1. Creating a Data Driven Culture
7/10/2013
Jay Grossman
jay@grossman.org
http://www.jaygrossman.com

2. What is Data Driven?
A data-driven organization:
• acquires data
• processes data
• leverages data
in a timely fashion to:
• create efficiencies
• iterate on and develop new products
• navigate the competitive landscape.

3. Where Structured Analysis Can Help
• Operationalize and extend reporting efforts
• Build models to provide teams greater insights
about customers, products, and processes
• Predict and recommend actions for
customers, employees, partners, etc.

4. How do we get there?
Focus on the following areas are needed for the
cultural shift to being data driven:
• Process
• People
• Platform
• Products

5. Process
• Every requirement/goal should have a
measurable outcome
• Build in metrics at every stage of planning and
delivery to gauge effectiveness and promote
learning
– Designate Acceptance Criteria to move forward
– Look for levers to maximize utility, not Silver Bullets
• Collect and measure everything
• Utilize an iterative, agile approach

6. People
• Data team structure options
– Center of Excellence
– Hub and Spoke (preferred)
– Decentralized
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Team members have access to necessary data
Leverage the skillsets you have internally
Train and mentor interested staff
Data related objectives part of job
performance

7. Platform
• Centralize data where possible (data
warehouses/stores)
– Scalable, secure, reliable, updated timely
– Evolving over time
– Considerations on efficient addition of data sources
• Make data available to many consumers
• Automate away any recurring inefficient/manual
process
• Pick the right tools for the users’ backgrounds and
needs. Then standardize for operational efficiencies.
– R, Python, SQL, Hadoop, Tableau, Excel…

8. Products
• Design metrics carefully
– should be unbiased, deterministic and measurable
– should reflect what makes the business tick
• Let the data speak
– Unsupervised machine learning can open new findings
that challenge your intuitions
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Dashboards and API’s
Tailor to your audience
Focus on ROI
Share the data and findings widely

9. Initial Tactics/Considerations
• Talk to people
– Their goals, pain points, data they have, and the way they like to
work
• Define key entities (customers, products, sales, employees)
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What are their properties and actions
What metrics used to gauge effectiveness
How they relate to one another
What are their lifecycles
• Look for easy wins at first – low effort, high value
• Implement centralized format for effectively sharing results
• Knowledge sharing and team workshop sessions are
effective