application was the prime shaper of attitude. Research linking ‘user type’ to ‘attitude towards use’ has not been examined but a relation has been assumed to exist intrinsically. Naturally opinion towards a particular technology varies according to the use one derives from it. So a business analyst will have a different opinion profile to BI applications versus let’s say a ‘non user’ or an ‘occasional user’. Among user types it is the executives that derive the most business value from the BI tools followed by Power users and Consumers. This gives us some idea of which user type are the highest stake holders in the BI game.

4.2 Research Method

BI apps are typically seen in large organizations with well developed structures and extensively codified corporate cultural. No two organizations are the same in terms of attitudes towards technology initiatives. Therefore sensitivity to the varying degrees of opinion in organizational environment should be the pivotal element in any enterprise system study (Hwang, 2005).

At the same time behaviour scientists who study attitude objects (BI technology in our case) within individuals and organizations have recently shown support for the Technology Acceptance Model (TAM). TAM has shown considerable promise in explaining complex adoption and implementation issues faced by stakeholders and end users (Amoako-Gyampah and Salam, 2004; Gefen, 2004). TAM related research mainly concern ERP systems but can be extrapolated to apply to BI systems. In the final analysis they are all MIS systems in the enterprise. TAM is one of most widely applied IS model to explain end-user adoption of IT (Davis, 1989; Hwang, 2003) based on statistical correlations between adoption levels and ease of use, usefulness, and intention to use.

The most important aspect to this research was the use of scaling techniques. Since our drivers study does not do a ranking of the drivers between themselves it is a non comparative scaling situation. The variables are measured are not continuous but itemized. Likert seemed a better choice in comparison with Semantic Differential or Stapel scales for our theoretical constructs such as driver, barriers, since the research required an individual rating on each item (Malhotra, 2004).

The overarching research methodology was taken from pedagogical text of Cooper and Shindler (2003) titled Business Research Methods. The diagram below (figure ) is a self explanatory methodology flow diagram of our research method. It has been mostly adapted from Cooper and Schindler (2008) but describes in detail how the research was actually carried out.



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Figure 1. Schematic of Research Methodology. (Source: Adapted from Business Research

Methods, Cooper and Schindler, McGraw Hill 8th Ed. 2003.pg-401)

As the workflow in the diagram above shows, we began with an investigative question. Next step is to select an observation approach, which is followed by definition of metrics and survey instrument. This leads to an aggressive data collection phase with data validation and error checking. The processed data is then used to validate or invalidate assumptions made at the inception of the study.

Since a research of technology adoption requires several stakeholders to work in unison, the partnership framework for successful technology deployments has been reviewed. This framework postulates that project success is pivoted around the harmonious relationship between processes, people, culture and systems (Atkinson, 2007). BI systems follow much the same philosophy shown in figure 2 below:

Figure 2. IT Project Partnership Frameworks. (Source: Adapted from: Maurice Atkinson,

Measuring Business Excellence, Emerald Publishing, 2007 Vol. 2 Pg. 11-22)

In the aforementioned IT Project Partnership framework, the project success is tied to the following four factors: processes, people, systems and culture. The research mind map for constructing the methodology for BI adoption is given below in figure 3:

Investigative

Question

Measurement Question

Select Observation

Approach

Survey Instrument Interview Questionnaire

Collect Data

Process Data

Business Intelligence Web Analytics

Report

Check for Errors



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Figure 3. ”BI Research Mind Map”

The mind map provided different facets involved in the research process, as they relate to business intelligence research and methodology. In-depth CIO interviews were conducted , looked at perception sentiment, IT analyst reports, vendor minefield, and data collection accuracy and tried to understand the opinions of power users, executives and non-users. A triangulation method was used for integrating and verifying the results of the survey findings with secondary data and in-depth interviews.

