Blog: Rajgopal Kishore Subscribe to this blog's RSS feed!

Rajgopal Kishore

Welcome to my blog. I wish to share best practices, insights and trends on business intelligence (BI). To me BI is about measuring your business, discovering performance levers and enhancing business performance. Effective BI is a closed-loop feedback system that learns constantly and is reoriented based on performance improvements.

Tools and technology are part of the solution but are not the solution in themselves. Too many organizations have all the right tools, technologies and technical skill sets but still fall short of effecting performance improvement.

This blog is about the problem-solving approach required to make BI impact business performance. My blogs share my personal insight gleaned by consulting with Fortune 1000 organizations and creating world-class SI practices. Some of the themes I write about include:

  • Gaps in current tools and technologies
  • Suggestions around organizational structures and skills
  • Making IT successful in BI
  • Client experiences - both good and bad

Join me in this endeavor.

About the author >

Rajgopal Kishore is an accomplished industry leader with more than 20 years of experience. He consults with Fortune 1000 clients around IT and BI strategy. He has jumpstarted and scaled IT/BI consulting practices at top-five outsourcing/system integration companies. His personal passion is to help clients realize business value from technology and outsourcing decisions. Over the last decade, Kishore has consulted on enterprise architecture, IT optimization, architecting complex transaction systems, performance assessments, IT strategy and BI strategy. While building consulting and solution delivery organizations, Kishore has relentlessly focused on listening to clients and providing solutions to real client needs as opposed to articulated requirements. In his last stint at a major IT outsourcer, Kishore felt a need to reorient team members to consultative engagements and, as a result, he created a game-based and case study-based consulting workshop. You can contact him at


The other day I had the opportunity to review a platform & service that purports to reduce the inventory of cash in the ATM network. Cash transactions and end-of-day levels were recorded and sent to an analytics engine. The information so collected is subjected to analytics algorithms and some manual analysis.

What happened when the analytics engine detected any sub-optimality? It flashed specific recommendations on the frequency of refills in each ATM, the bill denominations and amount at each refill. The analytics service is measured by the reduction in the inventory of cash in the ATM network, not by the number of reports that it generates.


Shouldn't this be the way Business Intelligence and analytics works anyway?


The tragedy of  BI / analytics today is that it is seen as technology to present information, and is often disconnected from operational systems. It rarely provides insights or recommendations for specific action.

Peculiarly, the biggest impediment to progress in not intention but organization design. I discover most services companies have flawed organizational designs - and hence progress in this area continues to be limited.

Keeping the above in mind, I have the following suggestions and ideas:

  • If you are considering BI and analytics in your business, centralize your data management and datawarehousing functions but embed analytics and BI into your lines of business function. For example, the risk guys should have their own analytics, the fraud their own, procurement their own and so on.


  • If you are an IT/Business consulting services player, differentiate the plumbing and foundations that are more technology centric from business intelligence and analytics that are closer to business. Consider creating Data Management/ Datawarehousing as a line of business focusing on foundations - data management, ETL, appliances, high performance computing, latency, physical data modeling and optimization. However, create Business intelligence and analytics as closely aligned to the industry oriented business units.

  • Consider business intelligence and analytics as an incubator for new ideas in your industry business units. After transactions systems have been perfected, the next set of performance improvements will only come from data. Remember - none of these great ideas will take off until you business units own them. So let them drive it.

  • Enhance your business consulting with the above capabilities. There is a huge capability gap in the industry. The gap is NOT on technology. The gap is around blending 4 elements to achieve enhanced business outcomes. These 4 elements are - understanding of the business, understanding mathematical modeling, visualization, and IT foundations. The best guys to "get it" are business consultants - I opine these guys are sharp and can learn what is needed to transform business as opposed to leaving it to the technology fraternity.

  • If you are a pure-play business consulting player, consider creating a business analytics offering positioned as "using information to enhance business outcomes". Side-step IT services players as providers of plumbing and foundation technology.

Posted October 5, 2010 7:11 PM
Permalink | 6 Comments |


BI and analytics are key to performance measurement and improvement and most organizations still look at it as technology initiatives rather than business solutions. It is very important that these initiatives are driven from Business with the help of IT whereas the sad part currently it is happening in reverse in most organizations.

Your suggestions and ideas are very valid in the current scenario


Referring to your first part of this blog in which you have discussed about the analytical solutions in any operational system around. I see a great deal in that as far advancement of the life style is concerned. But one fact is that, creating such advance systems in which real time analysis of the operational activities is involved, has become a genre which is followed by just a few product manufacturing players as of now. Apart from that most of the players are following the conventional approach and trying to play safe as there is huge investment and best minds to be taken on board.

