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  1. 1. International Journal of Management (IJM), ISSN INTERNATIONAL JOURNAL 0976 – MANAGEMENT (IJM) OF 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME ISSN 0976-6502 (Print) ISSN 0976-6510 (Online) Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME: Journal Impact Factor (2014): 3.2150 (Calculated by GISI) IJM ©IAEME BUSINESS INTELLIGENCE OVER CLOUD Apeksha Hooda Assistant Professor, Amity School of Business, J1 Block, Sector 125, Amity University, Noida, Uttar Pradesh-201301 ABSTRACT It was the time when in a particular sector there were only 2-3 companies and competition was not as stiff as today. Each of these was having its own niche product and niche market. Data with the companies was also of smaller size and easily manageable which could be easily analyzed to identify hidden trends in the business to make effective strategy. But now things are getting changed and with the liberalization and globalization more people started moving towards manufacturing and service sector. More and more Multi National Companies are coming up leading to tighter competition among domestic players in providing quality product and service in time. In this era of high competition where Adam Smith way of doing work has become obsolete, and companies are now looking for new ways of doing work, organizations have now started moving towards business intelligence tools to analyze their continuously growing data which is multiplying exponentially and here lies the significance of BI to explore the company’s big data to tap right opportunity at right time at right place to provide right service to right people, then only can a business stay ahead in global competition. While it will be easier for companies to gather and analyze the data with the stress on BI efforts, the challenge and opportunity will not be limited around collection of big data but the ability to translate it into strategic business decisions and demonstrate the right return on investments. To ensure this Business intelligence needs to move at the speed of business. Managing such a big and complex data is getting very tough job. Therefore comes the idea of storing data and running BI over cloud. The use of Business Intelligence (BI) in the cloud is a gamechanger, as it makes BI affordable and easily available as compared to traditional BI. But still BI over cloud is not as successful as it should be because of many underlying issues and causes like security, loss of control over data. Organization should be careful in selecting the apt BI after carefully analyzing what type of analysis it want to do. Plus, Organizations need to follow cloud computing best practices, insist on strong governance, and address potential issues and concerns with vendors before heading into the BI cloud. This paper touches upon the drivers and challenges related to Cloud BI, and can act as a source of information to leading Cloud BI vendors giving them idea of what users actually want from 90
  2. 2. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME cloud vendors when they go for BI usage over cloud computing . In addition to this, paper throws light on why organization prefer BI and where BI and cloud vendors are lagging so that these BI shortcomings can be addressed by BI and cloud vendors. Keywords: Business Intelligence, Business Analytics, Big Data, Cloud Computing. A INTRODUCTION Having knowledge about the business environment is a key for keeping the business r profitable and competitive in today’s business environment. By having the right insight into its environment, a company will be able to take the necessary actions to address trends in the market and allowing it to identify opportunities quickly and get the most out of it. To make sure that opportunit businesses have knowledge about its environment, it is beneficial to make use of Business Intelligence (BI) to produce and process information about the environment for strategic purposes. (Thompson and Walt, 2010) Now question comes what BI is? For business to have better insight of situation and trend they need some data analysis technique to dig deep into data to identify hidden patterns and relationships which otherwise are pattern difficult to identify. This enables business to develop right strategy to sustain in highly dynamic environment. Such data analysis tools are known as business intelligence tools which include OLAP, data mining, and analytics. Business Intelligence is basically a set of tools, methods that transform . raw data stored in a company data warehouse into meaningful and useful information for business purpose. Gone are the days when business data consisted of structured data, now BIG Data consist of both structured and unstructured data. And BI tools have capability to sift through such kind of data. a. Data Mining OLAP Raw Data Predictive Meaningful and useful Information Analysis Figure 1: BI concept BI at Four levels Business intelligence is a tool which helps user to do deep analysis of data stored in data warehouse to predict what to do in future which would otherwise cannot be sifted. Such Business w ed. Analytics undergo through four phases (Figure 3). ics 91
