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It is a known fact that Business Intelligence (BI) initiatives require a good amount of investment and time by the business. But organizations need to drive these initiatives not with a "cost" mindset but instead with a "profit" mindset.

The money invested in BI initiatives is meant to generate actionable information for timely decision making. It pays to note here that this actionable information can generate money many times over for the business.

The success of the BI initiatives rests on whether the organization has the right focus. The focus of all BI implementations should be on generating money from the information - be it through cost savings or through revenue generation.

Just like stock market where we invest money to make more money in the long term, a long-term view of BI investments will ensure that the ROI (or if I may say Return on Business Intelligence (ROBI) ) is indeed very high.

The advantage with BI initiatives is that the organization need not invest upfront on a big-bang approach to build an enterprise-wide decision support system. They can start small by building data marts, create small wins in terms of ROI and then slowly replicate the success across the entire enterprise. Add to this the fact that hybrid cloud architectures have started coming into vogue now thereby giving the organization an opportunity to host BI systems on the cloud. Since these cloud services work on a "multi-tenant" and  "pay-as-you-go" model, the upfront investment for a BI system gets reduced substantially.

Now on the one-hand we have this cloud services option; on the other hand we also have the option of "Open Source" BI tools such as Pentaho, Jaspersoft, etc that provide substantial savings in software costs. Thus there are means available to exercise control over the expenditure incurred for building BI systems. Hence the focus of the organizations should be to harness the data available across the enterprise and feed them into the right data model in order to equip decision-makers with the right information at the right time.

Here is an example for illustration. Let us take a common problem encountered by all organizations,namely, ATTRITION. As per the information given in Deloitte report on 'Compensation Trends Survey 2014-15', the Average Voluntary Attrition rate for IT Enabled Services sector in FY 2013 was 16.4%.

Let us consider an ITES organization 'XYZ' with around 10000 employees with this average attrition rate of 16.4%. Let's say the organization builds a HR data mart to monitor the attrition trends across various dimensions such as Business Unit, vertical, Horizontal, Designation, Age, etc. Using the information generated from the BI system, let's say that the organization manages to reduce the attrition rate to 10% in 2 years time and thereafter manages to sustain the attrition rate at 10% for the next 5 years. The reduction in attrition rate works out to 6.4%.

Let us further assume that the organization XYZ adds 200 new employees every year in addition to replacing the lost employees. The math works out as follows.

Year 1: 10000 employees; Start of BI Implementation; attrition of 1640 employees; addition of 200 new employees; replenishment for 1640 employees who left

Year 2: 10200 employees; attrition at 16.4 % again i.e., 1672 employees; replenishment for 1672 employees; addition of another 200; usage of BI system in progress

Year 3: 10400 employees; attrition reduces to 10% i.e., 1040 employees; retention achieved = 666 employees; addition of another 200; replenishment now needed only for 1040 employees

Year 4: 10600 employees; attrition maintained at 10% = 1060 employees; retention achieved = 678 employees; addition of another 200; replenishment now needed only for 1060 employees

Year 5: 10800 employees; attrition maintained at 10% = 1080 employees; retention achieved = 691 employees; addition of another 200; replenishment now needed only for 1080 employees

Year 6: 11000 employees; attrition maintained at 10% = 1100 employees; retention achieved = 704 employees; addition of another 200; replenishment now needed only for 1100 employees

Year 7: 11200 employees; attrition maintained at 10% = 1120 employees; retention achieved = 717 employees; addition of another 200; replenishment now needed only for 1120 employees


The average retention rate from year 3 till 7 works out to 691 employees per year. If we now consider the savings per year arising out of savings in recruitment costs (i.e., advertisement costs, placement agencies charges, etc), training costs, visa application costs(if any) for the new recruits and so on, it would be substantially high - not to mention the savings in reputation costs arising out of project delays caused by exit of employees. Now if we extrapolate this example for an organization that has 100000 employees or even more, the cost savings would be mind-boggling.

So in essence, investing in implementation of even one data mart i.e., the HR data mart to address the critical issue of attrition can turn out to be a shot in the arm for an organization. Now if one can imagine the benefits that would arise out of building an enterprise-wide BI system with the focus on the right KPAs and KPIs, I am sure any organization would stop worrying unduly about spending money (i.e.,typically BI tool licenses cost, consultants cost and support & maintenance cost) on BI initiatives.


In a nutshell, all that the management of an organization needs to do is to turn the thought of 'getting information out of money' on its head and think about 'getting money out of information'.



Views: 1252

Tags: BI, Information, Money


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Comment by David Jurist on April 25, 2014 at 1:50pm

Yours are excellent remarks about an excellent rationale for the evolutionary value of the data arts and sciences. Your remarks both summon the harbinger of visionary planning as well as beg the question that describes the problem that visionaries must overcome in profit-seeking enterprises.

Having evangelized the extraordinary value of inductive modeling in various environments during my long business career, I have discovered unfortunately that the world is still dominated by the accountants and the lawyers, who neither appreciate the value of the data arts and sciences nor care to adapt to them. Furthermore the brutal competition onset by globalization and currency wars has blinded the accountants to anything except short-term capture of earnings growth. They understand the cost of everything, but seldom the potential value of a new idea. They remain still too preoccupied by their lifelong investment in the constraining rules and practices of their "professions," not to the revelations that inductive modeling presents about the truths of objective reality in socio-economics, or even micro-economics of the firm.

The bust of the "dot-com" bubble in the 1990's sent CEO's and financiers scurrying for cover under the known comforts of their lawyers and accountants. Stunned publically by the loss of capital value as a result of investments gone sour, CEO's subserviated their CIO's and information practitioners to the comfortable knowns of their double-entry bookeepers in order to regain favor with their boards and stockholders.

Perhaps the best we can expect now is that eventually some lawyers and accountants will reconcile themselves in collaborative ways to the bright future that the data arts and sciences offer for informed and rational decision making and planning. Nevertheless, the retirement of two generations of lawyers and accountants may need to pass before we can advance widely "getting money out of information" with wholistic enterprise adoption rather than what monied skeptics view as a curious innovation.

Comment by Jim Arnold on April 25, 2014 at 11:49am

Glad someone agrees with me.

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