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10 Principles of Business Intelligence

 10 Principles of Business Intelligence
10 Principles of Business Intelligence

Hello Everyone, I'm Vijay Thapa, Freelance Software Developer.
And from this slide, I want to talk a little about Business Intelligence and its 10 Principles according to Thomas H. Davenport (President’s Distinguished Professor in Management and Information Technology at Babson College).
Before diving directly into the 10 principles, what is this BI (Business Intelligence)?
Based on my understanding, BI is a tool to process and analyze huge amount of data and present it into simple and actionable information that can help Managers & Executives in Decision Making.
As you can see in Slide 4, first huge amount of data is collected from different sources like databases, texts, etc. and then they are stored in a single database (also called data-warehouse), then using different BI tools and algorithms, these data are processed to get valuable information in graphical form (like charts and bar graphs) which is helpful to make fruitful decisions in any organizations. Here, BI data can have both Historical Data (Data that’s been collected over a period of time) or newly generated data.
The term Business Intelligence (BI) was first used in the 1860s, but consultant Howard Dresner is credited with first proposing it in 1989 and implementing it on Decision Making Systems.
Now, let’s talk about the Principles of BI. Here, on the basis of what I’ve understood so far, I’ve grouped these 10 principles of BI into 2 categories. They are as follows
a.     BI + Human Structured Decision = Better Decision
b.    Be Specific/Selective
Before discussion about these two in detail, I’d like to talk a little about Decision Making and Some of its types.
Every day, from having a breakfast to buying anything, we’ve to make some decision and pick one from different options available.
Thus, Decision Making can be described as the mental state that leads to the selection of an action among various alternatives available and produces a final choice. And the output can be either an action or an opinion.
Some of the different types of Decisions are discussed below
a.     Loosely Coupled Decision
It is a type of decision, where the final decision is made from limited set of data or knowledge.
For example, when we visit Chinese restaurant for the very first time, we make our first order based on the image provided on the menu, ingredients used and the name of the food. Here, we do not have the sufficient data of how many people ate that food? Or How many liked/Disliked that food? etc.
Thus, the decision made to order the food was Loosely Coupled. And the decision might be very accurate in this type.
b.    Structured Human Decision
Here the decisions are still made by human professionals, but “specific efforts have been made to improve decision making processes or contexts by determining the specific information and other process resources needed to make better decisions faster”.
c.     Automated Decision
Here, the decision is made using different BI tools along with different rules and Algorithms. The main objective is to make the process fully automated and therefore very efficient. This only works with decisions that are well structured and reducible to a set of rules.
Traditional BI uses Loosely Coupled Decision, whereas Modern BI uses Automated Decision.
Now, let’s discuss about the 10 Principles of BI
A.   BI + Human Decision = Better Decision
As discussed earlier, Modern BI uses Automated Decision using different algorithms which can make much accurate decisions but if combined with Structured Human Decision, Better Decision can be made.
Thus, some of the major principles of BI that falls under this category are as follows
1.     Decisions are the unit of work to which BI initiatives should be applied.
2.     Providing access to data and tools isn’t enough if you want to ensure that decisions are actually improved.
3.     “Loosely coupled” decision and information relationships are efficient to provision with information (hence many decisions can be supported), but don’t often lead to better decisions.
4.     The most interesting relationship involves “structured human” decisions, in which human beings still make the final decision, but the specific information used to make the decision is made available to the decision-maker in some enhanced fashion.
5.     You can’t really determine the value of BI or data warehousing unless they’re linked to a particular initiative to improve decision-making. Otherwise, you’ll have no idea how the information and tools are being used.
B.    Be Specific/Selective
As we know, BI data includes huge amount of data to process and present useful information that can help in Decision Making. But this huge amount of data may not be useful for every case.
For example, To recognize the different types or stages of cancer, the data of heart patient might not be useful.
Thus, the specific data can provide better and accurate data.
Now, some of the remaining Principles of BI that falls on this category are listed below
1.     If you’re going to supply data to a decision-maker, it should be only what is needed to make the decision.
2.     The relationship between information and decisions is a choice organization can make–from “loosely coupled,” which is what happens in traditional BI, to “automated,” in which the decision is made through automation
3.     The more closely you want to link information and decisions, the more specific you have to get in focusing on a particular decision.
4.     Efforts to create “one version of the truth” are useful in creating better decisions, but you can spend a lot of time and money on that goal for uncertain return unless you are very focused on the decisions to be made as a result.
5.     Business intelligence results will increasingly be achieved by IT solutions that are specific to particular industries and decisions within them.

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