From today, I will talk something about interactive analytics and a series of related tools in business intelligence industry. For those who doesn’t know much about interactive analysis and its tools, I wish my articles are helpful to you. I will explain interactive analytics from its definition and with case examples to help you understand today.
Interactive analysis is a cycle analysis procedure of assumption, validation, and adjustment to achieve the fuzzy computation goal.
The interactive analysis is the real on-line analysis to solve the complex computation problem in the real world, and it is one of the key points in the business computation.
Example of Case
Let us explain the interactive analysis with a common example in the business activities.
Step 1 Set the goal
Why the sales volume this month greatly exceeds that of the previous month?
Obviously, this is a fuzzy computation goal with several possible answers. You cannot get the result directly using any analysis mode.
Step 2 Guess the possible branch
Since there are several possibilities to give rise to the sales volume increase, the analyzer has to check every possibility, such as:
l Orders numbers increase
l Appearance of large orders
l Intensive consumption of specific customer base, for example the intensive screening the movies of children in the summer holiday
l Improvement of process
l Launching a marketing campaign
Obviously, a certain level of business knowledge is required to make these assumptions and the keen sense of smell to the circumstances inside and outside the enterprise. This is a relatively personalized effort.
Step 3 Branch validation
Based on the possibility and characteristics of data, the analyzer will choose a branch to start the analysis, such as Increase of Orders. If the number of orders does not increase through the calculating for validation, then it indicates that this assumption is not correct. You need to validate the next assumption to carry on the cyclic analysis.
For example, by going through the validation on this branch of Appearance of Large Order, the analyzer finds this is correct, and thus this branch can be justified.
Step 4 In-depth exploration and mining
These possibilities are usually the apparent cause instead of the root cause. To really settle the problem, you will have to drill down step by step to reach the core. For example, the appearance of large order may result from:
l The new salesmen is highly capable
l The new sales policy of the company boosts the large order
l Intensive procurement of clients from a certain sector
It is obvious that the process of drill-down is a cyclic procedure. The analyzer must judge on the characteristics of data at that specific point to choose the branch of the highest possibility, so as to progress level by level, until the problem is solved.
Step 5 Solve problem
The procedure of exploration and mining does not require the unlimited drilling down. The whole procedure can put an end once a clear answer enough to make a decision is found. For example, through the validation, the Centralized Procurement in a Certain Sector is determined just the root cause. Then, this is enough for analyzer to make a decision: The sales volume can keep rising by simply beefing up the sales forces and efforts in this sector since the recent sales rise is the result of centralized procurement by the clients in this sector.
Step 6 More computations
To this step, the computation goal is achieved. However, we can realize more business values through more computation on the basis of the existing results, such as:
l Find the list of customers in this sector
l Find the list of salesman which are good at this sector
l Find the reason why the client in this sector increase the procurement quantities abruptly
l Find the abnormal actions in the sector related to this sector and the downstream/upstream sector.
And this is the end of first part for Interactive Analysis and Related Tools.