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K L Mukesh
   Young company of 5 years;
   Started with basic performance reports like sales &
    installations
   Few months down the line started dealer performance
    reporting
   Started measuring Call center performance
   Started with ROI reporting
   Supposed to generate 25-30 reports; Number of reports
    and dashboard growing by the day

   ETL challenges – technically feasible, cost and time an issue
Reports   Information




Data                             Knowledge




       Actions     Insights
Selected one of the vendors for BI, considerations
were

• Speed to Market – Few weeks, rather than many
months
• Lack of internal expertise in BI
• Drastic fluctuations in end user requirements
• Disparate set of metadata within the enterprise
• Predictive Modeling
Upside:
   Data Transfer to cloud (Vendor location) streamlined
    in weeks
   Started with Customer Analytics
   Moved to Predictive modeling
   End to end integration – analytics to call center for
    actions back to analytics for performance tuning
   ARPU significantly above industry average

    Downside:
   Scaling horizontally across functions a challenge;
    Small applications with highly distributed users
    moving to the cloud – dealer services, managing
    field services
   Data mart / performance dash boards associated
    with these applications a natural fit
   BI on cloud will follow transaction based systems
    on cloud; Easy BI access to distributed users;

   Enterprise view a challenge.           Hopefully
    architecture will evolve over time
    Large data set options where time lag
    acceptable being explored.
    ◦ Customer profiling
    ◦ Churn prediction
    ◦ Campaign performance
    ◦ Processing capacity intensive – statistical modeling
   Loading data once in a fortnight acceptable.
    Significant capacity requirement variation
   Data moved using physical medium
APPLICATION SIZE


T          SMALL            LARGE
U
R   REAL
N   TIME

A             Yes           No
R
O
U
N
D
    TAKE      Yes          Yes
T   YOUR
I   TIME
M
E
Thank You

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  • 2. Young company of 5 years;  Started with basic performance reports like sales & installations  Few months down the line started dealer performance reporting  Started measuring Call center performance  Started with ROI reporting  Supposed to generate 25-30 reports; Number of reports and dashboard growing by the day  ETL challenges – technically feasible, cost and time an issue
  • 3. Reports Information Data Knowledge Actions Insights
  • 4. Selected one of the vendors for BI, considerations were • Speed to Market – Few weeks, rather than many months • Lack of internal expertise in BI • Drastic fluctuations in end user requirements • Disparate set of metadata within the enterprise • Predictive Modeling
  • 5. Upside:  Data Transfer to cloud (Vendor location) streamlined in weeks  Started with Customer Analytics  Moved to Predictive modeling  End to end integration – analytics to call center for actions back to analytics for performance tuning  ARPU significantly above industry average Downside:  Scaling horizontally across functions a challenge;
  • 6. Small applications with highly distributed users moving to the cloud – dealer services, managing field services  Data mart / performance dash boards associated with these applications a natural fit  BI on cloud will follow transaction based systems on cloud; Easy BI access to distributed users;  Enterprise view a challenge. Hopefully architecture will evolve over time
  • 7. Large data set options where time lag acceptable being explored. ◦ Customer profiling ◦ Churn prediction ◦ Campaign performance ◦ Processing capacity intensive – statistical modeling  Loading data once in a fortnight acceptable. Significant capacity requirement variation  Data moved using physical medium
  • 8. APPLICATION SIZE T SMALL LARGE U R REAL N TIME A Yes No R O U N D TAKE Yes Yes T YOUR I TIME M E
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