4.2.1 Survey

Adoption is usually measured on a Likert scale with values of varying intensity (Chaffey, 2008) The 5 point Likert scale is considered by far to the most suitable instrument to measure the ‘degree of adoption’ (Malhotra, 2004; Dwivedi, 2007). Hence this research uses the same scale to measure the Drivers and Barriers of BI adoption. 71 questions were devised in the survey. They measur the qualities such as probable drivers, barriers, problems and issues in BI adoption. This survey along with a set of demographic questions and an identification of user type (question) became the measurement instrument. As mentioned earlier the Likert scale was use to measure the degree of preference whether positive or negative. The total vector sum of the composite preference was calculated for a certain user type such as ‘Power User’. Once the direct questions were drafted a survey form was constructed. Instructions on how to fill the survey were put into place. These included the purpose, title and the time required to complete the survey. An explanation of complex terms in the survey was also provided in the footnotes.

The following calculation has been performed to arrive at a figure for the sample size of the survey: Sample Size =ss; ss = Z 2 * (p) * (1-p)/ c 2 Where: Z = confidence level=95%, p = percentage picking a choice, (= 0.5 used for sample size needed) c = confidence interval=8% Sample size = 148.5 Survey Respondents

4.2.2 CIO Interviews

Remenyi, 2008) and to see whether any disparity existed between the results of one type of instruments versus the other. Face to face interviews givethe opportunity to discover unexpected



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results not possible in a confined setting of a survey questionnaire. Also since the responses are not pre determined the respondents can answer any way they feel, sometimes giving out a treasure trove of information that was not original have been anticipated.

The personal structured interview was a pre-determined selection of questions which were emailed to the CIO a day in advance. Most of the interview questions were taken from an interview conducted by Zoeb Adenwala featured in a 2007 article titled, “BI an Ace for CIOs.”In this interview a CIO, from Essel Pro Pack, a paper products company in India was experimenting with new generation BI apps which promised plug and play capability. The CIO was facing problems with lack of data insights about his customers and suppliers. The interview reflected the next generation thinking by CIOs in the BI industry and as such presented a very appropriate instrument for this study. A few Chaffey (2008) and Ramamurthy et al (2008) questions were also added to the interview as well as rather general question regarding the IT industry in Pakistan.

4.3 Research Constraints

All data collection approaches in academic research have ethical implications (Remenyi, 2008). Since my research collected B2B data there were also issues with corporate permissions to conduct the survey. Some organizations felt that the survey was cover for a performance appraisal being done by management. Government owned banks had issues with revealing any information whatsoever and were slow in submitting completed forms. There were issue with privacy and confidentiality in handling of data. No one accepted the survey without the Authority Letter attached. Issues of informed consent were also given due consideration. Responses from one organization were not shown to the other. The survey had no field for name and organization. This was the requirement for most companies before accepting the survey. The results will be sent to all those who have asked for a copy. Care was taken to minimize ‘leading questions’ in the survey and interview both. Handicapped respondents were given an assistant to help them fill out their forms. No respondent was paid for filling out the forms to avoid bias.

In personal interviews there are many ways in which 'errors' can be made by both the respondent and the interviewer, and this can lead to 'bias' in the results. The objective of the interviewer should be to minimize the likelihood of such bias arising (Crawford, 1997).

Scarcity of data and in particular data on the BI industry of Pakistan is nonexistent. Even Gartner is yet to publish a paper about BI in the country. The much trumpeted purchase of 26% of stock in the nation’s landline carrier, Pakistan Telecommunications Company Limited (PTCL), by the Dubai based group Etisilat in July of 2008 finally prompted Gartner to open a small office in Islamabad. It will be some time before they publish anything on BI in Pakistan. The small nuggets of information about the BI industry reside only in the internal documents of either the BI vendors operating in Pakistan or some industry watchdogs. In short BI expertise are low and the statistics unavailable.

The authors’ estimate that there are only 30 ‘real’ clients for BI in Pakistan and slightly over 200 hard core BI professionals. The bulk of the clients are major banks and telecoms with some government and educational institution as well.

5. RESULTS & DISCUSSION

This section chapter takes the findings from the Survey & Interview Results given in Appendices to perform an analysis of the questionnaire and demographic data collected. Data is analyzed using separate compilations for each ‘user type’. These individual compilations are then aggregated into a final score known as the Grand Composites. These scored reflect the overall user opinion on drivers and barriers to adoption. Recommendations are given based on the research findings for all stakeholders in the BI industry. In the end the possible directions for further research are explored.



