Current business and technology conditions that complicate effective application of business analytics to business intelligence and knowledge management data, and prospects for improvement
Management
Introduction
Business enterprises often conduct diverse market surveys and other experiments in order to collect and analyze data and information. Business analytics maybe explained as the manner in which information is exhaustively used by means of quantitative and statistical analysis for explaining predictive frameworks which in turn drives the decisions and actions taken by the management.
Business analytic mechanisms are of extreme importance to organizations as they help in gaining a competitive edge over firms not employing business analytic tools. Moreover technological breakthrough in the fields of data storage, optimization, modelling, data capture and data standards have paved the way for large scale and complex business analytic mechanisms. Effective business analytic models help in making sound enterprise decisions which are based on current and accurate data and not on intuition (Alnoukari, 2009).
Despite the above, there may be several hindrances which may have led to failure in knowledge management and business intelligence due to complicated and ineffective applications of business analytics. Prior research has revealed that most business intelligence and knowledge management mechanisms have in fact hindered the smooth operation of analytical mechanisms (Harris, 2010). This is mainly due to the fact that most organizations have few people who are experts in handling business analytics and correctly interpret the results to take certain decisions which require an integration of knowledge management and business intelligence. Such individuals have the required academic training as well as hands on experience to deal with complicated analytical issues and finding authentic results. On the other hand, most individuals do not have the required academic qualification and training or the experience required to handle business analytics. Such individuals may experience frustration as a result of the complex analytical tools and thereby create their unprepared review which leads to diverse versions of facts. This leads to the denigration of the inclination for an organization to integrate and skilfully use the potential of its manpower as well as asset capital.
Scholars maintain that another hindrance to effective application of business analytic tools may be due to the variations in the definition of business intelligence. Organizations should first explain the business intelligence level that is required for it to thrive and prosper and accordingly develop analytical applications which will help in deriving relevant facts which may facilitate knowledge management and business intelligence (Sawyer, 2011). The issue with nomenclature has on one hand helped companies like IBM, SAP and Microsoft to prosper by selling their products. They promote the software by the name of “business intelligence” and hence organizations fail to understand that these are software and not business intelligence tools. Such mechanisms do not create data instead provide information to business enterprises. This is one primary reason as to why business analytics are not effectively employed leading to failure of business intelligence (Staples, 2009).
Another critical issue which is often found in most organizations is the lack of focus on striving to apply business analytics to make enhanced decision making to enhance business. According to Davenport (2010), the main issue with most organization is the lack of sync between knowledge management and business intelligence for effective application of business analytic tools. This is despite the fact that such organizations have trained business analysts who may aid the management in taking decisions based on data and facts.
Another issue which has been experienced by most personnel is the lack of training to critically apply business analytics to knowledge management and business intelligence. Moreover personnel may find it extremely complex to use the business intelligence mechanisms and this leads to undermining the significant function of knowledge management and business intelligence in effectively developing business analytic methods.
Another issue is that organizations have diverse teams who spend quality time as well as substantial money in developing certain technological activities like data warehousing, choosing and putting into practice the business analytic methods, assembling the communication and hardware and polishing the processes in order to derive data which may be translated into facts. However, most firms may have garnered irrelevant data or may not have stored the information collected in the past. These are critical errors and may potentially impact the effective implementation of business analytics to facilitate knowledge management and business intelligence (Davenport, 2006).
Academic scholars have spent years trying to understand the manner in which some select organizations have employed business analytics to upset existing markets and develop new markets. Accordingly scholars maintain that organizations need to modify their structure to fit in intense and complex software technology and knowledge management in order to make business analytics more effective. This does has its own pitfalls including de-motivation, resistance to change, less commitment and loyalty, ambiguities in measuring performance and the dangers of automation. Scholars agree that a wide chunk of workers although knowledgeable may lack the productivity levels to excellently perform a sophisticated and complicated analytical work (Samild, 2011).
Conclusion – prospects for improvement
Organizations like Microsoft, Oracle and IBM have soared ahead in business intelligence mainly because they have trustworthy and effective business analytic systems. This has been possible by centrally storing the data in the corporate ware house. They have clear definition and this makes information exchange successful. Moreover already defined report structures have to be followed prior to giving the decision makers the reports based on which they take ad hoc decisions. A network of experienced and knowledgeable individuals should be entrusted and they should particularly align standard guidelines and help in implementing the business intelligence project. Last, strong and clear communication channels should flow in the organization to facilitate knowledge management and business intelligence (Ko & Abdullaev, 2007).
References
Alnoukari, M. (2009). Using business intelligence solutions for achieving organization’s strategy: Arab International University case study. Internetworking Indonesia Journal, 1(2), 11 – 15.
Davenport, T.H. (2010). Business intelligence and organizational decisions. International Journal of Business Intelligence Research, 1(1), 1 – 12.
Davenport, T.H., & Harris, J. G. (2006) Competing with analytics, Harvard Business Review. Retrieved November 13 2013, fromhttp://download.microsoft.com/documents/uk/peopleready/Competing%20on%20Analytics.pdf
Ko, I.S. & Abdullaev, S.R. (2007). A study on the aspects of successful business intelligence system development. Lecture Notes in Computer Science, 4490, 729 – 732.
Samild, S. (2011). Tom Davenport: why aren’t most organizations competing on analytics? Retrieved November 13, 2013 from http://analystfirst.com/2011/09/02/1001/tom-davenport-why-aren%E2%80%99t-most-organisations-competing-on-analytics/
Sawyer, R. (2011). BI’s impact on analyses and decision making depends on the development of less complex applications. International Journal of Business Intelligence Research, 2(3), 52 – 63.
Staples, S. (2009). Analytics: unlocking value in business intelligence (BI) initiatives. Retrieved November 13, 2013 from http://www.cio.com/article/489257/Analytics_Unlocking_Value_in_Business_Intelligence_BI_Initiatives
