A Conceptual Framework for Delivering Cost Effective Business Intelligence Solutions as a Service G. M. Muriithi
J. E. Kotzé
Department of Information Technology Central University of Technology South Africa +27 51 5073677
Department of Computer Science and Informatics University of the Free State South Africa +27 51 4013707
[email protected]
[email protected]
ABSTRACT Smart use of business intelligence (BI) can allow organizations to leverage the huge amounts of transactional data at their disposal and turn it into a powerful decision support mechanism that gives them competitive advantage. Despite the potential benefits of an effective BI system, the adoption and use of BI systems within the enterprise remains low, especially among smaller companies with resource constraints. This can partly be explained by the pre-dominant deployment approach available today in which a firm needs to procure, install, configure and operate a BI system in-house. Barriers of high cost, complexity and lack of in-house expertise discourage many firms from adopting BI systems. This paper argues that adopting a cloud computing model, where BI is offered as a service over the Internet can lower these barriers and accelerate the pace of BI adoption. However, migrating BI systems from traditional onpremise environments to the cloud presents huge challenges. There are technical, economic, organizational and regulatory hurdles to overcome. Further, BI systems are multi-component (ETL, Data warehouse, data marts, OLAP, reporting, data mining etc.) and deciding which component(s) to move to the cloud, and which ones to leave on-premise needs careful consideration. In addition, the fact that cloud computing is still in its infancy means there is a general lack of conceptual and architectural frameworks to guide companies considering migrating enterprise systems to the cloud. This paper takes a closer look into traditional BI and proposes a conceptual framework that companies can use to chart an adoption path for cloud BI. The framework combines attributes of IT outsourcing, traditional BI, cloud computing as well as decision theory to present a consolidated view of cloud BI. The domain of South African Higher Education was chosen as the target in which the framework will be tested.
Categories and Subject Descriptors H.4.2 [Decision Support]: Data warehousing, Business Intelligence C.2.4 [Cloud Computing]
General Terms Management, Economics, Theory, Legal Aspects Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from
[email protected]. SAICSIT '13, October 07 - 09 2013, East London, South Africa Copyright 2013 ACM 978-1-4503-2112-9/13/10…$15.00. http://dx.doi.org/10.1145/2513456.2513502
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Keywords Cloud Computing, Business Intelligence, Cloud BI, Data Warehouses, SaaS, BI as a Service.
1. INTRODUCTION Effective BI systems give decision makers timely access to quality information, enabling them to accurately identify where the company has been, where it is now, and where it needs to be in future [4]. Despite the immense benefits that an effective BI system can bring to a firm, numerous studies have shown that the use and adoption of BI systems remain low, particularly among smaller institutions and companies with resource constraints [2, 4, 7, 18, 20]. A recent survey of BI adoption among South African companies conducted by ITWeb [7] revealed that out of a sample of 82 companies, more than 50% have no integrated data warehouse (a central component of a BI system). Out of those without a data warehouse, 55% have no plans to roll out any in the next 3 years. According to this study, the main barrier to BI adoption is high costs (26.58%), followed by problems with data irregularities (22.78%), and compatibility with existing systems (11.39%). In a recent study of cloud computing adoption within SA higher education [20], more than 50% of respondents stated their institutions have no functional data warehouse (DW). Among the most significant barriers to adoption of BI in SA higher education is cost and lack of in-house capacity to develop and maintain their data warehouses. To worsen the situation, state subsidies to SA universities are on a steady decline [5,6] with the proportion of state subsidy as a percentage of overall income falling from 43% in 2004 to 40% in 2007 even as the number of students seeking university admission balloons. This calls for universities to carefully monitor the utilization of a limited budget, closely track and monitor student admissions, enrolments, and students at risk of failing, completion rates and possibly employment trends for graduating students. This paper argues that adopting a cloud computing model where BI is delivered as a service over the Internet can significantly lower the barriers of adoption such as high costs and lack of inhouse expertise and help accelerate the adoption of BI systems. Though laden with potential benefits, successfully migrating BI systems from traditional on-premise environments to the cloud needs to overcome several technical, economic, organizational and regulatory hurdles. The paper is organized into three sections. Section one provides a brief overview of cloud computing and BI and reviews recent work on conceptual frameworks that attempt to combine the two. Section two proposes a framework for cloud BI that allows
decision makers to organize their thoughts around cloud BI, the key factors that need careful consideration when planning a migration and the interplay between IT outsourcing, BI and cloud computing. Section three demonstrates the applicability of the framework by discussing an illustrative scenario from South African higher education.
