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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 rkishore9@gmail.com.

August 2010 Archives

 

Analytics is the application of mathematical modeling & optimization methods coupled with appropriate visualization, to enterprise and extra-prise information, leading to behavior change amongst business users and consequently, enhanced business outcomes for the enterprise. The early success in analytics has been seen in areas such as marketing optimization (better targeting of direct mailers or e-mails), attrition analysis in Telecom and markdown optimization in Retail.

I submit most large System Integrators (SIs) do not “get” analytics.

One definition of strategy is the positioning one creates by solving a fundamental human problem. I do not see the seminal thinking, building of foundations and the fundamental shifts being attempted by SIs to address the challenge of creating competitive advantage from information. What most SIs do instead is to model data, move data, massage data and report data.  They have built up large revenues and head-count focused on ETL, building/ maintaining datawarehouses, and building/maintaining reports. With scant regards to behavior change caused and business outcomes occasioned. SIs rarely look “inside” data.

The KPO units have done better. They look “inside” data. They house mathematical modeling skills; they engage with business stakeholders at the client; they speak in terms of business KPIs. For various reasons, they often get clubbed under BPO – even though their culture and brief is different. I submit even the KPOs have not moved the needle enough. Often they focus on scaling, automating and cost-effectively executing client-established analytical functions.

 
Why do SIs not get “analytics”?

Vision and leadership - The role models in analytics – such as Marriott, CapitalOne, JCPenney, Amazon, Netflix  and Harrah’s  – have all had visionary leaders – who saw the potential of analytics to create a competitive advantage. They clearly the set the agenda and vision for the organization. Programs were carved out, organization structures were refocused, and priorities were indicated. The discipline of collecting and housing clean data was established in a sound way.  Board level commitments were made for business results.

Why should it be any different for SIs? If you need to service an analytical enterprise, you'd better also be a believer and a champion. 


Mental model - I got to learn that, at a large SI, the proposal for acquisition of an analytics start-up was shot down because the decision makers did not see it as “IT” work. Our mental model seems to be constraining what and how we service our clients. This was tested in early 2000s when business consulting came into SIs. We suddenly saw people amongst our midst who did not necessarily have an engineering degree, did not code, did not understand web “post” and “get”. It took us several years to accept and leverage the folks with a business background. To me it looks like we are in a similar situation in analytics. We are not sure where this belongs.

Analytics start-ups that get acquired are faced with management who are unsure how to leverage them. Often they are construed as KPO and merged into the BPO business – something I have a problem with.

To compound the problem, analytics is the knotty area that requires collaboration between 4 orthogonal disciplines – business, math, visualization and IT.   If any one of the above 4 attempts analytics by themselves, the results are far short of the full potential – “using information to enhance business outcomes”.


Branding and gumption - Few SIs have the branding, gumption and the inclination to engage on business outcomes. Even during conversations around ERP or CRM implementations (and I hope one implements ERP to further business outcomes!) SIs comfortably lapse to a conversation around budgets, project schedules and project success as opposed to business measures. Analytics without a conversation on business KPIs lapses into outsourcing of data preparation, cleansing, SAS or SPSS programming and reporting. Little surprise that one of the first to set up a Social Intelligence practice is McKinsey.

Measurement and organization structure - I do not pretend to have cracked this one. But the issue here is very simple. If you measure an early stage practice or idea the same way you measure an established practice or unit (say, your BFSI unit), you have setup the early stage idea for an uphill climb, and for possible early failure. If you have an “analytics” unit (and I am not saying that it is the best way to organize analytics in an SI), the revenues from the unit are likely a small fraction of the total.  One way to solve it is to associate this with a “mass” offering, such as business intelligence and datawarehousing. Another is to conceive of other measures – such as “influence revenues” or large deals occasioned.  Quite another is change the structure to embed analytics into each industry vertical unit – a move which probably should be examined 3-4 years down the line when analytics is more mature (remember the days when “Java” or “.net” was a practice on its own?). Either way, there is no substitute to vision and sponsorship from leadership – if the chief does not believe in the analytical enterprise, you cannot go very far.

 

In summary – clearly start-ups have been successful in creating viable revenues streams around analytics. We now have got to a point where start-ups cannot flourish further without a larger eco-system. Larger organizations are trying to create analytics practices. Their success is limited because I feel they have not understood the challenge at hand. In order to be successful they need to address the issues of

  • Vision and leadership – that understands how they can service or enable an analytical enterprise
  • Mental model – that breaks away from the current business model – and that can accommodate the 4 areas needed to address analytics
  • Gumption – to engage with clients on business outcomes
  • Measurement and organization structures – to house analytics and acknowledge that it can have an impact disproportional to the revenues it brings.

Posted August 6, 2010 8:09 AM
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