People make decisions just about everything. Among other things, we made choices where we go to uni, we decided which place to go for job, we selected everything with certain reasons, even if it's random (it's a reason after all!). Now, how does this situation occur in businesses? It's even critical. To make the business keeps growing, you certainly need to improve your business performance. To make the performance improved, you do need to undertake well-informed actions. Moreover, the actions/decisions you make need to be based on a logical, valid, and timely information. Lastly, how do you know which information is important for you to consider before making decisions? Well, some managers act intuitively, hence they may ask any kind of information ranging from who are the top 10% customers, what are the best selling product in Northern Region of the country, to what are the best material mix for my product. So, a stand-out system must be established to provide such information. And this idea is what's resembled by Business Intelligence. It manipulates business data [typically from a data warehouse] into any form that is supportive to the decision making process. It also has the aliases of EIS (Executive Information Systems) and DSS (Decision Support Systems).
Does Better Decision Support Create a Better Outcome?
This is what I had in mind, the minute Business Intelligence terms in FIT5093 thrown to my ears. And this was exactly the topic once discussed by my lecturer in FIT5097, the Business Intelligence Modelling [now I see the connection between the two units. Thanks Mary! :p]. The final statement was, it's not necessarily. Even if you have all the universe in your hands, you might get the chance where your actions do not result as you expected. Unless it's not about people and anything affected by people's behaviour, you may have the greater percentage of outcomes as intended. But when it comes to decision about customers, employees, the community, and the effects they made, there's always a room for uncertainty. And that's why you need managers instead of all-computer-made-decisions, isn't it?
This view is also supported by the concept of CRM (Customer Relationship Management) in my other unit, FIT5158, which was said that CRM and data mining is only a matter of tools. Whilst the real thing is how you react on the information you get. In other words, it's the decision that count, including how you turn your focus to be customer-centric. Of the interesting things about studying CRM and BI concurrently this semester is that you get the sense of how they convey a different taste of decision support in the real application. According to what I read in "OLAP Solutions" by Erik Thomsen, CRM is more of a knowledge discovery while BI is bringing you all the drill-down, drill-up, and drill-through of a structured data in the data warehouse. To say it another way, BI gives you the big picture of what happened [aggregation in most cases] whereas CRM attempts to figure out how those patterns could be of any meaning & useful knowledge to the business. So, it's like CRM linking them up to make what BI reveals as knowledge.
Alright, after all these things, how does this BI look like? How can it support the decision? How useful are they? These are the questions that we'll get into very soon! Keep an eye on! :)
