The document summarizes the evolution of a young company's business intelligence and analytics capabilities over 5 years. It started with basic performance reports but gradually expanded its reporting to include dealer performance, call center performance, and ROI. Facing challenges with ETL and fluctuating requirements, the company selected a cloud-based BI vendor that could provide faster time to market. This allowed the company to start with customer analytics and predictive modeling, and integrate analytics with its call center and other functions. However, scaling across functions remained challenging.

1. K L Mukesh

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
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R REAL
N TIME
A Yes No
R
O
U
N
D
TAKE Yes Yes
T YOUR
I TIME
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E

9. Thank You