The nexus between run-time business intelligence (RTBI) and business objectives

Quinn (n.d.) accentuates that RTBI is not only the software that a firm procures and deploys within its structures hoping for better service, but combines a whole lot of organizational actors both human and non-human. The indivisible nexus between humans and non-humans is explained in details by actor-network theorists (see Johnson, 1988; Callon, 1986; Law, 1992 for more details). It is the working together of all these actors, driving towards a negotiated direction that yields the desired results within an organization. Quinn (n.d.) remits that organizations exist and existed without the hyped and modern business intelligence solutions. However, the enrollment of an RTBI-artifact solution into the already existing network of actors involved with decision making, could help a firm bridge the gap between the running of its operational activities and its strategic decision making activities (Quinn, n.d.). Business intelligence helps an enterprise to constantly align its choice selection with the desired business value as engraved in business goals. Arnot (2004) & Ghemawat and Levinthal (2008) recount that a firm either chooses to define all the choices it whishes to follow, or partially defines these choices but keeps an open eye on what additional choices should be further selected or removed from the initial set. Fixing choices carries with it the aftermath of seeing past ineffective methods of business processing carried forward, leading to more business losses, while flexible choice selection means learning and adapting from emergent situations. In agreement with the latter, choice selection has more to do with an RTBI-artifact and the people around it working together towards decision making, while adaptation is informed enhancement of business activity. All in all, RTBI is not only the analytical software that produces statistical reports and aesthetic graphs, but also the individuals that play the role to making sense of those and using them for advantageous business processing. This presentation looks at an RTBI model as presented by Quinn (n.d.) (see figure 1 below). The model depicts three mutually-inclusive levels of an RTBI system as strategic, analytic and operational. He further asserts that a robust RTBI would incorporate all these three layers because they support one another.

Figure 1. RTBI layers – adapted from Quinn (n.d.)

According to Quinn (n.d.) the strategic layer of an RTBI helps a business enterprise defines metrics to empirically measure business performance. These measures are visualized in graphical computer applications, which only serve to graphically depict for managers (both senior and line) what the metric scales are reading from databases as guided by predefined business rules. In business intelligence (BI) parlance, these applications are termed dashboards (Gravic, Inc., 2010). Azvine, Cui, Nauck and Majeed, (n.d.) & Quinn (n.d.) perceive the strategic layer as an enabler for RTBI to permeate the entire business domain. To illustrate with an example, say one of the strategic goals is to cut costs within a specified period in an organization. However, cutting costs may depend on a lot of antecedents that are residing in the various departments within a firm. It can for instance mean that redundancy in call-centers should be minimized, or print costs should be cut, or business processes should be reengineered etc. Thus indicators can be set on the dashboard to signal to managers whether the organizational set targets dependant on these antecedents are being realized or not. Over and above serving an indicative role, the strategic layer also enables a business to devolve strategic responsibilities to the various operational areas. Individual departments are able to see the role they are playing within the entire business enterprise by understanding their strategic responsibilities (Quinn, n.d.). In an endeavour to clarify the mutual inclusiveness of the three levels as given by the Quinn model, we are going to run in parallel a patient analogy. In this analogy the strategic layer could be likened to the senses that report to a human-being when something is not well in his/her body.

Like in human-beings, abnormality still has to be diagnosed by doctors, the strategic layer could be able to sound a signal to the responsible person at the right time, but this problem still has to be diagnosed for causality. That is, we know what went wrong, but what caused it, when, and how? According Quinn (n.d.), after a problem is spotted the analytical level should be able to tell more about the problem. This means looking at all the possible causes to an identified problem and coming with the precise cause of it. Back to the analogy, the person goes to hospital, and a doctor runs all tests and checks which have the potential to reveal the cause of the suffered ailment. Only tests and checks pertinent to the clarification of the cause of the problem are performed. This is also true for RTBI according Azvine (n.d.), which streamlines diagnosis to only the area embodying an anomaly.

The last layer is the operational layer, Quinn (n.d.) says at this level the problem is known and only the correct fix is pending. If it is a business process error, a method to fix it is worked out and applied at this level. In our analogy, that would be a point when a patient walks out of the doctor’s room with a prescription note on their hand. However, this does not guarantee that the indentified problem or ailment is going to be remedied as envisaged. The operational layer also means the dashboard lights or alarming signals, are put to normality. Meaning, all the business activities running are in agreement with the set rules, which are derived from the current corporate strategy. Going back to Ghemawat and Levinthal (2008)’s choices, if the defined rules are still not satisfying the envisaged goals, then it is time to re-look into the choices that were chosen. In simple terms it means change what is not working for business.

In sum, an RTBI connects to the various business enterprise units via the operational layer, but reveals through the strategic layer. Operational activities are forced to adhere to certain processing standards, which are simple guided by metrics derived from the corporate strategy. For instance, call-centre management in firms adopting RTBI would have a live measure of calls-per-hour or average-calls per call-centre-agent against set threshholds. Secondly, the analytical layer service both the operational layer and the strategic layer. In servicing the operational layer, analytical processing enables constant business process validation against defined rules. Everything performed during a business transaction is analyzed for correctness, authenticity and value-add. On the other hand, analytical processing helps a business projects into the future by supporting the strategic layer. That is, based on what is happening currently, new choices can be incorporated in the business strategy which might be of greater value in the future. Lastly, the strategic layer allows management to steer overall business processing with a information based instrument.

References:

1. Quinn, k. (n.d.). How business intelligence should work. http://www.informationbuilders.com/products/whitepapers/pdf/How_BI_Should_Work_WP.pdf (Accessed 17 June 2010).
2. Johnson, J. (1988). Mixing humans and non-humans together – The sociology of a door-closer. Social Problems 35(3):298:310
3. Callon, M. (1986). Some elements of a sociology of translation – domestication of the scallops and the fisherman of st-Brieuc Bay. Sociological review monograph 196:233
4. Law, J. (1992). Notes on the theory of the actor network – ordering strategy and heterogeneity. Systems practice 5(4):379:393
5. Arnot, D. (2004). Decision support systems evolution: framework, case study and research agenda. European journal of information systems 13:247:259
6. Ghemawat, P. and Levinthal, D. (2008). Choice interaction and business strategy. Management Science 54(9):1638:1651
7. Gravic, Inc. (2010). The evolution of real-time business intelligence. Available from: http://www.gravic.com/shadowbase/whitepapers.html (Accessed 24 May 2010)
8. 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).

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