Cloud Services Supporting Business Intelligence - Literature review Example

? Cloud Services Supporting Business Intelligence Researchers argue that cloud computing presents future benefits for business intelligence, BI. Business intelligence on the cloud encompasses the interaction of two critical information technology trends which could be considered like a puzzle where users get in and complete some puzzles but until completion, getting the whole picture becomes difficult. The cloud computing technology presents a cost effective, efficient and quick computing platform on which BI technology rides to gain insight, reduce cost and enhance speed and quality of business decisions. These are the benefits which this paper seeks to elaborate drawing arguments from various sources. BI on cloud, being an emergent technology would have some limitations as various rapid changes are undertaken on the technology to make it meet the business intelligence needs of organizations. The limitations as presented by various scholars and researchers will be presented in this paper so as to give an overall picture of the technology. Despite these limitations, the future of cloud based BI remains viable and presents vast opportunities for effectively and efficiently making business decisions. Introduction Operating in an environment marred by economic slowdown, business organizations seek to adopt ways that would maximize on available resources. The recent technological developments introducing cloud computing has seen substantial business intelligence systems get hosted by service providers in Internet cloud computing hence providing sustainable competitive edge for business enterprises. According to Ouf and Nasr (2011), business intelligence (BI) refers to a decision making process using people, data, processes and associated tools and methodologies. It enables organizations analyze data and utilize the resultant valuable information for making business decisions (Ingthorsson 2011). Historically, BI depended on relational databases, data marts and data warehouses which aided business analysts in the organization of historical information so as to generate reports that would inform executives and managers on strategic trends and opportunities. Recent years has seen the adoption of systems that would propel business intelligence towards reliance on near real-time operational data including customer relationship management, CRM, enterprise resource planning, ERP, marketing and supply chain among other databases. Inherent to any kind of BI would be the concern for data quality, dependence and consistence and its subsequent creation and maintenance. Despite the rapid transformations in BI, aggregation of factual data from available data sources remains necessary. Cloud computing has opened up the economics of BI to provide small enterprises with the opportunities to compete using insights provided by BI. According to Ouf and Nasr (2011), the cloud provides a virtual unlimited computing power pool, memory and storage which are delivered in discrete modules. Each node would have limits on units of memory, storage space and processing power. Despite the variation in these parameters based on price point and service provider among other factors, the resources in a pool of cloud present a great grid of industry standard, interchangeable computing services. Cearley (2010) further notes that services would be delivered to external customers by a third party in public cloud computing whereas IT would provide the services to internal customers in private cloud computing. To achieve true scalability would require the database architecture to fully maximize the resources available in the pool. Indeed, organizations appreciate the capabilities that cloud computing would provide in their operations with Ingthorsson (2011) arguing that successful companies must evaluate this opportunity and seek to align themselves accordingly. Global studies by Institute for Operations Research and the Management Sciences, IORMS (2011) indicated that 60% of organizations worldwide would embrace cloud computing technology in the following five years so as to not only grow their businesses but also achieve competitive advantage. This figure doubled from 2009 IBM’s findings on the same where 83% of the business enterprises studied pointed out at analytics and business intelligence as top priorities. According to Gillam (2010), the American government has been keen on implementing cloud computing in desktop management, content and records management, business intelligence and government to contractor service delivery modes. Oracle (2010) projected a 6.9% compounding annual growth for cloud business intelligence tools from $8.1 billion in 2009 to $11.3 billion in 2014. Organizations should therefore seek to familiarize themselves with the core offerings around cloud computing which include Infrastructure as a Service, IaaS; Platform as a Service, PaaS; and Software as a service, SaaS. IaaS is made up of a pooled network, storage infrastructure and server which allow subscription or renting of storage and computing power accessed from the desktops of users via the Internet. With this, the business chooses a specified number of servers where some core specifications would be incorporated. The customer incurs the cost of installing data and applications thus incurring the costs of configuration and management. Essentially, PaaS refers to a packaged platform for application development and deployment that incorporates application tools and accompanying infrastructure. PaaS provides for the opportunity of web applications creation without incurring capital cost and the complexity that comes with management of software and infrastructure. PaaS allows for deployment with inherent security, reliability and scalability and accommodates additional storage resources and compute. Lastly, SaaS integrates both the software delivery and business models. Its functionality has been exhibited through software that undertakes standard business functions in sales, procurement and