  3. 3. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME Figure 3: BI at four levels. Source: gartnerbi-emea-2013-part-1analytics-moves-to-the-core.html Descriptive Phase It is the first phase in business analytics which focus on what happened .It analyses historical and transactional data stored in organization data warehouse to identify reason behind company success and failure. This is most common post mortem analysis done by any organization. For e.g. if company sales is going down then, identifying why it is going down. Here BI will help in identifying hidden trends and relationship in big data of company to identify reason of decreasing sales like particular area of sales going down, problem in sales representative. Then comes Diagnostic analysis where main focus is on identifying reason behind what has happened. Predictive analysis will work with various statistical tools like correlation, regression to find out what will happen in future .For example, which customer is going to leave your business. But it will not help in identifying what action to be taken to prevent this customer churn. So comes last phase of BI Analytics: Prescriptive Analysis In addition to predicting future, this suggest not only best possible action to be taken to get benefit from prediction but also shows action’s effect on business performance. It is most beneficial kind of analysis especially when you don’t have enough time and money for experimenting and taking risk. This area of BI analytics is in use since 2003 but its power has yet not been fully utilized and very few companies are using this (Gartner,2013). Information is most important asset for any organization as application software comes and go but one thing remains with it-Information and it is this information which upon analysis provide path to a company to establish its market. This information should be used proactively. BI over cloud Though Business Intelligence tools are helpful tools for company to analyse their data but cost of implementing is high as company first need to move data to servers from traditional system and then buy BI tools and run it. This involves cost of buying hardware, software, hiring IT staff and other overheads. In addition to this even if company already has IT infrastructure, processing large amount of data require high processing and storage. IT infrastructure of a company can support increased size of data warehouse up to some limit but if this keeps on increasing then it becomes difficult for a company to manage such data effectively and efficiently and there comes the need of 92
  4. 4. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME cloud computing. In this case cloud computing can be used successfully to provide scalability for peak periods when needed and only pay for what is used.(Thompson & Walt,2007) Cloud computing allows for scaling up and down as capacity is required by the business and this is paid incrementally to the cloud computing vendor (Raths 2008). With the cloud-based solution there is a lower financial risk to the business because the business only pays for what it use and can terminate the service level agreement at any time. In addition to this it supports nomadic computing. Cloud service can be used in three ways (A Hooda, 2011): • • • SaaS-Software as a Service - With this feature consumers are not required to buy complete application software packages which are very expensive. However software can be used on the rent basis. These softwares actually run at provider server and can be accessed anytime from anywhere. IaaS-Infrastructure as Service - With this feature user need not to worry about the infrastructure to store his database and other applications as this feature of cloud computing provides all required infrastructure needed to run the consumer applications and to store his databases. PaaS-Platform as a Service-With this feature developer need not to worry about the platform support required to develop the software. The cloud computing environment enable distribution of BI tools as Software as a Service (SaaS). Different types of clouds are there to choose from viz. Public cloud: This cloud is accessible to general public over internet like Google cloud. Here service is used as pay as you go. Private cloud: Here infrastructure and data is managed internally by enterprise and it