Big Data What does it mean ? How do we mine it?

Big DataWhat does it mean? How do we mine it?

John JohansenOctober 2013jjohansen@agiletech.com

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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

Volume

Velocity

RF Tagging

Healthcare Monitoring

Unstructured Data

Sensor Data

Real Time Data

> Peta? Zetta

Voice of the Customer Text Analysis

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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

Production Reporting

Interactive Dashboards

Data Discovery

Predictive AnalyticsOperational Analytics

Diffe

rent

iatio

n

Sophistication

High

Low

Low High

Predictive Analytics

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Potential Challenges

“Prediction is very difficult…

Niels Bohr

Casey Stengel

President Bill Clinton

Especially if it’s about the future.”

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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.

Organizations Embrace Analytics Differently

Killer Applications

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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 JohansenPartner

Agile Technologies, LLCOne Easton Oval, Suite 388

Columbus, Ohio 43219

jjohansen@agiletech.com

www.agiletech.com