Business Intelligence in the Cloud - Literature review Example

Report on Business Intelligence and Tools Name: Institute: Business Intelligence in the Cloud Introduction In the past decade, the world has experienced some rapid advancement in the business landscape, and so business setting has entirely changed these days. The nature as well as the structure of the modern dynamic world has made times of uncertainty, inadequate data, and risks to become part of feature of contemporary business, and not the state of emergency. Business organizations in this changing world are progressively more acknowledging the desired for a more flexible as well as scalable information technology architecture (Foshay & Kuziemsky, 2014). The rapid tempo of contemporary’s business setting has as per Singh and Samalia (2014) made Business Intelligence (BI) systems elemental to the organisation’s success. Practically, BI systems changes the raw data of the organisation into relevant data that can help the organization recognize essential trends, analyse user activities, and make haste intelligent decisions. Some years back, Chaudhuri, Dayal, and Narasayya (2011) maintain that BI systems were utilised to recognize and deal with necessities such as output and resourcefulness in the office. At the moment, business organisations are progressively more utilising BI to analyse consumer behaviour, identify inclinations within the market, and seek fresh opportunities. In this report, we present a critical analysis with regard to business intelligence on the cloud. Business Intelligence Presently, with no a working cloud, business organisations cannot get the needed information for data analysis in good time to facilitate convenient decision-making. Chang (2014) posits that the capability to get data instantaneously has grown to be more and more essential in contemporary years owing to the considerable narrowing of management cycle times. What’s more, competitive demands compel organisations to make intelligent decisions derived from business information received as well as carrying it promptly. Chaudhuri, Dayal, and Narasayya (2011) believe that the capability to change raw information into useful data pertinently can result in hundreds of thousands even up to millions of US dollars to company’s profits. BI aids in strategic as well as functional decision making, for instance, a study conducted by Gartner ranked the strategic BI utilisation as performance management in the business, as well as consumer relations optimization. Others entailed supervision of business activities together with conventional packaged objective for decision support in particular functions or strategies as well BI reporting at management level. Basically, business intelligence (BI) has two elemental meanings that are not similar, and connected to the deployment of the slogan intelligence. Singh and Samalia (2014) maintain that the key, less frequent, is the entity intelligence capacity utilised in business processes. According to Singh and Samalia (2014), BI is a new-fangled domain for the analysis of the utilisation of human cognitive capabilities as well as virtual intelligence implements to the organization and decision back up in different business issues. The ensuing relates to the intelligence as data valued for its dominance and importance. Foshay and Kuziemsky (2014) hold the view that BI is a specialized data, knowledge and bits and pieces useful in the management of business. Therefore, from this point of view, business intelligence can be defined as a wide-ranging class of functions as well as tools for amassing, granting access to, and assessing information with a view to assist businesses make better organisational decisions. Foshay and Kuziemsky (2014) further notes that the phrase (Business Intelligence) connotes having an all-encompassing knowledge of every aspect that have an effect on the business. Besides, it is imperative for business organisations to have a wide-ranging knowledge in relation to aspects like the customers, competitors, internal undertakings, as well as fiscal setting to make resourceful and superb business decisions. According to Muntean, Cabău, and Rînciog (2014), a purposeful domain of business intelligence acknowledged as competitive intelligence gives attention solely to the external competitive setting. In this case, data is amassed with regard to competitors’ activities as well as decisions are made corresponding to this data. In modern-day businesses, growing values, automation, in addition to technologies have led to vast quantities of data to become accessible. What’s more, cloud technologies have presented room to amass this data, and also facilitated data loading, transformation, and mining. Of late, tools for business application integration have improved data gathering. Tools for OLAP reporting have facilitated more hasty production of new reports, which weigh up the data. For this reason, BI has at the moment developed to be the art of filtering through vast data quantities, mining pertinent data, as well as changing that data into information as a result of which measures can be taken. Issues in BI The essential key to conquering of BI system as per Gurjar and Rathore (2013) is the integration of information from diverse business systems into a cloud. At the moment, only a few business enterprises have a complete functional cloud computing system as a consequence of the enormous effort towards integrating the whole data in the organisation. Lasi (2013) stresses that in view of flourishing dynamic business setting, only the organisations that are largely competitive will realize continuous market achievement. In this case, business organisations will differentiate themselves by the capability to influence information with reference to their marketplace, end-users, and undertakings to make the most of on the business opportunities. Scores of surveys like those carried out by Forrester, International Data Centre, as well as Gartner have established that most of business organisations all over the globe are craving to invest in BI. It was as well established that in the