This document provides guidance for business intelligence (BI) and analytics teams. It suggests that teams focus on knowing the business and stakeholders, building useful products, selling the value of their process, and continuously improving. It also describes a case study where a team used clustering and visualization techniques to analyze customer data and identify meaningful groups, uncovering new insights. The team enhanced its analytical capabilities, collaborated with the business, and is looking to institutionalize the process and provide self-service analytics.

2. As a BI or analytics team
do you sometimes feel
And at other companies, you think
BI teams are
Isolated and trapped,
surrounded by difficult terrain?
Connected, engaged,
supported by good infrastructure?

4. Evaluative Questions Informative Questions Prospective Questions
Enablement Partnership
What Why Action

6. Improve the
Product
Sell the
Process
Know Your
Business
Build
Something
Awesome

7. Know Your Business
Institutional knowledge and
stakeholder relationships are
key. Align your analyses to
business questions
Build Something Awesome
The product doesn’t need to
be perfect, but it must be
thoughtful and useful
Sell the Process
Always have a roadmap and
expect the work to generate
new questions and
deliverables.
Improve the Product
A process improvement can
be worth celebrating,
especially if it creates the
opportunity to generate
insights, faster.

9. Business
Objective
Asset Growth
Account Growth
Business
Question
Can we provide managers with data
driven insights not found in traditional
reporting or survey methods?
Analysis
Question
Do households cluster in specific ways
not apparent in traditional reporting and
survey analytics?
Business Objective
Business Question
Analysis Question

10. Data Process
Findings and
Recommendations
6,000+
households
PCA Analysis,
Cluster Analysis,
Visualization
Connect the dots
in a meaningful
way so the
business can
interpret the
results
Indicators Related To
Wealth, Investment Goals, Asset
Consolidation, Financials,
Borrowing, Profitability

11. We started simple, with the
drag and drop k-means
analysis in Tableau 10, but
soon enough moved to R for
more advanced data prep and
analysis
We normalized the data and
then performed dimension
reduction (using PCA) in R,
and chose top 4 principal
components (~80% variance
in data).
Algorithmically (K-means and
Hierarchical) clustered the data
into 4 groups

12. Used Tableau to visualize
the resultant clusters, and
identified key performance
metrics, uncovering insights
for the business.
Challenged the assumptions
and made recommendations
specific to each cluster

13. Executives prioritize
objectives
Business makes a
decision on available
data
Did it work (quarterly,
aggregate data)
Executives prioritize
objectives
Business makes a
decision on available
data
What will
Work
Is it
Working

14. - Collaborated with the business to
refine the requirements and get a
better understanding of the end
state
- Enhanced Analytical Capabilities:
Leveraged statistical analysis and
modeling to answer the right
questions at the right time for the
right audience

15. POC
High Value and
High Visibility
Institutionalized
Process
Analytics as a
Service
Executive Buy In Live in Production Integration Self-Service
Looking towards the future
Build trust and
understanding with
executives and
business partners
through a POC
Align on a project that
is high value and high
visibility to deliver on
learnings from POC
Integrate new
repeatable analytics
process into existing
activities and reports
Provide a platform to
ensure that new
services are being
consumed to inform
decisions

16. Improve the
Product
Sell the
Process
Know Your
Business
Build
Something
Awesome
Work to automate your
traditional reporting, so
you have time to
deliver insight
Build actionable and
visual intelligence to
get leaders on board
with your analysis and
recommendations
Analytics projects don’t
have to be heavy lifts,
just require the right
people and passion
Be Proactive. Always
come with something
for a business team to
react to.