is not accessible to general public outside. Hybrid cloud: It is having features both of public cloud and private cloud. For e.g. enterprise may store archived data on public cloud and operational data on private cloud. Community cloud: It is when organizations having similar requirement share one cloud among them. Business has to choose from above types of cloud depending on his requirements. The cloud really dovetails into business intelligence in the sense that if a company doesn’t have the skill sets or the people to really see the value of business intelligence, or the processes in place, it could tap into a cloud, potentially, and get some economies of scale, leverage what’s there, and hopefully move things along a little faster.( E. Hungate, 2010) The economics of cloud-based business intelligence is indeed promising, but so is time to market. Discovering a new product opportunity based on data garnered from business intelligence and speeding that onto store shelves is the goal for any sort of analytics project. What makes software as a service so appealing is the dollars you save and then how quickly you can generate value (Baez,2010). Fig 3 shows the factors which promote BI usage over cloud and limitation of BI over cloud which are taken into study: 93
  5. 5. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME Reduced Maintance overhead Loss of control over data Reduction in total cost of ownership Business Intelligence over Cloud (Benefits) Flexible Infrastructure Lack of management buy in Limited In house capability Security/Trust issue Business Intelligence over Cloud (Limitation) Lack of budget Lack of integrated business solution Focus on Core Business Fig 3 : Benefits and limitation of BI over cloud METHODOLOGY Due to the relatively new nature of the subject Cloud computing in the domain of Business Intelligence, there is not enough secondary data available therefore; research has attempted to collect primary data to study following objectives: •Do strategies based on BI really works? Do Objective 1 •Should BI be moved to cloud computing infrastructure? Should Objective 2 •Identification of factors to be taken into consideration while selecting BI cloud vendor. Identification Objective 3 Structured questionnaire was administered to respondents containing 10 questions each of questions, which has been analyzed in data analysis and interpretation section to study above mentioned zed objectives. In addition to this, research has been supported by secondary data collect from work collected already done in the area of “Business Intelligence over Cloud”. Source of secondary data is Business I mentioned in Reference section and in text as well where is it quoted. tex 94
  6. 6. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME Primary Data information Source of primary data: Structured questionnaire Sample size: 67 Sampling Method: Random Sampling Sample Type: Working professionals in Companies / Students with work experience. Tools used for data analysis: Cross Tabulation and Chi square method in SPSS. Hypothesis testing is done using Chi-Square( ) method. Data Analysis and Interpretation 1: Awareness of BI tool among users 1.Have you ever heard the term BI? No 24.14% Yes 75.86% Chart 1 Based on conferred questionnaire; out of sixty seven, 76% users are aware about BI (Chart 1). From the study it is found out that close to 24% IT users are still not aware of BI and those who are aware still not using BI (results shown in below table: Table 1) though they heard about it and its benefit, reflecting that lot of opportunities exist for BI vendors in India to tap on. Cross table 1 1.Have you ever heard the term BI? 2.How did you learn about BI? articles in magazine/n ewspaper/w used at ord of work mouth other Total 34.7% 100% Yes 56.52% Table 1 95 8.78%
  7. 7. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME 2. 100% users who are aware of BI said that BI makes their work effective and simple (Table 2). Based on data analysis, it is found out that strategies based on BI works in organization whereas none of the user said that it does not work which means that it is a tool which can be leveraged to gain deep analytics of Big Data organization is having. 1. Have you ever heard the term BI? Yes 4. Do strategies based on BI tools really work? Yes sometime 3. Do you think BI usage makes work effective and simple? Total Yes 52.17% 47.83% 100% Table 2 3. Based on analysis of above positive results, it is found out that BI helps in formulating strategy by providing help in exploring of big data followed by ability to identify hidden pattern and relationship and lastly in doing effective market research (Table 3). Factors contributing to BI Do Yes strategies sometime based on BI tools really works? Quick exploring of big data Rank Rank Rank 1 2 3 2 2 8 Ability to identify hidden pattern and relationship Rank Rank Rank 1 2 3 3 3 5 Effective market research Rank Rank Rank 1 2 3 5 1 5 6 5 6 4 1 4 3 4 14 7 2 11 Table 3 7 4 9 8 5 4. When it comes to move BI over cloud, author first attempted to know how many BI aware users know about cloud and it was found out that 100% responders knew about cloud computing(Table 4). 1.Have you ever heard the term BI? 7. If yes, would you buy the idea of using BI over the cloud? No Yes 6. Have you ever heard the term cloud computing? Yes % of Total Table 4 96 8.7% Yes 39.1% Not sure 52.2% Total 100.0%