2. Background BI deals with integrated approaches to management support and is defined as “a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making." [13]. A comprehensive set of definitions and descriptions of what constitutes BI can be found in [21]. The central component is the data warehouse, a “subject-oriented, integrated, timevariant and non-volatile collection of data in support of management's decision making process” [22]. A data mart is a sub-set of a DW and often stores data for a specific subject or business process such as student admissions or orders [1, 22]. Data is extracted from various internal sources (e.g. ERP, CRM) and external sources using Extraction, Transformation and Load (ETL) tools. The data is then cleaned, standardized, transformed and loaded into a data warehouse (or a set of conformed data marts) from where it is made available to users and other analytical applications for query and analysis [11, 23]. Query and analysis tools can be viewed in five dimensions – reporting, monitoring, analysis, planning and advanced analytics [26]. A traditional BI system is hosted in-house, and significant human and financial resources are incurred to ensure its continued operation. Cloud computing (CC) shifts the traditional model of viewing computing as a product that is owned and operated in-house to one that perceives computing as a service that is delivered to consumers over the Internet from large data centers or “clouds” [12, 24]. Although many definitions for CC exist [3, 12, 25], the one proposed by the US National Institute of Standards and Technology (NIST) has gained widespread acceptance. NIST defines cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [3]. Users of cloud services are called tenants, which denote either a single user or an organization with thousands of users [12]. In a CC model, services can be complete applications (Software as a Service (SaaS), development tools (Platform as a Service (PaaS) or raw computing resources (Infrastructure as a Service (IaaS) [3]. Further, these services may be deployed using public, private, community or hybrid clouds [3]. To lower costs and improve efficiencies, cloud providers exploit techniques such as virtualization [10] and multi-tenancy [28] that make it possible to achieve massive economies of scale by consolidating multiple tenants into one or more data centers and allow tenants to share computing resources such as hardware, operating systems, storage, databases, development tools and applications as well as skilled support personnel that they would otherwise not be able to individually procure. Adopting CC promises many benefits [12] compared to traditional on-premise approaches, but also face many technical, legal and policy barriers [14] some of which are summarized in Table 1.
Benefits Low upfront costs Faster deployment Low operational costs Little need for in-house expertise Broad accessibility Scalability
Risks Security and Privacy Performance Issues Regulatory compliance Internet Resilience and Bandwidth Poor cloud maturity Organizational support
The next section will review recent work on conceptual frameworks for cloud BI.
2.1
Conceptual Frameworks for Cloud BI
Due to the fact that cloud computing is still in its infancy, conceptual frameworks for cloud BI are limited and have only recently started to emerge [8, 9, 15]. This paper takes particular interest in the framework proposed by Baars and Kemper [9]. They propose a cloud based BI framework (Figure 1) derived from an extensive review of three key concepts: outsourcing, business intelligence and cloud computing.
Figure 1: Cloud BI Framework [9] The cloud BI framework is organized into four blocks: provider and contract, service composition, distribution and benefits. A key element in provider and contract is the Service Level Agreement (SLA) that spells out agreed service levels for important metrics such as availability, data security, flexibility, scalability and reliability. For service composition, the framework divides basic BI services into either hardware or software tools, and views these services alongside three layers: data, logic and the access layers. Providing hardware as a service corresponds to IaaS. In this case, hardware virtualization makes it possible for medium sized enterprises to access high end hardware resources that would otherwise be too expensive for them to procure. The data layer includes the DWH, Data Marts and ETL tools and providing these as a service denotes PaaS in the cloud model. The logic layer with tools such as reporting, OLAP and data mining maps onto a SaaS model. Distribution has two sides: physical distribution and architectural distribution. Physical distribution defines three deployment models while architectural distribution splits the distribution into either a single provider or a combination of “best-of-breed” providers. Finally, the benefits are tangible and include cost savings, scalability, performance and informational benefits (for example when several members share data in a business network).
