Why Infosys Should Buy FICO

Now Indian Software Companies need to grow up and look behind Software Services, and fight for much more lucrative Software Product side with significant focus. Infosys and Iflex have shown they can play well by building Finnacle and Flex-Cube (both are banking ERP Products) respectively.

IFlex sold itself to Oracle while Infosys has gone from strength to strength. It is already the most valuable software company in India, and among Top 10 Software Services provider globally. Its main problem to move to the next level is the lack of experience in big ticket acquisitions. But that should not be a problem; they can hire few acquisition experts and go ahead.

I see FICO (earlier Fair Isaac) as one of the best possible target for Infosys at this point of time. There can be many reasons; here are few:

1. FICO's Low Valuation due to bad market condition

As per yesterday's market close, FICO share stood at 12.72 (Mar 20th After Hours) and Market Cap at $621 Million. A company having Revenue over $800million, this valuation is too low. Source Google Finance.

2. 55 Years of History and Strong Branding in US

FICO has been in market for last 55 years, and it is the pioneer company to establish Analytics and Decision Management as an industry. Its FICO Score and Falcon are very strong brands.

3. Strong R&D Culture

Fair Isaac has very strong R&D culture, and owns many patents. This will be a great addition for company like Infosys who lacks it.

4. Financial Industry Coverage

Infosys's major focus has been Financial Industry for long; its Banking ERP, Finnacle, has been doing great for the company. But this has been limited to South Asia. Most of Europe and America is dominated by IBM and Oracle.

Infosys needs some partner to break this barrier and establish itself as global major for Banking ERP. There is no other company that suit Infy better than Fair Isaac. Fair Isaac works with more than 80% of World's Banks. Its Fraud fighting product Falcon and Credit Score FICO are used by almost all Financial players in US, and outside.

5. Complimentary Products and Services

Infosys has ERP Product and BPO Operations expertise and infrastructure; while Fair Isaac has Business Intelligence Tools and Enterprise Decision Management Product. Infosys's strength lies in low cost operations and services, and is moving up the value chain organically. Fair Isaac works in high IQ knowledge management area and is fighting to move down the value chain. Great complimentary possibilities.

6. Infosys has enough cash for buyout

If Economic Times's report is to be believed, Infosys has cash reserve of well over 12,000 Crore INR ($2.4 Billion) currently. This keeps it in comfortable position to make this buy-out, and still be cash positive.

Not to worry about financing operations for FICO; it has good revenue of over $800 million.

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Ending Note:

I see Infosys as the best suitor to make this buyout but there are other probable bidders too. These are:

1. Wipro (strong cash position; experience in acquisition)

2. TCS (strong cash position; good acquisition experience; Tata group backing a bonus)

3. Accenture (can think of major entry in Banking thro this buy-out; cash position unknown; but have strong track record of acquisitions; good revenue making only second to IBM in BPO and Software Services)

4. IBM (can buy FI any day; too big operations and might not opt for this acquisition; already strong presence in Financial Industry)

5. Amazon (strong acquisition history; interest in data mining and analytics)

6. Google (Google has been buying companies from variuous background; one of the biggest data miners today and thus could opt for FI)

Well, there could be many who join this bandwagon. If Infy prepares itself well and gets FICO, it will create a new history for Indian Software Industry.

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Please share your view as comment here. Would love to know what you think. Cheers

More:

FICO CEO Mark Greene speaks to CNBC

Business Intelligence 2.0 : Decision Management

Introduction

Decision Management System refers to the integrated IT System which handles Data Management, Analytics and their Implementation for Business Use.
Decision Management System which are built by DM Companies in enterprise level are even referred to asEnterprise Decision Management or EDM. EDM is equivalent to an ERP System, where ERP is for operations and EDM is for Decisions.

Capabilities

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

#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.

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.

Decision Management is all about People

I have seen people comparing Enterprise Decision Management (EDM) with Enterprise Resource Planning (ERP). Some question me about how can DM system add value when some one already have ERP in place. And the answer is simple, DM adds more value when there is ERP already implemented as EDM complement ERP and not compete.