  8. 8. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME 5. Based on analysis (Table 4), 39% respondents would buy the idea of BI over cloud and 52% are not sure about moving to a cloud. 7. If yes, would you buy the idea of using BI over the cloud? Yes Factors contributing to BI over cloud Strongly Disagree Disagree Not sure Reductio n in total cost of ownershi p (TCO) Flexible Infrastruct ure Limited in house technical resources 11.1% 22.2% 11.1% 11.1% Reduced maintenan ce headache Focus on core business Reduction in total cost of ownership (TCO) Flexible Infrastruct ure Limited in house technical resources Reduced maintena nce headache Focus on core business 11.1% 11.1% 11.1% 22.2% 11.1% Neutral 11.1% 22.2% 22.2% 11.1% 11.1% 33.3% 22.2% 33.3% 55.6% 11.1% 33.3% Agree 55.6% 44.4% 44.4% 55.6% 22.2% 44.4% 66.7% 55.6% 44.4% 33.3% Strongly Agree 11.1% 11.1% 11.1% 22.2% 33.3% 11.1% 11.1% 11.1% Agree+Strongl y Agree % 66.7% 55.5% 55.5% 77.8% 55.5% 55.5% 77.8% 66.7% 22.2% 44.4% 55.5% Table 5 Based on data present in Table 5, users who gave positive response for moving BI over cloud said that reduction in maintenance overhead is the driving factor for moving BI over cloud followed by reduction in total cost of ownership. In addition to these factors, other mentioned factors in table also received positive response with more than 50% users agreeing to them. However, in case of users who are not sure of moving BI over cloud considered flexible infrastructure as the positive point for moving BI over cloud followed by limited in house technical resources. Note: Analysis is done based on the Agree and Strongly Agree response of respondents for each of the factor mentioned in Table 5. 6. 8.9% users who said no for moving BI over cloud, quoted loss of control over data and security being the primary concern(Table 6). Point to be noted here is that 100% of users saying no have major concern of loss of control over data and security issue. Users who are hesitant to move BI over cloud again have security/lack of trust issue as major concern in their mind. 7. If yes, would you buy the idea of using BI over the cloud? No What hinders you from going for BI over cloud? Disagree Loss of control over data Security issue/ lack of trust Lack of manage ment buy in Lack of integrated cloud solution 50.0% Neutral Agree Lack of budget Not sure 50.0% 100.0% 50.0% 50.0% Lack of budget Security issue/ lack of trust Lack of managem ent buy in Lack of integrated cloud solution 50.0% Strngly Agree 8.3% 16.7% 8.3% 16.7% 50.0% 50.0% 100.0% Loss of control over data 66.7% 16.7% 50.0% 50.0% 33.3% 16.7% 50.0% 41.7% 25.0% 8.3% Table 6 97 33.3% 8.3%
  9. 9. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME 7. When respondents were asked about what factors they consider important in c s cloud vendor selection, points shown in below radar diagram (Chart 2) were quoted: Most important being performance and security followed by SLA and back up service. Vendor Selection Criteria Performance 7 Vendor Accessibility Security 1 SLA 1 6 Market reputation 2 4 3 5 Rank Hardware/Software Support After sales service Highest Rank - 1 Lowest Rank -7 Back up Service Chart 2 Hypothesis Testing To verify BI should be moved to cloud or not based on respondent response. H0: BI should move to cloud Test Statistics (using SPSS) Chi-Square(a,b) Square(a,b) df Asymp. Sig. Yes_cloud 1.200 1 .273 Table 7 Factor_BI_cloud _Yes 12.533 10 .251 Interpretation of Table 7 Tabular value of at degree of freedom 1 and 5% significance level is 18.3 which is bigger than calculated value for Yes_cloud, 1.2. Therefore, based on chi-square test BI should move to cloud. square B This is again confirmed by applying chi square test to factors supporting BI over cloud in y second column of table where tabular value of at degree of freedom 10 and significance level 5% is 18.3 which is more than the calculated value 12.533. Thus based on chi square test null test, hypothesis(H0) is accepted. Therefore, BI should be moved to cloud. 98