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5.1 Country Specific Issues

An extensive literature review of the various research works conducted around the globe in the area of Business Intelligence does not elaborate on sensitivity around the culture element of BI user types as regards the uniqueness of opinion in a particular country or region. The thrust of the research has been on challenges that are ‘common’ to information management initiatives around the world and not to a particular nation or culture. Given the fact that BI is such a highly specialized knowledge area, the consideration of country specific variation in survey or opinion results on BI related subjects holds little weight. Firstly, BI has been the domain of large corporations. Most large companies have several offices and maintain a national or international presence (BRAC, 2009). Their workers are not confined to one country but part of the global workforce. Modern companies are structured in a way that even though the data is gathered at a centralized repository, the sources of this data are scattered all over the globe (BRAC, 2009).

The case of Pakistan is not much different especially for the BI industry. Most BI customers in Pakistan are regional companies if not global. Telenor for example is a European telecom operator doing business in Pakistan and developing a strong BI strategy. Secondly, a handful of BI specialists, vendors and client companies that make up the small ‘Business Intelligence’ community of Pakistan are not any different from your global IT or business professional. Their data needs and user habits essentially mirroring their neighbouring country counterparts. Thirdly, IT is not like law or marketing where each country has a different code or a pattern of consumer behaviour. All over the world IT applications are the same. In the light of the above arguments the research does not go too deep into the treatment and analysis of Pakistan specific components such as the cultural dimension of user behavior. The assumption is that there is no fundamental change in findings just because the test market is Pakistan.

5.2 Survey Results Discussion

A prototype questionnaire had been floated in July 2009. The survey was filled by 182 candidates over a period of 25 days. Survey questions are given in the Appendix. Some results came in later but were also included at the time of data tabulation.

Each type of user identified in the survey will be taken and the results in each of our 8 main question categories will be discussed. It is not possible to discuss all 64 bar charts for each user types. Hence the combined cumulative discussion. Non users, extra net users or consumers form the largest part of our survey group after IT users since Turban (2008) declares them the biggest user type in existence.

The results are not presented in the typical Likert scale method of colored sliced bars showing percentage of scores on the 5 point scale. Instead a cumulative bar chart is developed for each sub category. This was done since with the traditional Likert representation method, it was rather difficult to tell clearly which were the most important drivers or barriers in a particular question category. We will however discuss in detail the ‘Composite’ scores and demographic results in this section. See Applendix C for each chart.

The aggregate of all scores on each question in the survey and the broader category of user for each set of drivers, barriers, problems and challenges is examined. In the drivers category highest rank went to’ increased business competitiveness’ followed by ‘risk mitigation’ as shown in ”BI General Drivers” Appendix C. This was an expected result since these are rather simple and positive questions on the survey. In today’s post Sarbanes Oxley Compliance world these governance questions resonate with all users. The second place offered to ‘rich reporting capability’ is also befitting in the sense that BI is all about reporting and it is a basic driver. The role of the website does not have much significance in vendor selection as shown In the specific drivers arena the following scored high: • Better and faster decisions • More efficient service • Improve enterprise performance



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• Predict Market trends

This is shown clearly in”BI Specific Drivers” Appendix C. All results for specific BI drivers are in line with expectations except ‘predict market trends’ which proved to be a bit of a surprise. Perhaps due to aggregation a middle scorer eventually made it to the top. It must be mentioned here that the forecasting trend in the BI market is certainly pushing this driver up the hierarchy. Current and accurate information scored low points overall. The reason could be that the bulk of user are concentrating on more advanced functionalities and downgrade the importance of basic ones. Only Executive User tend to rate this high.

Barriers that all users voted as the most significant was ‘data security concerns’ preventing BI to be pervasive and hence impeding adoption. This is definitely in line with expectation. Though newer regulatory environments dictate information access, organizations themselves feel information secrecy is almost a ‘best practice’ when it comes to internal data (Howson, 2007). ‘Upfront costs’ for most users are a barrier which is fine since most BI applications cost millions of dollars in total cost of ownership.

