Using Big Data Analytics to Improve Government Performance

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Alan Goon

Using Big Data Analytics to

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Gartner delivers the technology-related

insight necessary for our clients to make

the right decisions, every day.

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The

Nexus of Forces Is Driving

Innovation in Government

Extreme

Networking

Rampant

Access

Global Class

Delivery

Rich Context,

Deep Insights

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In 2020:

Everyone

and

Everything

is

Connected

to Internet

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Information

And Noise is

Exploding at

Exponential

Rates

“Big Data”

Creates Big

Challenges:

Volume

Velocity

Variety

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#GartnerSYM

Are you getting

the most

business value

from your data?

1 1 0 1 ₀ 1 0 ₁ 1 0 1 1 1 0 1 ₀ 1 0 ₁ 1 0 1 1 1 0 1 0 1 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 0 1 ₀ 1 0 ₁ 1 0 1 1 1 0 0 1 0 1 1 0 0 0 1 1 1 ₁ 1 0 1 1 1 1 0 0

Government Information Is a Public

Asset

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A Digital Revolution Is Under Way —

Causing a Re-imagination of Everything

Business Models

, Politics, The

Economy, Trade, Computing Devices,

Operating Systems, Connectivity,

News, Information Flow, Storage,

Magazines, Money, Payments,

Knowledge, Note-taking,

Transportation, Funding & Lending,

Recruiting, Production, Education,

Thermostats, Music, Shopping…

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Key Issues

1. What is Big Data?

2. How does big data improve government

performance?

3. What are the use cases for

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#GartnerSYM

Key Issues

1. What is Big Data?

2. How does big data improve government

performance?

3. What are the use cases for

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#GartnerSYM

What Is "Big Data"?

• A scientific breakthrough

(Hadoop in 2005) for

handling vast amounts

of data?

• A marketing ploy to get

you to spend more on IT?

• A useful set of barely-related

technologies for large-scale

analytics?

• Not really

• Sometimes

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"Big Data" Defined

"Big data" are high-volume, high-velocity and high-variety information

assets that demand cost-effective, innovative forms of information

processing for enhanced insight and decision making.

Source: The Importance of 'Big Data': A Definition, Mark Beyer, Douglas Laney, G00235055

VOLUME

VELOCITY

VARIETY

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Variety of

data

48%

Volume of

data

35%

Velocity of

data

16%

Source: Getting Value From Big Data, Gartner Webinar, May 2012

Which Big Data Characteristic Is the

Biggest Issue for Your Organization?

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By 2015, more than 30% of analytic projects

will deliver insights based on

structured

and

unstructured data

.

Strategic Planning Assumptions

Correlating, analyzing, presenting, and embedding insights

from structured and unstructured information together enables

government agencies to better personalize the constituent

experience and identify opportunities for efficiencies,

innovation, and even new business models.

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Key Issues

1. What is big data?

2. How does big data improve government

performance?

3. What are the use cases for

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The Primary Goal of Big Data:

Analytics to Give Your Organization an Edge

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BI and Analytics: A Core Business

Competency

It’s all about decisions: enabling businesses to

strengthen their decision-making capabilities by

applying the latest and best methods of analysis

to the most important decisions facing them.

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New Reality:

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Reality Check: The Current State

• Today, 80% of analytic investments have been in

producing reports from lagging information.

• The volume, velocity and variety of new

information sources and the need for new

analytical capabilities are increasingly presenting

challenges and opportunities for organizations.

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Acknowledge Big Data Analytics

Initiatives Are Unique

Technical

Challenges and

Opportunities

Business

Challenges and

Opportunities

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Government CIOs Have More Sources of

Information to Do More With

Open

Social

Commercial

Public

Operational

Enterprise

"Dark Data"

Text Image Mobile Transactional Data Social Audio Video

IT/OT Search _Engine Documents

Prescriptive

Analytics

What should

happen?

Predictive

Analytics

What will

happen?

Diagnostic

Analytics

Why did it

happen?

Descriptive

Analytics

What

happened?

Open Data

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What Will

Happen

?

Level of Business Impact

It’s all about decisions – why is

information and analysis so important?

What Should

Happen?

Why Did it

Happen

?

What

Happened?

Skill Levels Required

Better

Decisions

Information

Descriptive

Analytics

Predictive

Analytics

Prescriptive

Analytics

Diagnostic

Analytics

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Information

Analysis

Prescriptive

_Analytics

Diagnostic

Analytics

Predictive

Analytics

Descriptive

Analytics

Information, Analysis And Decisions:

The Basics

What Should I Do About It? What Is Likely To Happen? Why Did it Happen? What is Happening?

