Big Data
What does it mean?
How do we mine it?
John Johansen
October 2013
jjohansen@agiletech.com
Session Objective
Who saw that coming!
• Our organizations’ data is increasingly capable of helping us anticipate and plan for the
unexpected.
– Powerful tools are emerging to help identify patterns and make predictions of potential risks and opportunities.
– These predictive analytics will allow companies to focus on the real trouble spots and develop the right conclusions.
• This session will explore and demonstrate these tools while identifying potential applications and
solutions in our day-to-day jobs.
• At the conclusion of the session participants will understand
– The role that analytics can play in supporting your organizational objectives.
– How these tools can identify elevated risk and help plan effective strategies to maximize opportunities.
Who saw that coming? You did!
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Agenda
• Big Data Explained
• Why Are These Solutions Emerging Now ?
• Some Common and Not-so Common Applications in our Businesses
• The Steps in the Mining Process
• A Real Live Demo of a Mining Process
• Questions
• Wrap - Up
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But first… a promise…
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But first… a promise…
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But first… a promise…
No plunge !
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Big Data Explained
RF Tagging
Unstructured Data
Healthcare Monitoring
Variety
Sensor Data
Velocity
> Peta? Zetta
Volume
Voice of the Customer
Real Time Data
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Text Analysis
Four Emitters Connected to the Internet of Things
• Cows & Crops
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Transactions, Interactions & Observations
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Big Data in the Gartner Hype Cycle
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There are less scientific hype indicators
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Why Now?
• Chances are, the tools you need are the tools you have.*
• At long last, the data that you need is the data that you have.
• The processing power that you need is the processing power that you
have.*
* And if you don’t already have them, they are readily available
with very reasonable ROI
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Four Emitters Connected to the Internet of Things
• Cows & Crops
• Christmas Trees
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Data is Increasingly a Differentiator
High
Differentiation
Predictive Analytics
Operational Analytics
Data Discovery
Interactive Dashboards
Production Reporting
Low
Low
Sophistication
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High
Predictive Analytics
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Potential Challenges
“Prediction is very difficult…
Especially if it’s about the future.”
Casey Stengel
Niels Bohr
President Bill Clinton
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Predictive Analytic Solutions
• We see these solutions as a class of software tools that will look
through large sets of data to uncover subtle patterns in data, then use
those patterns to predict the behavior of a new set of data.
• The impact these solutions are having at companies are generally in
the areas of:
– Improving the profitability of existing clients by identifying high probability
cross-selling activities.
– Improving the effectiveness of direct marketing programs through more
focused, higher likelihood of success programs.
– Identifying fraud.
– Identifying likely candidates for churn either in the customer-base or in the
channel.
– Modeling customer reactions to price or term changes.
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Organizations Embrace Analytics Differently
Killer Applications
What are the steps?
• As with any other solutions, we suggest starting at the end: Outlining
specifically what we are hoping to achieve.
• Next we need to establish at least two sets of data from our existing
historical data. One will help us Train the model, the other will be a Test
set that will allow us to validate that in fact the model that we create is
valid.
• Once we’re sure we’ve got a model that establishes the right
relationships, we can run our source data through our model and get our
predictions.
• Analyze, Rinse. Repeat.
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Four Emitters Connected to the Internet of Things
• Cows & Crops
• Christmas Trees
• Pill Bottles
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Demonstrating the Power
• We’ve arranged for a quick demo of the Microsoft tools.
– Churn Prediction
– Claim Amount
– Cross Selling
• It’s interesting to note, that this demonstration is running on a simple
server, using software tools that are included, free, with Microsoft
SQLServer.
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Demonstration – Predicting Churn
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Demonstration – Claim Cost
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Demonstration – Cross Selling
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Four Emitters Connected to the Internet of Things
• Cows & Crops
• Christmas Trees
• Pill Bottles
• Diapers
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Questions?
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In conclusion…
• Big Data Explained
• Why Are These Solutions Emerging Now ?
• Some Common and Not-so Common Applications in our Businesses
• The Steps in the Mining Process
• A Real Live Demo of a Mining Process
• Questions
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In conclusion…
“I figure lots of predictions is best.
People will forget the ones I get
wrong and marvel over the rest.”
Alan Cox
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Contact
John Johansen
Partner
Agile Technologies, LLC
One Easton Oval, Suite 388
Columbus, Ohio 43219
jjohansen@agiletech.com
www.agiletech.com
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