finance. The consumer would be provided with the capability of using the provider’s applications on the cloud infrastructure which could be accessed from the customers’ devices through and interface like web browser such as web based email (Oracle 2010). Upsides Cost cutting remains the concern of managers in organizations worldwide as organizations seek to accomplish more with less and propagate innovation and creativity (IORMS 2011). Cloud based BI reduces business costs as it allows for businesses to pay for usage from operational provision thus overcoming capital expenditure requirement to accomplish the initiative. Ingthorsson (2011) points out at the capability of renting software with cloud computing as opposed to buying full licenses as an opportunity for cutting costs. This approach based on subscription ensures that companies pay for their usage only. Further, operational overheads together with the associated skills would be greatly reduced as much of the overhead would be on the provider. Avoidance of capital expenditure and associated long term financial commitments increase flexibility thus funding more data mart undertakings. According to Mircea and Stoica (2011), operational overheads being in the provider’s hands provides the opportunity for business to align its growth to technological growth as the business benefits from flexibility to software usage, infrastructure and scale in line with business expansions and contractions. IaaS provides for this capability as it saves the customer the need for acquisition and maintenance of underlying infrastructure. This would be suitable for proving concept and impromptu analytic data projects as need arise. This nimbleness makes it possible for isolated business units to be more proactive to the needs of BI than their rivals hence increase the quality of the adopted strategy setting and implementation (Ouf & Nasr 2011). It also provides a low cost mechanism for pilot projects at least in as much as infrastructure, software installation and management and any associated costs are concerned. PaaS provides significant flexibility which builds on the offering of IaaS to encompass software components installation which enables an organization to implement a web-based application customized to its needs. PaaS could also incorporate additional operational management activities like fix packs, software upgrades and software patches. Customer organization could therefore focus on implementation of a solution that meets the demands of its business as opposed to the traditional overhead activities like software component upgrades. SaaS offers more comprehensive option as it gives a business oriented solution that backs up physical processes of a business that the people engage in day to day. The recent years have had attention being paid more to traditional business functions like sales, procurement and finance but the evolution of SaaS has business intelligence firmly on sight now. Dependence on IT has been a common bottleneck that prevents business users from experiencing the full potential of BI. IT intervention would always be required in BI process from data warehousing to creation and customization of reports. Business users quickly retrieve the needed data from the organization’s BI system, customize the dashboard, extend the system to other users and add reports: all these require IT intervention. Against the widespread argument that cloud computing solves the problem exhibited in conventional BI, SiSense (2011) argues that clod BI does not reduce reliance on IT staff. What actually happens is that the IT service would be outsourced from a third party who would still have to engage staff in creation and customization of data. As such, the effectiveness and efficiency of the outsourced providers who are more professional and exhibit high sense of expertise when dealing with their software systems should be the basis of the advantage argument. The cloud offers the self-service capability where evaluators would have access to new technologies without downloading free software and setting them up. A consumer would unilaterally provision the organization’s computing capabilities including network storage and server time as required automatically without any human interaction with the respective service providers. Cloud based analytics would play a significant role in BI by accelerating adoption of emergent technologies as the cloud offers an appropriate platform for evaluation of new software. Pitfalls Buyya, Broberg and Goscinski (2011) argue that with any leap, there should be the gap of risk and some challenges to be overcome. These challenges exist in the various stages involved including building, development, migration, running and consumption stages. Cloud BI too has some limitations. Research studies by IORMS (2011) indicate risk management as a critical issue in cloud computing in the finance industry where 80% of the samples businesses indicated they would focus their attention. Ingthorsson (2011) also argues that many businesses have concerns for external, network and physical security when dealing with cloud computing platforms. SiSense (2011) further observes that the increase in breaches of high profile data should be a concern for organizations on the risk of exposure to unauthorized parties, whether within or outside the organization. Other than the resultant business risks, there would also be legal risks with the exposure of certain regulated data. Even in cases where the provider is to blame, the ultimate legal responsibility would be borne by the owner of data. BI solutions would be made up of particular and customized applications of BI technology that would answer business questions and creatively analyze data. Advanced users of BI build most of their strategies in making business decisions into the dashboards, reports and queries that they utilize. Examples include the tracking of Key Performance Indicators and its secrecy for the management of the company. Making such a source of competitive advantage available to the numerous users