Possible uses of RTBI

Ghemawat and Levinthal (2008) reveal how organizations struggle to select the most appropriate set of choices towards implementing actions. They base their reasoning on the fact that there is an infinite bundling of the set of choices available to any organization. This coupled with the changing demands of a modern knowledgeable customer (or client), impose to a business enterprise a need for agility towards decision making in order for it to remain competitive in the market place (Martin, 2009). The environmental demands for increased speed towards quality product/service delivery are not calling only for appropriate decisions to be made for better performance, but also calling for them to be made at an accelerated pace (Schonberg, Cofino, Hoch, Podlaseck and Spraragen, 2000). It is explained in the past section (Defining RTBI) what RTBI is capable of, and how better it is purported to fare in comparison to the long-existing and widely tried operational business intelligence. RTBI is hailed by Gathibandhe (2010) and Azvine, Cui, Nauck and Majeed (n.d.) as an organizational implement that encapsulates the capability of both best choice selection and agile decision making. In this section the possible uses of RTBI are visited by giving examples of implementations that were successfully done by a few institutions.

Revealed Current Applications of RTBI

Watson et al. (2006) give an account of how RTBI was successfully deployed at Continental Airlines, which resulted in the upsurge of its performance and increased client base. In this account, the RTBI was centered on the needs of the client. That is, the RTBI was designed and implemented such that Continental Airlines served customer needs as and when they arose. Most valued customers were recognized through the system during transaction processing and timely incentivised for their loyal support. Other events that could deter enrolment of new customers into the Continental Airlines family were unveiled at opportune times and mitigated with agile responses. Watson et al. (2006) asserts that profit margins grew and myriad other benefits were realized by both the clients and business.

Gravic, Inc. (2010) explains several implementations of RTBI as follows. RTBI is being used to detect fraudulent events by some of the major banks. A credit card transaction normally culminates in no more than a mere recording of an entry into a database. What Gravic, Inc. (2010) explicates is: even before an entry could be entered into a database, that very same record is validated for authenticity against well known and emerging fraudulent trends. All these happen in real time, and also enable timely actions to be taken by either supporting decisions that require human intervention, or automated rule-based processes. Another account which Gravic, Inc (2010) recognizes as of growing interest in RTBI, is stock trading. Various analyses are performed by RTBI on behalf of trading customers. Real time trading data and product recommendation reports are supplied live for informed investment decisions to be taken, thus possibly enabling risk (or loss) elimination. Finally, Gravic, Inc. (2010) recommends RTBI for inventory control and strategic marketing. In inventory control, a business is supplied with timely information to determine whether there is a need to restock certain products, given the rate at which stock is diminishing and the trends of history purchases. In this case the data kept as historic data in the data warehouse inform strategies going forward, while the inflow of real time information tactically briefs the business about anomalies and surfacing opportunities.

One last example is recounted by Schonberg et al. (2000) who say that e-commerce is another area where the use of RTBI is proving indispensable. They term such RTBI e-business intelligence. In their discussion of measuring success, these authors highlight the informative nature of click-stream data, which is data gathered during website navigation by users. In this case, RTBI can be used to establish a live communication between a business enterprise and a customer accessing its web site. That is, an enterprise can read the precise streamlined requirements of customers as they emerge through navigational choice trends, and respond to such in time. This can be achieved by analyzing both click-stream data and data extracted from internet user profiles. Customer preferences by region, age, race etc. and various other discoveries that might help a business enterprise maximize profit can be captured. Thus in this case RTBI responds to e-commerce events by enabling a business to derive useful patterns from the behaviour of the global web user. This could help a firm aligns its interests with those of users as demarcated by boundaries, race, age and so forth. Moreover, alignment happens swiftly and without any delays.

Who needs RTBI?

From the given examples it can be inferred that RTBI is a tool calibrated for businesses that experience huge inflow of data, and at the same time need the generated data in a processed form for agile decision making. These are businesses that handle volumes of transactions and deal with volumes of clients and customers across space and time. But any enterprise operating in the modern era is inundated with data of varying degrees of usefulness. These data lie within and around its domain of operation. Martin (2009) asserts that success is inherent in sifting through these data to get facts that can help substantiate decision making, and timely so. Therefore, the sub-title (or question) posed in this sub-section can be answered by a counter question: who does not need indefinite success?

References:

1. Gravic, Inc. (2010). The evolution of real-time business intelligence. Available from: http://www.gravic.com/shadowbase/whitepapers.html (Accessed 24 May 2010).
2. Watson, J.H., Wixom, B.H., Hoffer, J.A., Lehman, R.A. and Reynolds, A. (2000). Real-Time business intelligence: Best practices at Continental Airlines. Information Systems Management 23(1):7:18.
3. Schonberg, E., Cofino, T., Hoch, R., Podlaseck, M., and Spraragen, S.L. (2000). Measuring success. Communication of the ACM 43(8):53:57.
4. Martin, W. (2009). Agile corporate management. http://www.wolfgang-martin-team.net/research-notes.php (Accessed 15 June 2010).
5. Gathibandhe, H. (2010). How smart is Real-Time BI? Available from: http://www.information-management.com/infodirect/2009_152/real_time_business_intelligence-10017057-1.html?pg=1 (Accessed 14 June 2010).
6. Azvine, B., Cui, Z., Nauck, D.D. and Majeed, B. (n.d.). Real time business intelligence for the adaptive enterprise. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.101.194&rep=rep1&type=pdf (Accessed 14 June 2010).
7. Ghemawat, P. and Levinthal, D. (2008). Choice interaction and business strategy. Management Science 54(9):1638:1651