Two findings here are probably indigenous to a developing country market such as Pakistan. One is the tendency to blame everything on the ‘lack of government support’ which is what most users did. Second is the constant complaint about ‘lack of technology infrastructure’ and a ‘trained work force’. All of these were given high scores by users as possible barriers as shown in ”BI General Barriers” figure.

In specific BI barriers fragmented data sources took the cake while BI being too specialized was considered another barrier. This is due to several non users skewing the results. BI project complexity came in second followed by fragmented data sources as shown in figure ”BI Specific Barriers”

Challenges felt by users were the scarcity of ‘business sponsors’. Users felt that business is not fully supporting their drive to implement BI. Cross organizational collaboration and dedicated business representation remained a challenge for most users as shown in figure BIT IT General C (Challenges) Barriers”.

Problem identified as significant by all users were on data quality and multi-vendor integration. Preferred vendor syndrome proved to hold its ground in the all user category as well as shown in figure”BI IT General (problems Barriers)”.

Lastly Ramamurthy et al (2008) were validated to a considerable extent by several users. Organizational scope for DW was rated the highest followed by data environments as the key determinant of BI-DW adoption. Organizational size and capacity came in second. The complexity of DW was significant as a barrier even for all user aggregated as shown in figure ”BI Previous Research Validation (Key Determinants)”

The distributions of User Types which are the pivotal focus of this research are summarized below in figure 4 together with their industry sectors. Most users of BI especially Power users come from the banking or telecom industry.



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Type of Users

Industry Sector of Users

Figure 4. Distrubution of users by type and industry sector.

5.3 CIO Interviews

The CIO interviews are one of the most interesting sections of this research and provide valuable findings for BI researchers and professionals. Two CIOs conceded to giving an interview on Business Intelligence. This is understandable in the sense that BI motions are a competitive advantage tool. Most organization in Pakistan though rigorously perusing a BI initiative would not like to divulge that information to the ‘academic press’ from fear of the competition guessing their strategic moves. The interview questions are provided in the Appendix. Since BI industry is mostly driven by Banks and telecoms in Pakistan one CIO from each industry was chosen. The results of interview transcripts were further triangulated with the survey results.

6. CONCLUSION

The objective of the study was to ascertain the drivers and barriers to business intelligence adoption.. Drawing upon the vast body of research in organizational theory and behavioural science (as applied to IT adoption) we came up with a list of operational constructs which were converted into survey questions. We supplemented the survey questions with personal interviews to strengthen the quality of our primary data. A comprehensive literature review of previous research in the field was performed. One research was identified as the anchor piece and it was also included in the survey for validation of results.

Different user types were analyzed for their behaviour on all of the 8 question categories and their responses measured and recorded. The scores of all users were then combined into a grand composite score and analyzed further to see what patterns emerged. These patterns were discussed at length in previous section. Results from the survey and interview questions are in Appendix A for reference. We saw the drivers and barriers change with each user type (Appendix C). We also saw the identification of challenges and problems also change. On composite scores we witnessed a more balanced score count. Even in composite score there were a few surprises and discoveries. There were also some disappointments over results that did not match with expectations. The most important findings from questionnaire were the insight into the attitudes and opinions of each possible user type in BI applications. Historically, research in enterprise adoption has focused more on the overall adoption but have rarely examined acceptance from the point of view of the user. We made some interesting discoveries. Non users for example do not make selection of a BI vendor based on website quality and advocate customer service improvement as prime driver and lack of awareness as a major



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barrier. IT surprisingly attached little importance to cost but wanted more analytical tools and functionality. In addition IT also admitted that the perception that BI has no real tangible benefits makes it harder for them to convince business to releasing project funding. As custodians of data naturally they feel that security concerns impede BI adoption.

Executive users insisted on competitiveness and data insight as the main drivers. Since execs are mostly responsible for a company’s performance they feel the pinch of cost escalation and lack of man power much more than other user types. Execs require rich reporting and see inconsistency in data as a big challenge in BI projects (Appendix C).

