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Graduate from Statistics/Mathematics/Economics can excel in this lucrative career.

Business strategies and decisions are made on facts, facts are derived from information and information is gathered from day to day activities.

The early software applications were more focused on automating business processes as is by replacing manual work, gathering and validating information. These applications were developed by using different computer languages and on different systems. With enormous and continued automation information is available in electronic form and electronic information(compare to paper-information), is easier to use . Now information is available but the challenge is how to use this information to advance business by taking decisions based on information. Here comes the concept of Data-Warehousing and Data-Mining to deal with these challenges.
 
What is Data-Warehouse(DW) and Business Intelligence (BI) ?
 
As the name suggest for Data-Warehouse is the warehouse(store) of data. Besides the ideal automations, most of the automations were done in pieces and by using process specific applications. Due to this reason information is available in different databases,machines,operating systems and locations. Therefore using this information altogether from heterogeneous systems is not easier, it should be readily available on a homogeneous system(similar access method). The process to consolidate subject related information on one single platform(language/database/operating system) and its maintenance is called Data-Warehouse. There are tools(software applications) available to consolidate data from different systems/format to one platform.
 
Business Intelligence is something related to access subject related information from available data and present it in an analytical form to help in decision making. There are many tools available that are being used for business intelligence. Some of the tools widely used for reporting and few might be used for further mining of data. Data mining is researching data for fact finding which is similar to trend analysis on statistical data.
 
 Road Map to be a DW / BI professional.
 
In brief good analytical skill, learning few software tools, analyzing data and knowing about companies during course time. 
 
1. First of all come out of this myth that software and computer application only belong to computer     professionals.
2. Good operational knowledge of computer( Including Microsoft office) .
3. Database knowledge - Rather going too much in theoretical, take a crash course in any database     (Preferably     SQL/Oracle) and learn how to store and access data from database.
4. Learning a reporting tool - Again hands on required on a reporting tool. Now these tools have     become simple to     learn and two weeks course would be handy for giving an idea.
5. Understanding trend analysis - This need analytical skills and working knowledge on the data.      Knowing     concepts of statistical methods, test of significances, skewness of data etc will make a     desirable candidate.
6. Drafting resume by highlighting computer knowledge, BI/DW tools and writing few words about      statistical     analysis.

An example of DW and BI and why company may consider or prefer Statistics / Mathematics / Economics graduate.

A loan providing company collected data of 296,000 prospective customers from various data provider companies to whom loan could be sanctioned. Loan provider company selected only 125,000 customers and contacted them by making telephone calls and through email communication. Company received 12,000 valid applications and out of these valid applications 196 people were approved and finally only 10 customers took the loan. It is a real example, good to explain this topic but harsh for business. The cost is involved at each step from getting data of 296,000 customers to finally approving loan for 10 customers.
 
To understand data warehouse part in this example we may assume that data was received or extracted in different format from each vendor and compiled at one place in single format.
 
The BI/data mining is involved to analyze the reason why (296-125),000 customers were dropped down at initial stage. Who was the best and worst data provider? Has the data was dropped down due to missing,ambiguous or outdated data? How money can be saved next time by getting better prospective customers? How number of calls can be minimized and what are the different economic reasons for approving limited number of applications and sanctioning loan. More importantly what would be trend for recovering loaned money and how much profit company would make after spending so much money to select customers and provide loans.
 
This all required to analyze data at each stage to determine socio/economic trend through number games. That is why company may prefer graduates who can better understand data and fact behind the data.