Table 1 Benefits and challenges of Cloud BI [2]
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Situational Analysis
This framework has some shortcomings and in particular is missing two key elements. First, it fails to recognize the many risks that a cloud BI may incur. We argue that a successful migration should consider not just the benefits, but should also factor in the potential risks and the technical viability of moving BI services to the cloud. Secondly, it lacks a clear model that can guide the process of migrating selected BI services to the cloud. To address these limitations, we examined a migration strategy (Figure 2) as proposed by Mircea [2]. Stage 3 and 4 requires organizations to evaluate several options subject to a range of factors that may be contrasting in nature. For example, when evaluating cloud provider selection, issues on pricing schemes, security and compliance, performance and so on will have different implications depending on the BI tool being considered. This calls for a clear decision model that can support the objective evaluation of competing choices. In this paper, we propose the use of the Analytic Hierarchy Process (AHP), a structured technique for solving complex multicriteria decision problems [16, 17]. It is an ideal solution for the problem because when evaluating alternatives, it allows managers to apply both qualitative measures borne of experience and knowledge (e.g. “provider X has better reputation than provider Y”) as well as quantitative measures (e.g. the annual license of A is five times that of B) thus giving more objective assessment results. In the next section, a consolidated cloud BI framework that combines the three ideas introduced in this section is presented. The proposed framework not only allows the positioning of traditional BI within a cloud computing context [9] but also includes a migration strategy [2] and a decision model for multi-criteria decision making [16,17].
3.1
Situational Analysis
This phase analyses the current situation within an organization and identifies potential opportunities for cloud BI. This may be triggered by many reasons. For example, the organization may have an existing on-premise BI solution whose contract is due for renewal and may want to explore alternative sourcing options in an effort to cut costs or improve services. As explained earlier in section 2.1, the potential candidates for cloud migration may be BI hardware or software tools across the 3 layer stack - data layer(ETL, DW or DM), logic layer(e.g. reporting tool, an OLAP tool or a data mining tool) and access layer (e.g. a portal or a mobile application).
3.2
Suitability Assessment
Each potential BI tool or component identified in the preceding step needs to be assessed for cloud suitability subject to a range of evaluation factors. These factors include business value, Figure 2: A cloud BI Migration Framework [2]
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Evaluation Factors
Suitability Assessment
As alluded to earlier, adopting a cloud computing model where BI is offered as a service over the Internet can lower cost barriers and accelerate the pace of BI adoption. This section proposes a conceptual framework that companies can use to chart an adoption path for cloud BI. The proposed framework is depicted in figure 3 and has three main sections namely: situational analysis, suitability assessment and implementation. Each of these sections will now be discussed.
Map BI tool to cloud model
Cost/Benefit/Risk Assessment
Identify Potential Cloud Providers
Evaluation and Selection of Cloud Provider(s)
Implementation
3. PROPOSED THEORETICAL FRAMEWORK FOR CLOUD BI
Identify potential candidate(s) for Migration
• • • •
Business Value Technical viability Risk Exposure Organizational Impact
Multi-Criteria Decision Analysis (e.g. AHP)
Implement Cloud BI solution
Monitor and Evaluate Solution
Figure 3: Proposed Theoretical Framework for Consolidated Cloud BI technical viability, risk exposure and organizational impact and are explained below: •
Business Value – what benefits will the migration effort bring to an organization? Migrating to the cloud must translate to cost savings or a much more improved service compared to on-premise alternative.
Once the BI tool is mapped to the cloud model, the tool is then taken through a cost/benefit/risk assessment, once again subject to the evaluation factors. If positive, potential cloud providers are identified and evaluated for suitability. In this stage, lessons learnt from prior outsourcing efforts such as contract negotiation and SLA issues can be valuable in selecting the cloud provider(s) that best meets the technical, operational and trust requirements. A common thread that runs through each step of this suitability assessment process is the need to select the best alternative(s) out of a range of choices against a set of criteria. This calls for a Multi-criterion Decision Analysis (MCDA) approach such as the Analytic Hierarchy Process (AHP) [16, 17].
3.3
Implementation
BI Tools that pass the suitability assessment conducted in section 3.2 are migrated to the cloud and their performance regularly evaluated so as to determine how well they meet the needs of the organization. Implementation may be carried out in phases (for example it doesn’t make sense to subscribe to a reporting tool on the cloud if data migration issues have not been sorted out).