Power Users being at the high end of application functionality usage believe that shortage of analytical tools is only a market perception. Even business analysts are answerable to stakeholders and need to focus on bottom line profitability.

All users feel BI gives them increased business competitiveness and reduces financial and operational risk. BI helps them make better decision and at a faster rate. It improves their service quality and helps them see trends in data. Data security in BI motions remained a top issue and project costs, a major area of concern (Appendix C).

Finally, an area of managerial importance is that of ‘collaboration to ensure project success’. All users identified this area as largely neglected and felt that more sponsorship was needed from business to ensure success (Appendix C).

From Ramamurthy, Sen and Sinha’s (2008) ‘research validation’ we learn that enterprise adoption has a political element to it. According to them this is related to ‘turf protection and control’. Also influencing adoption scenarios are factors such as user resistance, power struggle and lack of cooperation. Our research quite remarkable identifies the same aspects from different user types. Cross organizational collaboration issue being a case in point. Political issues around BI project could assume greater proportion because they are expensive and time consuming (Ramamurthy et al 2009). We can assume it means that our findings validate those of Sen and Sinha (2008) to a considerable extent (Appendix C).

The user types of Turban (2008) stand validated as marked differences appeared in the results from one user type compilation to the other. Chaffey (2008) e-commerce adoption drivers and barriers also stand validated as those factors that were chosen from the list to be tested for BI all scored fairly high on the charts. The study also helps in understanding the delay factors behind the so called late adopters (Ramamurthy et al 2008).

The interviews served as concrete data on the practical aspects of BI implementation collected from hands-on CIOs who anticipate every possible challenge in such projects. Their advice unmistakably was to get business buy-in from the start and to be aware of project scope creep.

The aggregate scores revealed that there is a correlation between the driver, barriers and adoption levels. Challenges and problems results were also statistically significant to prove our hypothesis as correct.

7. RECOMMENDATIONS

Recommendations based on the findings of this research that can be given to industry are manifold. These findings are very useful for application designers following the philosophy of user centred design approach. IT engineers can incorporate better functionality and productivity in their design.

Vendors can see it as a market penetration tool. It will also give vendors a priority list of problem areas to focus on in application development and roll out of new versions. This research is useful for marketers trying to determine what sort of messages will resonate with what type of user in the organization.



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For regulators this research provides insights into how and what is driving regulation compliance when it comes to reporting standards. Government officials can find out what the workforce feels are important issues in the industry. Their development plans can use the research to identify gap areas and plan new projects.

Finally companies can analyze the problems and issues faced during major IT projects and anticipate hurdles in advance to minimize negative business impact. It will also help them address managerial and team building issue in BI projects since this is another area addressed by this research. .

Further research is needed to see actual adoption levels on a percentage scale versus just the level of ‘intent’ of adoption. More research is needed to see how drivers and barriers change at each stage of the project cycle. More C-level and functional manager level opinion need to be solicited using the personal interview technique. The Power User type needs a more elaborative study on user behaviour.

Research using larger representative sample would certainly provide more accurate results. Multi country administering of the survey instrument would appeal to a larger global audience. In contrast, more country specific research also needs to be performed to see any cultural element affecting adoption scores.

In terms of methodology a more IT specific framework needs to be developed for further research to standardize on. More empirical testing is required to see the degree of correlation between business drivers and barriers vis-a-vis the adoption cycle. In addition to adoption measurement further research can focus on a multidimensional study of drivers and barriers linked to such variables as sentiment, perception, customer satisfaction and propensity to purchase.

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Apendix A Survey Form A-1 Title: Drivers& Barriers to Business Intelligence Adoption Instructions: The following survey is on ‘Business Intelligence (BI) Adoption’ in a corporate enterprise such as a Bank or a Telecommunications company. For each of the following questions indicate your favored response by putting a cross in the box next to the statement. Estimated time to complete survey is around ten minutes.