I have been thinking about two major areas where analytics can dramatically change the flow of your business:

1) Saving energy:
It is going to be the biggest challenge in near future and I can see analytics playing a major role in optimizing the energy management systems to the great extent.

2) In running your business in an smarter way:
When a consumer enters into a shop or store like wall Mart, having a membership RFID enabled card provided by Mart or Bank. It gets detected at entrance by the help of RFID detectors and all his/her past transactions records held at that store get scanned. Based on those records a store owner can provide exclusive offers to the consumer and this way the consumer loyalty programs can be managed in an smarter way.

In short I can say that there is a huge opportunity to change the way things are handled in any business, only one thing is needed for this: Out of the Box Thinking :)

Varun Gaur.

I think the entire industry is yet to understand the hardware paradigm of the software industry.In BI particularly with respect to BI tools and technologies the focus is always on software and how optimal and sublime we can design it to produce better BI tools for reporting.The question is is BI really intelligent enough to take strategic decisions.Providing BI tools to any organization would be like providing books to an illiterate person with a documentation on how to read them.The question delves deep into how optimal a decision can those BI tools predict-this is where predictive analytics comes in and optimality also calls for space and time considerations.The BI vendors need to align with the chip companies(the chip industry has not undergone any path breaking innovation in the recent 6 decades ever since Moore's law came into play) to create a BI environment where BI tools are married properly to the hardware and thus produce faster and more better results.On the decision side I think statistical modelling,game theory and intuitive intelligence could be utilized to produce optimal results.

Some thoughts from my end.

The Gas-heater example was simply amazing ! To my mind, there are two unique and imperative parameters for its success, namely,

1. Placement of Sensors (generically, collection of right set of input information)

2. Communicating it through GPRS (generically, translation of information as input to analytical systems)

The analytics/ BI team then takes over and they are pretty good at their job.

To my mind, in any system, the control over Logic/Processing and Output is much more than Input from system teams' perspective. Hence increased dependence over others and vurnalability/risks surface.

Organisational Design (and the principal argument in your blog) should answer the above concern.

In the Gas example, it would have been very difficult for anyone to tell the position of the sensor but Gas installer expert.

Aligning to the LoBs / BUs, makes a lot of sense.

To build on that, increased amount of participation is required from the client.( partnership in design instead of only requirements and testing phases).

I suggest we require two set of people for any BI project

1. Domain experts with very little/workable knowledge of subject.

2. Subject people with significant domain knowledge.

- Domain being the client business

- Subject being the function like Finance, IT, Marketing.

E.g. - Profile of people in Finance division for Tech Company is, they understand a lot of Finance from an Tech. Company perspective but not necessarily understand technology.

(To clarify, try answering who has a higher probability of knowing Telecom better - Tech company telecom expert or Telecom company Business expert?)

People for category 1 can be supplied by client.



I agree with your premise that analytics teams should be measured on the quality of recommendation (actionable insights) and not quantity of information (BI reports and graphs).

I also agree with your comment that org design and resulting incentives aren't aligned to acheive the above objective.

However I would also like to elaborate on one of the 4 elements for business outcomes. - Understanding of business.

And i'm not referring to the oft repeated 'domain knowledge' of individuals. I'm referring to the overall inability of analytics teams to isolate and clearly articulate causal relationships in any business outcomes.

In your e.g., the problem for any analytics engine would be:

1) What if there is a leakage in the pipes, thereby expending more gas to heat the same room?
2) What if somebody had a temporary structure that was blocking the wind for a few days?

In the retail industry (where I work) the data isn't as clean as say telecom or banking industry. The customer isn't obliged to give us the correct age or income. And any promotion we run has a million causal factors affecting its success. For e.g., despite all analytics we run, our best performing stores are because (surprise surprise) they are cleaner than average stores. And Analytics team don't have a 'cleanliness quotient' as a field in their database.

So as an analytics manager it is far easier for me to be reviewed based on the number of reports rather than the quality of recommendations...

Hi Kishore,
I agree with you that there is huge emphatic drive for analytics today; however the major focus delves on the tools and technologies. The technical jargons associated with BI anyway would not matter unless they create a visible, quantifiable business impact, which we may be measured using performance indicators. However a paradox confronting analytics industry today is the choice of “Top down approach” rather than “Bottom up approach”. Solutions generated and recommendations provided are many a times mathematically and statistically correct but fail to drive business impact, because there is no mechanism in place to incorporate the soft component and qualitative parameters associated with business while deriving the solutions. In most of the cases analytics units consists of hard core statisticians or the software technocrats who justify their recommendations and solutions from empirical and academic perspectives but fail to justify that from a business perspective. So it is imperative that efficient business solutions be derived through appropriate technology and tool, rather than using the most popular BI tool and technique to derive a business solution which may not be optimal.

Leave a comment