Metamorphosis of Decision Management Process

There are numerous players in the market providing consulting services that mainly focus on solving company’s problems and help the senior executives to take optimal decisions. These consulting services within the Decision Management space fall in one of three categories:

1. Analytics Consulting
2. Market Research Consulting
3. Management Consulting

There was a time when Consulting mean Management Consulting. Consultants were heavily paid individual from MC companies like Mckinsey and BCG. Companies and big Business groups often called them and asked to their suggestions.

But Consulting was not that alone. Fair Isaac has been providing Analytics based Consulting services for the last 50 years and they have helped companies grow in their own way. Analytics Consultant helped business managers to understand the existing operations, the pros and cons of the decisions made etc.

The third too existed, Market Research Consulting. This is somewhere between the first two. This is about knowing Market and competitors, and planning ahead to catch the opportunity that Market throws. TNS and AC Nielson are the major players in this area.

All three have existed and done well. They did not compete but worked together to help companies grow. But they never talk with each other. This has created a huge problem for the long term prospects of the company.

Every company waits for the issue to come and then call for the decision management service. There is however no sustainable solution. And each time one issue gets over, another problem is awaited.

If a sustainable solution is to be found, it needs to be started right from knowing the existing business scenario with good MIS Reporting System. Even though most of the companies have good reporting system, it lacks efficiency. It is better to automate such services so that little time and manpower resources are wasted.

Next comes knowing the operations in a better way and identifying the major decision points. This follows replicating the profitable decisions and eliminating the unprofitable ones.

Once this phase is overcome, the stage is ready to understand the market and the competitors to come up with highly optimized decisions.

At the end, when everything is streamlined and understood. The forward looking thought needs to be formed with all the necessary information ready. This is the stage for calling the Management Consulting services.

Until this process is not implemented by the companies today. Every year they will face a new problem in their business and will have to call Management Consultants to find the escape route. And the Management Consultants go away with a huge fee showing companies a quick solution, which in itself is based on lack of actual information of the existing business.

All About Decision Management System

There can be several works in a end to end decisioning system. Decision Management System has to include all those steps. I have tried to summarize them here:
1. Collect and Explore data
2. Understand and Organize Data
3. Design Data Repository and Implement it
4. Build Reporting System (MIS)
5. Analyze Data for hidden insights
6. Perform Predictive Modeling and Segmentation of Portfolio
7. Design Strategy Based on Analysis, Modeling and Segmentation
8. Build and Implement Decision Optimization System
9. Monitor the performance of DO System
10. Fine tune the DO System with feedback from Monitoring System

I am a strong believer of Good Data. Analytics all depends on data, so before performing any analysis on data it has to be ensured that data is Analysis Ready.
Most of the resource intensive work is around data handling. So from points 1 through 4 are related to it.

If some company wants to start using DM system, then its best to follow steps one by one.

#1. Collect and Explore Data: Collect all relevant data either from internal source or from external sources. External sources may be getting information from Data Bureaus or Market Research firms or from partner companies, more the merrier. Then comes exploration of these data collected and finding the necessary ones. All data cannot be analyzed due to resource crunch and it does not make any sense to analyze useless ones.


# 2. Understand and Organize Data: The data in raw form is useless. It needs to be transformed into usable form. So a thorough data study has to be done to understand the business interpretation. Then the knowledge has to be used to organize data for Analytic use. This may include creating derived variables, converting files to usable forms. The usable form may be creating csv files from SQL server in order to convert to SAS (or whichever Analytic Package is used). The Quality Assurance test will need to be transformed in each step, either it is reorganizing data or converting formats.
Also, there might be need to collect some data for future use. Identify such shortage of data.

# 3. Design Data Repository and Implement it: Every time need for Analytics arises performing data works becomes very costly and those data works are mostly common between those functions. So maintaining a good data repository system pays.
Data Repository System should be designed in such a way that all the problems in the first two steps are phased out. The learnings from data exploration and organization can be implemented into an automated system.
If some new data needs to be collected, then build a module in DDR to do that function.


# 4. Build Reporting System (MIS): Data Collection and Maintenance is almost done with the earlier three steps, and now comes reporting. Data in raw form, however good, is hardly interpretable in business. So build a system to report basic insights from data.
Banks may like to know daily cash flow, number of customers visiting daily, rate of increase in credit, number of accounts going charge-off daily etc. There may be hundreds of such metrics that can be directly found out on daily basis and its all repetitive. Such insights when handy can help make many business decisions fairly easy.


# 5. Analyze Data for hidden insights: Not all information data can say is readily available. There might be some hidden reason when some outcome change and it needs to performed on need basis. This might be done routinally in order to avoid sudden accident, or can be done after a alarm.
This might also be needed when a new portfolio program is to be started, or some redesigning is required vis-a-vis competition.


# 6. Perform Predictive Modeling and Segmentation of Portfolio: To take strategic decisions, predictions are always important. It can be best done by segmentation and predictive modeling.

# 7. Design Strategy Based on Analysis, Modeling and Segmentation: Performing the statistical functions does not directly relate to business. It has to be put into business understanding and a strategy needs to be designed. It should include not the Analysis output and Model scores but Business Rules based on those outputs. Each and every decision points need to be evaluated and decisions need to be optimized w.r.t the desired outcome(s).
The desired outcome may be income, revenue, risk, attrition, response etc. based on the business need.

# 8. Build and Implement Decision Optimization System: After the strategy designed, its the time for it to be put in action. In the competitive environment, there might be need to change the strategy quite frequently following the change in market conditions and competition. So each time a different strategy is adopted, putting a new decision system makes it almost impractical in terms of both time and resources. This calls for the need of a Decision Optimization System that allows to change decisions often.
Two of the features of good decision management system can be:
a. creating, changing and removing business rules
b. fine-tuning the operating points for predictive model scores

# 9. Monitor the performance of DO System: Continuous monitoring of DO system is as important as building it. There might be various reasons to do it. The change in market conditions is one, while few business rules and models may have error in them or there might be error in their implementation. But most commonly the need is of change of business need than anything else. There may be many operational issues (resources crunch, lack of expertise etc) while trying new strategy.


# 10. Fine tune and upgrade the DO System with feedback from Monitoring System: The information collected from monitoring system can be used to fine-tune decision points as well as to upgrade the system. No system is perfect and it is hard to find the drawbacks prior to actual implementation, so it is important to follow the path of continuous improvement.

Now, its your turn to say. I have said all I had, and I expect comments here. Please share if you find that I missed to include something or I missed the actual path.