face of major investments in enterprise resource planning (ERP) as well as customer relationship management (CRM) in the past decade the majority of organisations are still under pressure to realize competitive advantage. Gash, Ariyachandra, and Frolick (2011) believe that this was as a result of the information restrained by these systems and any business would concentrate for one point acknowledged as ‘accurate and haste access to information’. For that reason, businesses have to support the analysis and utilization of data in an attempt to make practical decisions. Business Intelligence on the Cloud Cloud business intelligence as per Chang (2014) is an innovative model of delivering business intelligence abilities as a service by means of cloud-based architecture that is cheap, flexible, and quick to deploy. Bearing in mind the prospects offered by Cloud Computing, vast investments in conventional business intelligence solutions are time and again not practical and unappealing, whilst trendy solutions rooted in Cloud Computing, acknowledged as BI services on demand or Cloud BI are becoming more and more popular. According to Gash, Ariyachandra, and Frolick (2011), Cloud BI solution has unique significance for businesses that need to advance agility whilst simultaneously decreasing costs of information technology and capitalizing on the Cloud Computing benefits. Bearing in mind the current trends in espousing Cloud Solutions, Gurjar and Rathore (2013) argue that data centre is by no means going to fade away sooner and cloud computing is without doubt democratizing access to information. The cloud paradigm strengths like hastened speed-to-market, scalability, reduced total cost of ownership and so forth, have compelled scores of BI vendors to bring in cloud services as an apparent and characteristic extension to the on-demand and on-premise BI applications (Gurjar & Rathore, 2013). Companies such as Google and Amazon provide limitless storage as well as processing power hence permitting any business to provide for its heightening information mass whilst controlling the IT associated costs. For instance, smaller business organisations that beforehand could not afford to put up an enormous server farms to process huge data amounts, can now rely on providers such as Google and Amazon to support in efforts for large-scale analysis. Additionally, numerous innovative Software as a service (SaaS) and analytic as well as cloud-friendly BI solutions are surfacing, which according to Gurjar and Rathore (2013) connotes that organizations can make the most out of the cloud not just for data storage, but as well for data crunching. There subsist, obviously, a number of key considerations; data privacy and security are the most important, but portability/openness, performance and uptime are as well imperative. Relying on the business's certain needs, Chang (2014) claism that there is more than one essence of cloud, this ranges from public (reasonably priced and extremely scalable), private (providing enormous control and security) and hybrid (integrating aspects of both public and private). Therefore, the finest approach will eventually rely on what is vital to the target business. In addition, when it comes to business intelligence, Gash, Ariyachandra, and Frolick (2011) argue that cloud solutions will just be the same as the performance they deliver. Cloud Computing According to Naghavi (2012), the Cloud computing setting allows the distribution of BI tools as a service, normally acknowledged as SaaS. SaaS is commonly recognized as software “on demand” and has turn out to be a trendy delivery paradigm for business applications. Such paradigms, as mentioned by Chang ( 2014) are hosted in the cloud and are also data-centric, making them easy too access through a browser. The majority of software companies at the moment have numerous SaaS offers on the cloud. Gurjar and Rathore (2013) maintains that SaaS is more inclusive and robust as compared to Platform as a Service (PaaS) or Infrastructure as a Service (IaaS). Owing to the heightening reputation of cloud computing as well as the significance of selecting the suitable BI tool, it is vital to well comprehend the cloud computing nature and analyse the pros and cons of utilising BI on the cloud. Cloud computing, on the word of de Oliveira, JulioMurlick, Pereira, and Vicentin (2013) is a practical setting which offers the users the ability to access the power of computing through which they cannot otherwise have right to use owing to organizational or financial restrictions. Cloud computing, from time to time termed as ‘a domain inside service computing’, and is defined by Gurjar and Rathore (2013) as a across-discipline that acts as a connection between the organisation and IT services. Cloud computing intends to permit computing technology as well as IT services to carry out business services more resourcefully and efficiently. Technically, cloud computing entails virtual applications of software and hardware as well as service oriented architecture (SOA), and its resources are shared amongst a cloud of service vendor, partners, and users. What’s more, cloud computing allows businesses to pool their resources to enhance processes within the business at decreased capital outlay. It as well permits businesses to outsource the IT errands, as a result enabling the organization to concentrate on its main competencies (Gurjar & Rathore, 2013). Essentially, as maintained by Naghavi (2012) cloud computing offers centres for big data cheaply owing to their know-how in provisioning as well as organizing computer resources. Organisations using cloud computing enjoy numerous benefits such as increased competitive advantage and productivity, but de Oliveira, JulioMurlick, Pereira, and Vicentin (2013) maintain that businesses considering to utilise the cloud in their setting must reflect on the possible risks also. Businesses have to work with assurance, security, as well as legal professionals to make sure that the right privacy and security levels are realised. According to Gartner, businesses potentially are able to achieve a cost or completive