of cloud would be a big risk as such information could get to the competitor whereas BI has been credited with giving business strategies tangible form that businesses seek to protect without any slight compromise. According to Gillam (2010), the US government has secret and sensitive data still maintained in facilities owned and operated by the government. Therefore, the argument by Furht and Escalante that “not all applications are suited to cloud computing, so it follows that not all knowledge management applications will be good candidates for migration to a cloud platform” holds (2010, p.450). In as much as researchers such as Ingthorsson (2011) argue on the less expenditure with cloud based BI, SiSense (2011) seeks to clarify that the specifications of the machine required to deliver this solution would not be cheaply acquired. Typically, BI solutions would call for high end hardware offering multiple strong CPUs with significant RAM gigabytes. Hosted BI vendors will charge for any hardware requirements and renting would even cost an organization substantially more. Nonetheless, cloud BI vendors would advise their customers to cut on the data available for queries so as to avoid this challenge. For example, reducing the number of fields to be queried or historical data rows would bring down the solution cost. But then again, compromising on the data available to users would negatively impact on the value of the available business intelligence, hence should be a measure of last resort. According to Mircea and Stoica (2011), the rapidly changing state of information and communication technology in the modern world remains a matter of concern as opposed to being a state of emergency. Being an emerging technology, companies should expect unforeseen glitches as the platform develops towards maturity. The fact that the provider would not be as close as the internal IT staff even poses more challenges (SiSense 2011). After all, successful business intelligence should be fast and flexible for business users and BI’s dependence on IT would require that the IT support be directly answerable and always close. Finally, Ingthorsson (2011) points out at data movement as another challenge as organizations’ reliance on data movement would be external. As opposed to in-house system in which access and connection time would be relatively reliable, cloud BI accessibility for their daily operations would be pegged on various systems including the ISP uptime and vendor’s servers. With ERP products’ lifetime averaged at approximately 15 years by Buyya, Broberg and Goscinski (2011), companies will sooner than later need to adopt new IT paradigm. The migration of applications is a risky venture and would not always guarantee better delivery of service. Since this migration depends on decoupling of processes, work would have to be organized through a process centered model as opposed to the standard silo approach common with IT network, server, database, storage and so on. Therefore, before employment of cloud based BI, Cearley (2010) proposes that organizations understand the definition and evolution of cloud computing. This helps organizations to understand optimum conditions that call for deployment of cloud computing. The next step would involve the evaluation of the models, technologies and architecture to adopt followed by an analysis of how the technology would affect the business strategy and IT direction of the organization. It is such considerations that would lead to successful implementation of cloud based BI. Conclusion Decision making in organizations would come after consumption of business intelligence results and analytics reporting. In the current economy, organizations seek to be intelligent and gain competitive advantage in the market through use of innovative BI solutions. Cloud BI provides a less expensive and complex approach to business intelligence as opposed to installing software on-site, the BI software would be accessible via the Internet in a SaaS model. It therefore reduces on the need for purchase of both software and hardware. Though this argument has been opposed by other scholars in information technology, it remains widely viable that the cost of business operation reduces with the adoption of cloud-based BI solutions. Therefore, cloud BI solution could be considered as a feasible solution to challenges with the economic crisis. As such, economic organization, irrespective of the size has been provided the opportunity that would otherwise not be available without the adoption of cloud BI. There could be limitations with using cloud based business intelligence but the future of BI would largely depend on cloud computing for effective business decision making processes. As the technology matures, cloud BI would be streamlined and made more consistent to ensure sustainable business decision based on cloud technology. References Buyya, R, Broberg, J & Goscinski, A (eds.) 2011, Cloud Computing: Principles and Paradigms, John Wiley & Sons, Inc., Hoboken, New Jersey. Cearley, DW 2010, Cloud Computing, Gartner Inc., Stamford, CT. Furht, B & Escalante (eds.) 2010, A Handbook of Cloud Computing, Springer, New York. Gillam, L & Antonopoulos, N (eds.) 2010, Cloud Computing: Principles, Systems and Applications, Springer, New York, NY. Ingthorsson, O 2011, ‘Business Intelligence in the Cloud’, viewed 25 April 2012, Institute for Operations Research and the Management Sciences 2011, ‘IBM study: Cloud computing, BI, analytics top CIO priorities’, Analytics, 31 May, viewed 25 April 2012 Mircea, M & Stoica, M 2011, Combining Business Intelligence with Cloud Computing to Delivery Agility in Actual Economy, viewed 25 April 2012 Oracle 2010, Cloud Ready Business Intelligence with Oracle Business Intelligence, An Oracle White Paper, Oracle Corporation, Redwood Shores, CA. Ouf, S & Nasr, M 2011, ‘The Cloud Computing: The Future of BI in the Cloud’, International Journal of Computer Theory and Engineering, vol. 3, no. 6. Sisense 2011, ‘SaaS (Cloud) BI – Questions worth Asking’, viewed 25 April 2012 Read More