ERP by its name itself suggest the planning of existing resource. It is Resource Management Business while EDM is about using the resource smartly by taking optimal decision. So DM is a Knowledge Business.

Here I am trying to challenge his hypothesis that exist in the market.

"EDM can be implemented like ERP"

This will be a great mistake. Better Software can be chosen for ERP but for EDM to be successful that is not enough. You need better people.
Many of the companies tend to buy Enterprise Analytics Software and EDM Tool, and establish a in-house Analytics division straightaway with out experienced people. This is creating two type of problems in the industry -

1. Lack of Technical Know-how
The teams are not at all capable of Analyzing Data and fail to prove the significance of the team. A good analytics team should have people with business knowledge, statistical knowledge and the relevant analytics functional experience. If one lacks, the result is unlikely to be positive.

2. Over Expectation from Board
When people don't have enough knowledge about what can be done and what cannot be done, they tend to over expect.
Most of the time, people in the Management board decide to go with EDM system to get some advantage of the system and implement it. But they are unaware of the unavailability of the good data collection system. This creates huge tension to the Analyst, if he is not well equipped with proper knowledge of Data necessity and the Analytical functions. So he fails and the team too.

So be careful while forming a BI Team, have people first and software second. Else ready for the negative ROI from the BI Team.

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.

Behavioral Targeting -- follow up from Avinash

Avinash Kaushik has started an interesting discussion on Behavioral Targeting. Most of the time investing in Behavioral Targeting software and going forward with aggressive Behavioral Marketing needs a lot of skill set and indepth understanding of its use.

Premature use of BT Software may lead to various problems as a consequence of poor or no data, and no proper plan or skill to use the results.

The action based on the BT results can be the change in marketing source change (mode of advertising) or actualy change in website (content change, content location change etc). Each is a outcome of focussing something and discarding something. These strategic move may come by the dominance of customer segment by volume or quality.

You need to be aware for not doing one of these mistakes.

1. Loss of important but unprofitable segment: The segment that is found to be unprofitable (in direct calculation) may be profitable indirectly. They may be strong in spreading the news or good views about the company or product etc. They may even be a segment whom the other mass follow. Like for my own blog, major source of traffic is due to the inclusion of web analytics discussions while I am not a web analyst (but I learned it later from blogger friends and their books). There is huge chance that if I remove web analytics topics, I will lose much traffic. And this is not because that majority of the readers come for web analytics topics in my blog but the source from where they come are web analyst friends blogs referal links. (I could find this in the Google Analytics report.) In this scenerio, BT study may suggest something where the business benefit lies somewhere else, and the problem is not with the BT software but with the user (weaknesses listed above).

2. Over-reacting to profitable segment: This is as worse as above. If I like pink rose, it not necessarily mean I like a pink car. To find out accurately what customers want and what not, proper data is not enough but sufficient data is. This justifies Avinash's point 1.

3. Portfolio Moves ok, but how about market move: I am not sure how sophisticated are today's BT tools, but I expect them to fairly show the portfolio movement and give good segmentation picture. Testing of software and the method of using the BT software outcome can be done as Avinash has pointed out in his point 2. But the question lies, is that enough?

My take is, it is not. Both the methodology and the software need regular testing and upgrades. Market is moving faster than ever before, the technology changes, the macro-economic fundamentals change, and new products and process evolve almost everyday. This asks for us to be more prone to change than ever. We need a more advanced system in the likes of Enterprise Decision Management (the link follows James Taylor's blog where he writes on EDM), but I am not sure whether EDM is available for website management. If its not available, I am sure it will come sooner than later. In the absence of such system, we will need very skilled people and frequent testing infrastructure. Atleast once a month testing of software and the methodology is required (which I found practical and relevant while working for sub-prime loan market earlier). This is more important if the software vendor is not very proactive. I beg to differ with Avinash here.

This is all I have to say. And now its your turn to say. Please share your views in comments.