Statement

Strongly Agree Agree Neutral Disagree Strongly

Disagree

Drivers The drivers of BI include: BI General Drivers are:

Reduced cost of ‘information analysis’

Desire for increased ‘business competitiveness’

Desire for increased ‘profitability’

Enterprise wide data driven decision making capability

Availability of data analysis tools

Risk mitigation (Financial or Operational)

Rich reporting capacity

Optimization in resource allocation

Deeper data insight

Organizational efficiency (Financial/operational)

Vendor website role in BI buying decision

BI Specific Drivers are:

Rapid Change in data volumes lead to a need for BI

Governance requirements (IT & Corporate)

Stakeholder Demands

Expanding on ERP (Enterprise Resource Planning)

Data availability readiness

‘Forward – looking views’: Forecasting

Ensuring alignment with corporate strategy

Effective decision making at all levels of company

Predict market trends

Improve enterprise performance

‘Single version of the Truth’

‘Current and accurate information’

Rapidly Changing Information Needs

Customer service excellence

More efficient service

Increasing service costs

‘Better and faster decisions’

Statement

Strongly Agree Agree Neutral Disagree

Strongly Disagree

Barriers The barriers to BI include: BI General



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Up front cost

Set up cost

Running cost

Lack of skills to implement BI/ Data warehousing

Lack of exec. board interest

No real or tangible benefits

Poor ROI (Return on Investment)

Lack of knowledge about BI products

Lack of technology (pre -BI infrastructure)

Data security concerns (Pervasive BI and Outsourced version)

Insufficient government support for BI Initiatives

Lack of a complete BI Suite offering by any vendor

BI Specific

Typical BI systems not optimized for OLTP [1]

Frequent data latency issues

Implementation time lags

BI project complexity

High costs of OLAP[2] based systems (BI tuned)

BI tools highly specialized for wide spread use

Complexities of data management

Fragmented data sources in the enterprise

IT General General Information Technology ‘challenges’ & ‘problems’ around BI projects include: Are these challenges?

Evolving business model (keeps changing)

Unique data model (keeps changing)

No standard market definition of BI suite

Cross –Organizational Collaboration (Difficult)

Business Sponsors (Few)

Dedicated business representation (Low)

Availability of skilled team members

BI ‘Application Development Methodology’

Planning BI projects

Business Analysis and Data Standardization

Impact of ‘Dirty Data’ on business profitability

Statement

Strongly Agree Agree Neutral Disagree Strongly

Disagree

Importance of Metadata management

The Silver Bullet Syndrome [3]

Are these Problems?

Inconsistent or poor quality data

An ad hoc approach to installing BI systems

Preferred Vender Syndrome

Multi-vendor offering integration

Previous Research Validation: DW Adoption research now being applied to a BI Adoption Scenario



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Are the following ‘Key Determinants’ of BI Adoption?

Absorptive Capacity Of Organization (How companies assimilate and apply IT effectively to realize economic benefits)

Organizational Size (IT funding and date volumes become key criteria)

Organizational Scope for DW (Mere attraction to IT innovation's superiority causes hasty decisions and non retractable investment)

Organizational Data Environment (Reducing errors and increasing the ability to access previously unavailable information)

DW’s Relative Advantage (Degree to which an innovation is perceived to be better than the one it replaces)

Is the following a ‘Barrier’ of BI Adoption?

DW’s Complexity (Difficult to understand for most)

