Business Intelligence Case




29th July 2015.



It is often said that knowledge is the fundamental basis of power. Knowing how to acquire and handle knowledge plays a huge part in the manner in which it translates into an authoritative and vital piece .The corporate world knows this much better. Companies are gradually coming to light, about the deep importance of records, data and information about what impacts them in any way. The development of systems to interpret data, such as the business intelligence systems, and the employment of analytical techniques, has ensured that the decision making process of companies is heavily reliant upon the systems. However, there are a number of current business and technology conditions that complicate effective application of business analytics to business intelligence and knowledge management data. This paper is going to look into a number of these conditions and reasons for them, and also offer an insight into the prospects for improvement.

Before delving much deeper into the topic, it is important to first of all understand the basics of data and information. This will help in understanding how the cogs of data, information, business intelligence and business analytics, fit into each other to create an operational system. Now, data is the collection of raw facts about all the routine operations of the company in relation to a given phenomena of transaction. Once this is fed into a machine for processing, it translates into information. In interpreting the meaning of the information, there is the need for business intelligence, which comes in to help in the processing of the information into generation of reports. Once this is done, then comes the business analytics. The main function of business analytics, as its name suggests, is to provide an analysis of the extracted information and offer much more insight into the meaning of it. For example, it can interpret trends and therefore help in coming up with the solutions and necessary predictions after forecasting. This is where most companies start falling short, creating conditions that make it hard for effective integration of business analytics (Davenport $ Harris, 2006).

The first problematic condition is that most companies and businesses fail to implement the final stage of the information processing. It is often commonplace to find that the information has been processed until the stage of using the business intelligence systems. The three stage process of data collection and storage, also called warehousing, the interpretation, using business intelligence systems, and the deduction, using the business analytics system, is very fundamental to the success of a business (Staples, 2009).The business intelligence systems only act to deduce the relevance of the information such as the accurate results, projection and distribution. However, the business analytics stage involves far much more than this. It incorporates extraction of trends, development of new ideas in terms of the business, such as the market segments, and offers a detailed view on how to achieve future goals. Business analytics is the most vital step of the information processing, and unfortunately, is the first undoing for most companies.

The second condition involved is the challenges in handling of big data technology. Currently, companies are facing threats from all directions as far as their data security is concerned, with the dangers of data theft getting more and more real with the advancing technology. This creates a situation whereby, the implementation of a business analytics system into the business intelligence and knowledge management processes becomes a high risk factor since the data and the valuable information generated, is prime for theft. How organizations should handle this, is another factor since there is essentially no limits to how quick technology changes. Currently, there is the cloud computing, and the social media explosion. By the time one system has moved one step ahead, the other still has no upgrade, thereby creating an integration headache (Sharda, 2014).

The third condition that makes this a challenge is the lack of sufficient technological knowhow at the workplace, to deal with the tremendous requirements needed in running such systems accurately. Actually, most organizations usually stand a high risk of running into serious losses by placing their absolute trust in the original algorithms and programs set to run the system. The thing is, a small variation in the real life, actual figures, won’t be reflected or noted by the system, this can result in a serious deficit, accounting for catastrophic losses and detrimental management failure from the personnel in charge. The real underlying reason to this is that there is essentially very few, if any, qualified personnel capable of handling such systems .This creates a condition whereby there is a technological vacuum, where anyone who has got ‘an idea’ of how to run and interpret the system, is tasked with it. Actually, this is what causes the other problems (Samild, 2011).

Luckily enough, these problems have got practical and working remedies, which, if implemented well, can help in rectifying them. The most outstanding solution to all this, is through acquisition of skilled personnel to handle the system. Skilled personnel are just one of the criteria, training them on constant basis is another. This is because the present technological landscape is ever changing, and as a result, there is an urgent need to keep the personnel on toes with the changes. They can also know when to adjust the algorithms for a better and more accurate data processing(Sauter,2014).This will ensure that the common mistakes done by the company, such as stopping at step two of the information process, is eliminated, thereby putting the company at a competitive advantage. Once this is done, the company should also ensure that a constant system upgrade is put in place. Just like software, there is bound to be a newer, upgraded version of the system after some periodic interval.

In conclusion, data and information are very vital to an organization. However, what is even more important is how companies and organizations interpret business intelligence systems. They should know that data is completely different from information. The conditions highlighted as the challenges to implementation of a business analytics system to business intelligence and knowledge management have been discussed. They include lack of technical knowhow, and technological advancement. When organizations want to use their data to obtain meaningful information, it is important for them to fully adhere to the three step process of data storage or warehousing, information processing through business intelligence systems, and deduction using business analytics. Solutions have been suggested, including employment of skilled personnel, and regular system upgrade.



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Davenport, T.H., & Harris, J. G. (2006) Competing with analytics, Harvard Business Review. Retrieved December 2, 2011, from

Samild, S. (2011) Tom Davenport: Why aren’t most organizations competing on analytics? Analyst First. Retrieved December 2, 2011, from

Sauter, V. L. (2014). Decision Support Systems for Business Intelligence. New York, NY: John Wiley & Sons.

Sharda, R. (2014). Business intelligence and analytics.

Staples, S. (2009). Analytics: Unlocking value in business intelligence (BI) initiatives. CIO. Retrieved December 2, 2011, from

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