4. HYPOTHETICAL SCENARIO This section presents a hypothetical scenario that can be used to implement and evaluate the feasibility of the cloud BI framework proposed in section 3. Although the proposed framework is applicable to a wide range of organizations in both the private sector (e.g. SMEs in retail or construction) and the public sector such as local government, it is envisaged that it will be tested within the South African higher education environment (Figure 4). The main reason for choosing higher education as a test case is because SA universities and research centers are poised to soon enjoy much improved Internet connectivity (1-10Gbps) once the South African Research Network (SANReN) [19] is rolled out, eliminating a potential bottleneck for cloud migration that we highlighted in a recent study[20]. Universities may differ on the ERP systems they use to automate major internal functions such as student
administration, learning management and general administration (HR, Payroll, Finance, etc.), but most share similar operational and strategic goals namely: attract and recruit the best students, track and analyze student enrolment, learning and completion patterns, offer curricula that is responsive to market needs and carefully monitor the utilization of a limited budget. In other words, opportunities exist for Universities to exploit a shared cloud BI platform, possibly powered by a common cloud based data warehouse (or a set of carefully selected data marts). As stated earlier in section 2, moving to the cloud comes with many benefits but also incurs risks and technical challenges (putting a limit on the nature and type of data and BI applications that can be moved to the cloud).Architecture of the Proposed Solution User1
User2
Usern
….. Extract
Source
Extract
University 1 Encrypt, Prepare for Transport
Se nd
Staging Area
University 1
. . Source
Extract
Source
Extract
Encrypt, Prepare for Transport
Load
Shared DW
Access
. . . . .
Access Tools
Source
Se nd
Technical viability – How feasible is it to move the applications to the cloud? Issues such as ability to handle the data volumes envisaged, protect data while in transit and when at rest in the cloud provider servers, achieving acceptable performance (e.g. response times), allowable latency, multi-tenant readiness etc. • Risk exposure – How much risk is involved? Issues such as vendor lock-in, vendor going out of business, compliance violations etc. need to be factored in.! • Impacts of migration on organization – how will the migration affect employees and other stake holders? Will the migration lead to job cuts? Employee retraining? Resistance from stakeholders (e.g. business partners, employees).!! To start the suitability assessment process, the potential BI tool is mapped to a suitable cloud model. Hardware provision denotes an IaaS service; a DWH or data mart may imply a PaaS service; a reporting tool maps to a SaaS service. The deployment option (public, community, private or a hybrid cloud) is particularly important because it has far reaching implications with respect to each of the assessment factors discussed above. For example, a public cloud model may provide the lowest migration costs but incur unacceptable risks; a private cloud option incurs the highest costs but has the lowest security risks. •
University n University n
Community Cloud
… User1
User2
Usern
Figure 4: A Shared Data Warehouse for a University Community Cloud In the scenario presented here, selected data is collected from several universities and loaded into a shared DWH (hosted on a community cloud). Although the DWH platform is shared, appropriate data isolation and access rules are put in place to ensure tenants only access data that belongs to them or one that satisfies a data sharing agreement set out by the consortium. A case in point is where universities agree to share data on student applications – members can track which students have made applications to which universities and for which courses. This would prevent double admissions, where a student gains admission to two universities at the same time. Scope could be expanded to include matriculation boards, so that students’ results are immediately made available to universities as soon as they are released. A shared BI tool could reside on the cloud and be used to report and analyze student data. Since the data formats will be agreed by the consortium, and the data sources will be fairly structured, a common cloud based ETL tool for data preparation can be shared by all universities in the consortium.
5. CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH Adopting cloud computing provides a realistic opportunity for organizations to improve the depressingly low rates of conventional BI adoption because it breaks down the traditional barriers of high cost, complexity and lack of expertise. However, moving BI components such as the DWH or a data mining tool to the cloud presents formidable challenges, key among them security, performance and availability concerns. These challenges place a limit on the nature and type of BI
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components that can be migrated to the cloud. This paper proposed a conceptual framework that allows managers to organize their thinking around cloud BI, and discusses an approach they can use to evaluate the suitability of migrating selected BI tools to the cloud. A scenario from SA higher education was used to illustrate the applicability of the framework. Future research will involve testing the model in a real situation using an action research approach. Lessons learnt will be used to refine, and enhance the model.