advantage through careful espousal of cloud computing; however, they must first examine the related risks, and making sure that they are in agreement with organisational goals, in company with the anticipations of shareholders, partners, and regulators. It is particularly difficult to comprehend the risks related with cloud computing, but data security remains to be the main threat facing cloud computing, writes de Oliveira, JulioMurlick, Pereira, and Vicentin (2013). For scores of critics, cloud computing could be risky for the reason that cloud computing shared resource environment brings about unforeseen side channels (reflexively monitoring information). Other setbacks like sharing of reputation fate, which permits cloud users to manipulate Cloud computing is seen by Naghavi (2012) as a target rich setting for those desiring to do damage. Cloud BI For numerous years, business intelligence on the cloud was above all the domain of start-up retailers providing SaaS BI tools as options to the conventional on-premises software vended by the business intelligence market leaders (Gurjar & Rathore, 2013). However, cloud BI is turning out to be more mainstream as growing numbers of business users hold close the SaaS as well as cloud computing models as an element of their BI strategies: almost 25 percent of organizations who took part in Enterprise Management Associates Inc. (consulting firm) Survey in 2011 affirmed that they were utilising BI tools in the cloud, and just 2 percent admitted they were not interested or considering to espouse cloud BI technology. Importantly, top vendors of BI tools are acting in response to the heightening interest in the midst of users by concentrating more development resources, on cloud analytics as well as SaaS BI. The potential benefits of business intelligence on the cloud are akin to those generally presented by cloud computing: reduced costs on IT management as well as data centre, more rapid times for deployment, and heightened agility. In numerous instances, however, deployments of cloud BI are still somewhat low-end naturally; for instance, users of Salesforce.com or departmental applications desiring to carry out analysis on sales information (Gash, Ariyachandra, & Frolick, 2011). Some business are utilising the cloud for projects like proof-of-concept that afterwards result in internal BI software deployments, according to Gurjar and Rathore (2013). Amongst the issues that are restraining companies from espousing or increasing cloud-based approaches to business intelligence are data security concerns concerning transmitting susceptible business information beyond the business firewall for storage purposes and complexities in data integration. Chang (2014) argues that cloud will in the future be a crucial part of BI and will provide numerous benefits based on implementation flexibility, cost benefits, accessibility as well as implementation speed. Chang (2014) further notes that it is extremely pertinent to implementations of Business Intelligence/Data Warehousing (BI/DW) as archetypal BI/DW applications need high infrastructure conditions, and manage changeable load volumes; therefore cloud is more resourceful as compared to data warehousing. Even though at first cloud-based solutions were intended for small and medium-sized businesses (SMBs) that lacked accessible IT resources or capital to use on making and handling a hardware and software and infrastructure, today, scores of large businesses are looking into the cloud as a means to attach novel business solutions hastily and increase existing capacity of the data centre. Conclusion In conclusion, it has been argued that cloud is an enormous part of future Business Intelligence and provides a number of benefits in terms of implementation speed and flexibility as well as cost benefits. As aforementioned, BI on the Cloud presents big chances of getting rid of hurdles to decision making by combining mission critical as well as high volume business processes. For that reason, a solution by Cloud BI could be a viable answer to the setbacks of the fiscal crisis, and through this solution, all organisations regardless of their size can use opportunities in the market made possible by adopting Cloud business intelligence. Regardless of the BI landscape age, it has been noted that the cloud paradigm can lead to heightened adoption of BI, better customer experience, reduced reliance on IT, and improved access to analytics. References Chang, V. (2014). The Business Intelligence as a Service in the Cloud. Future Generation Computer Systems, 37, 512-534. Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An Overview of Business Intelligence Technology. Association for Computing Machinery. Communications of the ACM, 54(8), 88-107. de Oliveira, L. R., JulioMurlick, A., Pereira, G. V., & Vicentin, R. (2013). Adoption analysis of cloud computing services. African Journal of Business Management, 7(24), 2362-2374. Foshay, N., & Kuziemsky, C. (2014). Towards an implementation framework for business intelligence in healthcare. International Journal of Information Management, 34(1), 20-27. Gash, D., Ariyachandra, T., & Frolick, M. (2011). Looking to the Clouds for Business Intelligence. Journal of Internet Commerce, 10(4), 261-283. Gurjar, Y. S., & Rathore, V. S. (2013). Cloud Business Intelligence – Is What Business Need Today. International Journal of Recent Technology and Engineering (IJRTE), 1(6), 81-86. Lasi, H. (2013). Industrial Intelligence - A Business Intelligence-based Approach to Enhance Manufacturing Engineering in Industrial Companies. Procedia CIRP, 12, 384-389. Muntean, M., Cabău, L. G., & Rînciog, V. (2014). Social Business Intelligence: A New Perspective for Decision Makers. Procedia - Social and Behavioral Sciences, 124(20), 562-567. Naghavi, M. (2012). Cloud Computing as an Innovation in GIS & SDI: Methodologies, Services, Issues and Deployment Techniques. Journal of Geographic Information System, 4(6), 597-607. Singh, H., & Samalia, H. V. (2014). A Business Intelligence Perspective for Churn Management. 51-56, 109(8), Procedia - Social and Behavioral Sciences. Read More