main street, meet mr watson - matt coatney

Main Street, Meet Mr. Watson

The Accelerating Commoditization of Smart Machines and How Businesses Can React

Matt Coatney Director, WilmerHale LLP Founder, Five Spot Research Ltd

[email protected]/in/mattcoatney

@mattdcoatney

A call to action: smart machines are here to stay

Key technologies and when to use them

How to prepare your organization

Key takeaways

Lily pads

Technology introduction AND adoption are accelerating

Source: Wall St Journal/Asymco

1900 1920 1940 1960 1980 2000 2020

StoveTelephone

ElectricityCar

RadioWasher

RefrigeratorTV

DryerAC

DishwasherColor TV

MicrowaveVCR

Game ConsolePC

CellphoneInternet

SmartphoneHDTV

Tablet

Advanced technologies ARE within reach

1-2 years 3-5 years

Grouping similar itemsClustering and classification

Predicting behavior andmaking recommendations

Classification, regression and association mining

Handling very large dataCluster computing, NoSQL, etc.

Automating decisions andimproving interaction

Deep learning/AI

Grouping similar items

What it is Clustering: groups similar objects together (no pre-assigned categories)

Classification: groups similar objects together based on category samples

Common uses Content categorization and tagging

Customer and market segmentation

Medicine and insurance

Examples of vendors

Full-text concepts

Document title

Document metadata(date, author, practice, etc.)

Grouping similar items: classification example

Brief

Contract

Memorandum

Decision tree

Association rules

Grouping similar items: clustering example

http://www.greenbook.org/Content/TRC/4ExMarketSeg.pdf

Non-Traditionals (internet)

Direct Buyers (mail/phone)

Budget Conscious

Agent Loyals (personal touch)

Hassle-Free (passive)

Survey data

Geodemographic data

Credit information

Self-Organizing Map(Neural Network)

Predicting behavior and making recommendations

What it is Classification: predict new category outcomes based on similar objects

Regression: predict new numeric values based on similar objects/past performance

Association: predict commonly co-occurring objects, e.g. items bought together

Common uses E-commerce – more like this, frequently bought together, people who viewed this also viewed, etc.

Commerce – stocking, supply chain/distribution

Fraud detection and cyber security

Examples of vendors

Predicting behavior and making recommendations: regression example

Doctors’ visits

Procedures

Prescriptions

Hospital stays

Clustering and Regression Models

(Multiple Approaches –Ensemble/Panel of Experts)

Likelihood of hospitalizationin the next year

Predicting behavior and making recommendations: association example

http://readwrite.com/2008/07/16/strands_brings_recommendation http://pivotal.io/big-data/case-study/facilitating-data-analysis-to-better-understand-and-serve-customers-zions-bancorporation

C1 checking biz card merchant payrollC2 biz card merchantC3 merchant payrollC4 merchant biz card checking….

Association Rule Algorithms

Business card => Merchant AcctChecking => Business card…

Handling very large data

What it is Cluster computing: large arrays of commodity hardware

No SQL: efficient storage and retrieval for very large, semi-structured data

Scalable machine learning: clustering, classification, etc. optimized for large data

Data visualization: techniques and tools for meaningful display of massive data sets

Common uses High-volume transactions (e.g. customer interactions, web logs)

Social media interactions

“Internet of Things” (IoT) sensor data

Scientific computing

Examples of vendors

Handling very large data

Source: Capgemini

Handling very large data: Hadoop example

http://www.intelcloudbuilders.com/docs/cloudera_WP_10_Common_Hadoopable_Problems.pdf

Customer churn at a telecom company

Customer information

Call log data

Social media data

Location/cell coverage

Handset replacement and current market options

Hadoop cluster and related analysis components

(e.g. Mahout)

Likelihood a customer would leave the carrier (e.g. friends

leaving, coverage issues)

Handling very large data: more examples


Risk Modeling

“… A very clear picture of a customer’s financial situation, his risk of default or late payment and his satisfaction with the bank and its service.”

Ad Targeting

“The model uses large amounts of historical data on user behavior to cluster ads and users, and to deduce preferences.”

Handling very large data: more examples


Retail Promotion Campaigns

“Hadoop was able to store the data from the sensors inexpensively, so that the power company could afford to keep long-term historical data around for forensic analysis. As a result, the power company can see, and react to, long-term trends and emerging problems in the grid.”

“The retailer loaded 20 years of sales transactions history into a Hadoop cluster. It built analytic applications on the SQL system for Hadoop, called Hive, to perform the same analyses that it had done in its data warehouse system—but over much larger quantities of data, and at much lower cost.”

Power Failure Prediction

Automating decisions and improving interaction

What it is More processing + better algorithms + much more data

Deep learning: incrementally trained, stacked neural networks; allows more complex, nuanced patterns to be learned

Ensemble/panel of experts: improved performance combining multiple approaches

Common uses Cognitive tasks: answering questions, speech/image/audio recognition, game play

Improved human-computer interaction

Healthcare (e.g. Watson cancer diagnosis and treatment)

Examples of vendors

Automating decisions and improving interaction: an example

The shrinking adoption curve

Then…

10-20 Years

Window ofOpportunity5-10 years

…Now

5-7 Years

3-4 years

Timing is everything

Then Now

Embrace uncertainty

Think use cases before technology

Experiment, experiment, experiment

Fire bullets, then cannonballs

What can we do?

It’s an exciting (and scary) time

WE ARE HERE

Thank You

Matt Coatney Director, WilmerHale LLP Founder, Five Spot Research Ltd

[email protected]/in/mattcoatney

@mattdcoatney