[1] On-line Transaction Processing (OLTP): Refers to a class of systems that facilitate and manage transaction-oriented applications, typically for data entry and retrieval transaction processing. OLTP has also been used to refer to processing in which the system responds immediately to user requests. An automatic teller machine (ATM) is an example of a commercial transaction processing application. The technology is used in many industries, including banking, airlines, mail-order, supermarkets, and manufacturing. Applications include electronic banking, order processing, employee time clock systems, e-commerce, and e-Trading. (Source: Transaction Processing Performance Council Website, http://www.tpc.org, accessed, August, 2008). [2] On-line Analytical Processing (OLAP). OLAP is part of the broader category of business intelligence, which also encompasses relational reporting and data mining. The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting etc. (Source: Online Transaction Processing Council Website, http://www.symcorp.com, accessed, August, 2008). [3] Silver Bullet Syndrome: Silver Bullet Syndrome is excessive reliance on a single development improvement to put a development project over the top. It's a reference to the Lone Ranger's one-shot problem solving. More generally, silver-bullet syndrome is the projection of a multivariate problem onto a single (typically novel) axis, with insufficient factoring of other dimensions of the problem. (Source: Fox Wikis, Common Schedule Risks in Software Engineering, http://fox.wikis.com/wc.dll?Wiki~SilverBulletSyndrome~SoftwareEng, accessed Aug 14, 2008) [4] Previous Research Validation: Ramamurthy, K., Sen, A., & Sinha, A. P. (2008), An Empirical Investigation of Key Determinants of Data Warehouse Adoption. Decision Support Systems, 44(4), 817-841.



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Survey Form A-2 Title: Drivers& Barriers to Business Intelligence Adoption

Personal Information ○ Male ○ Female Age ○ 0-20 ○ 31-40 ○ 51-60 ○ 21-30 ○ 41-50 ○ 60-above Please list any IT or BI related certification(s) you have obtained Indicate you position within the company ○ C. I. O ○ IT Manager ○ Business Manager/Middle Management ○ VP /Regional Head ○ Director Board ○ Entry level position Estimated IT Budget (Annual) of your company in pound sterling (£) 0-50,000 100,001-500,000 1 Million-5 Million 50,001-100,000 500,001-1 Million Above 5 Million BI ’user type’ you identify with? □ IT Staff □ Occasional Information User □ Executives User □ Extranet: Partners, Consumers □ Functional Managers □ Power User, Business Analyst (BI) Check any IT- Business Initiatives underway or in the pipeline at your organization? □ Business process improvement □ Reducing enterprise cost □ Improving enterprise workforce □ Targeting customers and markets more effectively □ Expanding current customer relationships □ Attracting and retaining new customers □ Increasing the use of information □ Creating new products or services □ Managing change initiatives □ Expanding into new markets and geographies List major Business -IT ‘pain areas’ (problems) in your organization? Industry Sector you are in? □ Banking □ Information Technology □ Telecommunication □ Government □ Health □ Other



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Appendix B CIO Interview Questionnaire CIO:20 Questions

1. Gartner rates Business intelligence as number two in the list of priorities for CIO’s in 2009. Is that your shared perception? How about the situation in Pakistan?

2. The new vendor motto is "Making BI Easy", what does that really mean for you? 3. What are some of the Challenges and Problems you faced during the BI motion at your

company? 4. What were the Drivers to BI Adoption at your company? 5. What were the Barriers to BI Adoption at your company? 6. How far can you go in BI innovation; are there only so many sensible ways to deal with data

sets? 7. What about the open source applications, like Pentaho? Are they a competitive threat in the

BI market? 8. Microsoft is entering the BI market. What sort of threat are they to both the niche and

established players in the market? 9. What kind of business applications are there at your company? 10. How is your experience in handling BI projects till now in various companies you have

worked with? 11. How did you select the BI vendor, which were the key benefits in the BI application you

selected? 12. What functional area’s did you target and why? 13. What are the benefits which you got with BI? 14. What was your implementation plan and how many people from your team were involved? 15. Is the top management involved in BI decisions and reporting? 16. Are giving the BI capabilities across the enterprise or restricting to business analysts? 17. In coming years how many BI users will be there in your company? 18. Something which you would like to share BI wisdom with your fellow CIOs? 19. Is BI time consuming, costly & complex to handle? Your views? 20. Final words on Industry wide BI adoption in coming future in Pakistan?



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Appendx C - Survey Composite Scores (combined data from all types of users).

BI General Drivers

BI Specific Driver

BI General Barriers

BI Specific Barriers

BI IT General (Challenges) Barriers

BI IT General (Problems) Barriers

BI Previous Research Validation (Key Determinants

BI Previous Research Validation (Barrier)

DRIVERS AND BARRIERS TO BUSINESS INTELLIGENCE ADOPTION: A CASE OF PAKISTAN

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