Big Data Toronto 2013

1. Building a Big Data Analytics
Roadmap
Greg Doufas
Director, Insight and Analytics

2. Why?
• Don’t be part of the problem
– Hype
– Big Data = Big Money
– Nebulous concepts
– Business before Data
Plan
Proportion
A Big-Data analytics strategy is essential in ensuring your approach is results
oriented, transparent and PROPORTIONATE to the challenge you actually face.
To get there, you must PLAN appropriately (and thoughtfully).
A balanced and layered roadmap is the most powerful tool you will ever build.
DO what you SAY

3. Start From the Top
1. Vision
– Who do you want to be?
– What’s the identity you are trying to create for
your organization…your team…yourself?
– Make it business oriented; find partners and allies
We will lead the online retail sector as an innovator in the
field of audience data acquisition, personalization and
monetization. Analytics and data-based services will be a
primary and driving factor in marketing, sales, product and
business operations.

4. What are you going to do?
2. Business Objectives and Intentions
– What do you intend to accomplish?
– What capabilities are you building in order to realize your vision?
– It better derive value
• Target customer or visitors based on propensity to subscribe, churn, engage..
• Recommend products and services to existing customers
• Optimize marketing channels and media spend
• Personalize and customize the user experience
• Develop performance indicators, business metrics (and leading indicators!)
• Shift to customer centric product development cycles
• Collect, harvest and manage new data sources (ie. Customer information)
• Introduce new revenue streams through the development of new data-based
products and services
• Instill culture of fact based decision making

5. How are you going to do it?
3. Data Uses (Data Mining)
– What types analytics will be performed?
– This is also where people (talent) are factored
– These are skillsets that deliver the desired capabilities
• Target customer or visitors based on propensity to subscribe, churn, engage..
• Recommend products and services to existing customers
• Optimize marketing channels and media spend
• Personalize and customize the user experience
• Develop performance indicators, business metrics (and leading indicators!)
• Shift to customer centric product development cycles
• Collect, harvest and manage new data sources (ie. Customer information)
• Introduce new revenue streams through the development of new data-based
products and services
• Instill culture of fact based decision making
• Predictive Modeling and propensity analytics
• Collaborative filtering, basket analytics, probabilistic modeling etc.
• Mixed media measurement – revenue mix modeling
• ‘In-channel’ recommendation engines
• Reporting, multi-dimensional analysis, business analysis
• Customer and product profiling and usage analytics, segmentation
• Preference Centres, lifecycle analytics (CRM)

6. What data do you have/need?
4. Data Sources (data assets)
– What data sources do you already have?
– What do you need?
– Build an inventory (data and infrastructure)
Element Description Volume Frequency Quality Source
Website Data Raw web logs 100 Gb Daily 3 Omniture BW
Billing Data Transactional 50 Gb Daily 5 Oracle DB
First Party Data
(partial)
Instrumented
logs (partial)
50 GB ‘real time’ 4 Instrumented
on device
-- -- -- -- -- --
The Art of Data Mining: creating data from data
Creativity, expertise and thought pays off a lot more than just data collection.
Good data practitioners understand how to best leverage this data in order to
address business objectives.
Great data practitioners understand the power of creating new, derived data
attributes from this data to gain even more robust customer or product
attributes.
• 2 Start/Stop Billing Date data points can equate to 10 tenure attributes
(variables)
The development of ‘data mining datasets’ is a powerful concept – should drive
us to consider our future state ‘data palette’
• Fosters customer centricity, efficiency, creativity…value

7. Roadmap
Data Sources
Data Assets
Data Uses
Data Mining and
Analysis
Capabilities
Business
Objectives
Intentions
Value
Big Data
Infrastructure and
Tools
Technology serves our roadmap. Infrastructure and tools support and enable
our capabilities – they are not THE capabilities.

8. Technology: Things to Consider
• Be aware of your circumstances
– Understand infra and operational constraints (think about people,
money, time)
– How much of the problem (and the solution) do you really own?
• Be proportionate
– How much do you really need?
– Investment in capabilities (talent) must be proportionate to investment
in infrastructure and tools
• Tools
– What tools support your talent? (it’s not the other way around!)
• Think core competencies first, then tools
– Always refer back to the framework to keep yourself in-check

9. Technology: Choices…
Infrastructure Processing
PIG
…….

10. Roadmap
Find allies/partners. Layer the problem. Focus on the palette. Be proportionate

11. Thank You
greg_doufas@hotmail.com