  10. 10. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME CONCLUSION & FUTURE WORK Based on research it is found out that business intelligence is very lucrative tool for companies to dig deep into data stored in their server to explore big data efficiently and develop strategies based on predictive and prescriptive analysis. By studying data stored in data warehouse, the business will have an enhanced understanding of the current situation (based on Descriptive and Diagnostic Analysis) and it will be better equipped to predict or influence change(based on Predictive and Prescriptive Analysis. This will allow the business to adopt the right strategy to achieve business objective like targeting right customer with right product at right time and thus to have increased sales. But road does not end here. Since companies do not have expertise and budget to use and manage business analytics tool, air of cloud computing is spreading faster. BI SaaS solution provides a more flexible model that aligns better with business objectives where if something new comes up in technology then organization need not to worry about moving to new technology. This allows the business to evolve individual IT models, based strictly on business needs rather than on technology constraints. However, the business must find the right balance between the risks and rewards of computing in the cloud. Making use of BI over cloud is beneficial to the business but moving large data sets to the cloud could get costly in terms of loss of control over data and security is also a concern when making use of cloud solutions. Though cloud is very useful computing architecture and has lot many advantages but before going for BI over cloud, client should carefully study the cloud services offered and develop trust on it and enforce security mechanism at his individual end as well in addition to cloud server security systems and firewalls. Business must work with legality, security, to ensure that the appropriate levels of security are achieved. Organizations potentially can gain a competitive edge through selective adoption of cloud computing service, but not without first taking a comprehensive look at the associated risks thus, ensuring that they are not hurting business goals. LIMITATION & FUTURE WORK 1. Less focus on analytics maturity model and application of BI at various stages of analytics which will be covered in authors next research work. 2. Challenges faced by business when making use of BI are not focused which will again be focused in future research work. 3. Conclusion drawn based on less number of questionnaires. REFERENCES 1. 2. 3. 4. Elliott, T. (2013). Gartner BI: Analytics Moves To The Core. Retrieved from Ereth, Dahl (2013). Business Intelligence in the Cloud: Fundamentals for a Service-based Evaluation Concept. Retrieved from Workshop Business Intelligence WSBI 13. Harvey, B. (2010). Computing Forecast: Into the Clouds. Retrieved from Hooda, A. (2011). Cloud computing- Blessing or Curse. International Research Journal of Science and IT management I(I). 99
  11. 11. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 2, February (2014), pp. 90-100 © IAEME 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. Hungate E. & Baez (2010). Soul mates: Business Intelligence in the cloud. Retrieved from sion=1. IBM White Paper. IBM. (2013). Cloud BI and analytics is throwing up equal opportunities for small and large enterprise to harness the power of cloud. Retrieved from Kart, L. (2012). Advancing Analytics. Retrievd from Exec%20Forum_April%202013_final.pdf. Menon, Rehani (2012). Business Intelligence on the Cloud: Overview and UseCases. Retrieved. From ness_Intelligence_Cloud_0412-1.pdf .TCS white paper. Nedunchezhian, Moorty, Thirunavukkarasu (2012).A Survey on Integrating Business Intelligence with Cloud Computing. International Journal of Applied Information Systems 3(2). Ouf, Nasr (2011). The Cloud Computing: The Future of BI in the Cloud. International Journal of Computer Theory and Engineering 3(6). Raths, D.(2008). Cloud Computing: Public-Sector Opportunities Emerge. Retrieved from Rose (2013). Descriptive Diagnostic Predictive Prescriptive Analytics. Retrieved from Thompson, W.J.J. & Van der Walt, J.S.(2010). Business intelligence in the cloud. SA Journal of Information Management 12(1) Art. #445. Underwood, J (2013). Prescriptive analytics takes analytics maturity model to a new level. Retrieved from Hemantkumar Wani and Dr. N. Mahesh, “Security Issues in Cloud Computing for MSMES”, International Journal of Advanced Research in Management (IJARM), Volume 3, Issue 2, 2012, pp. 21 - 28”. ISSN Print: 0976 – 6324, ISSN Online: 0976 – 6332. Smita N. Gambhire, Asha P. Gavhane and Mahadev Patil, “Power of Cloud Computing in Customer Relationship Management”, International Journal of Management (IJM), Volume 3, Issue 1, 2012, pp. 225 - 230, ISSN Print: 0976-6502, ISSN Online: 0976-6510. 100