6. REFERENCES [1] Kimball R, Ross M, The Data Warehouse ToolKit: The Complete Guide to Dimensional modeling, Second Edition, Wiley, 2002 [2] Mircea, M., Ghilic-Micu, B., & Stoica, M. (2011). Combining business intelligence with cloud computing to delivery agility in actual economy. Journal of Economic Computation and Economic Cybernetics Studies, 45(1), 39-54. [3] Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (draft). NIST special publication, 800, 145. [4] Wierschem, D., McMillen, J. and McBroom, R.(2003). What Academia Can Gain from Building a Data Warehouse Educause Quarterly, Volume 1, 2003 [5] Council for Higher Education (CHE)(2009). The State of Higher Education in South Africa, Higher Education Monitor No. 8 [6] Steyn, A.G.W and De Villiers, A.P.(2005). Public Funding of Higher Education in South Africa by Means of Formulae. Council for Higher Education. [7] ITWeb (2012). Oracle BI Survey 2012. Available online: http://www.itweb.co.za/index.php?option=com_content&v iew=article&id=59643 [8] Liyang,T., Zhiwei, N., Zhangjun, W., and Li, W.(2011). A Conceptual Framework for Business Intelligence as a Service (SaaS BI). In: Proceedings of the Fourth International Conference on Intelligent Computation Technology and Automation, Pg 1025 -1028 [9] Baars, Henning and Kemper, Hans-Georg, "Business Intelligence in the Cloud?" (2010). PACIS 2010 Proceedings. Paper 145. http://aisel.aisnet.org/pacis2010/145 [10] Menascé, D. A. (2005, December). Virtualization: Concepts, applications, and performance modeling. In Int. CMG Conference (pp. 407-414). Available: http://www.cs.gmu.edu/~menasce/papers/menasce-cmg05virtualization.pdf [11] Chaudhuri S. and Dayal U.(1997) An Overview of Data Warehousing and OLAP Technology. ACM Special Interest Group on Management of Data(SIGMOD), Volume 26, Issue 1, New York, USA, March 1997. Available: http://portal.acm.org/citation.cfm?id=248616 [12] Marston, S Li, R.,Bandyopadhyay,Z., Subhajyoti, G., and Juheng, Z. (2011) Cloud Computing: The business perspective. Decision Support Systems 51 (2011), pp176–
100
[13] [14] [15]
[16]
[17]
[18] [19] [20]
[21] [22] [23] [24]
[25] [26] [27]
[28]
189 Retrieved on 24 May 2011 from World Wide Web: http://ssrn.com/abstract=1413545 Forrester Research(2010) www.forrester.com/ Cloud Security Alliance(2009) Security Guidance for Critical Areas of Focus in Cloud Computing V2.1 Retrieved on https://cloudsecurityalliance.org/csaguide.pdf Sun, X., Gao, B., Fan, L., & An, W. (2012, June). A CostEffective Approach to Delivering Analytics as a Service. In Web Services (ICWS), 2012 IEEE 19th International Conference on (pp. 512-519). IEEE. Triantaphyllou, E., & Mann, S. H. (1995). Using the analytic hierarchy process for decision making in engineering applications: some challenges. International Journal of Industrial Engineering: Applications and Practice, 2(1), 35-44. Lee, Y. C., Tang, H., & Sugumaran, V. (2013). A Deployment Model for Cloud Computing using the Analytic Hierarchy Process and BCOR Analysis. AMCIS 2012 Proceedings. Bichsel, J. (2012). Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations. EDUCAUSE Center for Applied Research. South Africa Research Network (SANReN) Implementation status (2013). http://www.sanren.ac.za/overview/ Muriithi, G.M. & Kotze, J.E. (2012). Cloud Computing in Higher Education: Implications for South African Public Universities and FET Colleges. PROCEEDINGS OF THE 14th ANNUAL CONFERENCE ON WORLD WIDE WEB APPLICATIONS, Durban, South Africa. Ponelis, S. R., & Britz, J. J. (2013). A descriptive framework of business intelligence derived from definitions by academics, practitioners and vendors. Inmon, W. H. (1996). Building the data warehouse. John Whiley & Sons: New York. Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88-98. Khajeh!Hosseini, A., Greenwood, D., Smith, J. W., & Sommerville, I. (2012). The cloud adoption toolkit: supporting cloud adoption decisions in the enterprise. Software: Practice and Experience, 42(4), 447-465. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58. Eckerson,W.(2009).Implementing BI in the Cloud. The Data Warehousing Institute (TDWI). Agrawal, D., Das, S., and Abbadi, A(2011). Big Data and Cloud Computing: Current state and Future Opportunities. In proceedings of the 14th International Conference on Extending Database Technology (EDBT), Uppsala, Sweden. Chong, F., Carraro, G., & Wolter, R. (2006). Multi-tenant data architecture. MSDN Library, Microsoft Corporation.