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Information

Prescriptive

Analytics

Diagnostic

Analytics

Predictive

Analytics

Structured

Hybrid

Unstructured/Content

Analysis

Information and Analytics Today

Descriptive

Analytics

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Information

Prescriptive

Analytics

Descriptive

Analytics

Predictive

Analytics

Diagnostic

Analytics

Structured

Hybrid

Unstructured/Content

Analysis

Emerging Trends: Information and

Analytics

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Data Science Drives All Kinds of

Analytics Sophistication

Da

ta

D

eci

si

on

Human Predictive Anticipating Human Diagnostic Understanding Human Descriptive Monitoring Prescriptive Controlling

A

ct

ion

Decision Automation Human Decision Support

Link Analysis, Clustering, Decision Trees

Inversion, Sensitivity Analysis, Design of Experiments

Neural Nets, Nearest

Neighbor, SVM, Time Series, Markov Chain Modeling ... Optimization, Decision

Management,

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Linking, Mining, and Sharing Data Expand

Information Management Practices

Why

What

How

Whose

Government Explanatory; Historical Structured; "Simple" Offline; Batch Predictive; Outcomes Unstructured; Linked In-line; Real Time Social

Open Data More sources

Social Data More relationships Shared Data More stakeholders Linked Data More context

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Big Data Capabilities Capture the

Business Value

Correlations and patterns from disparate, linked data sources

yield the greatest insights and transformative opportunities

Bi g Da ta Ca pa bi li ti e s

Primary Use Cases

Constituent Insights and Engagement Program and Outcomes Management Operations and New Services Risk Management and Public Safety Fraud Detection Ability to Store and Process Unstructured Data

Ability to Link Data of Various Types

Ability to Affordably Perform Comprehensive Analysis

Transactions Geographic Economic Contracts Sentiment Sensor Reports Monitoring Demographic Weather Mobile Network Email Industry

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Business Drivers:

• Cost-reduction

opportunities

• Improved outcomes

• Changing priorities or

use cases

• New business models

• Unused dark data

• Compliance (executive

order or legislative action)

Technology Drivers:

• Combination of structured

and unstructured data

• Performance issues with

enterprise data warehouses

• Traditional technology does

not scale

• Technology cost reduction

• Program grant requirements

Typical Drivers for a

Big Data Analytics Initiative

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Approach for Generating Big Ideas for

Big Data

Business Drivers Information Assets Possibilities 1. Generate hypotheses 2. Gather data 3. Analyze data 4. Draw conclusion 5. Re-test  Useful conclusion?  Actionable?  Feasible?  Defensible?  Implement it! internal external internal external

?

Examples internal external

What is important to us?

Could we adapt what they've done?

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The Road Map: Typical Stages and

Milestones of Big Data Adoption

Unaware

Experimental

Opportunistic

Strategic

Transformative

Aware

Time Value Initial implementation of the LDW Traditional technology cannot meet all needs

Information-driven

Enterprise

Big data initiative is justified Big data strategy

planned

Stabilized big data infrastructure Information governance is a must Data products emerge Big data is becoming the new normal Ramp up (investments outstrip returns) A milestone

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Key Issues

1. What is Big Data?

2. How does big data improve government

performance?

3. What are the use cases for

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#GartnerSYM

Predictive Policing

• Opportunity:

- Increase Los Angeles police presence where it's most likely needed

• Data and Analytics:

- PredPol applies models for predicting earthquake aftershocks to historical crime data and other

factors

• Results:

- Predicted twice as many crimes as experienced crime analysts in controlled trials

- 33% reduction in burglaries and 21% reduction in violent crimes in test region of Los Angeles,

compared to a slight increase in the rest of the city

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Police Predict Predator's Position

• Opportunity:

- Increase the speed of Swedish police investigations

• Data and Analytics:

- Communication behavior from phone calls in combination with crime statistics, weather, day of week, and city events

- Analyzed data from over 500,000 interrogations, evidence, and background info. using QlikView

• Results:

• Reduced 9 months of manual analysis to 3 minutes of

automated analytics

• Helped locate a serial killer in the city of Malmö by

calculating the time and location of the next shooting

• 6.7M krone reallocated from administration to law

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Dial Algorith-M For Murder

• Opportunity

- Reducing homicide rates - Safer communities

• Data and Analytics

- Dataset of two dozen variables on 60,000 crimes - Predictive algorithm created by University of

Pennsylvania

• Results

- Contradicts and corrects conventional wisdom among law enforcement community

- Improves (optimizes) parolee supervision and reduces dept of corrections expense

- Can predict 8 out of 100 homicides in MD, PA and DC

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Car Talk

• Opportunity

- Improve safety and efficiency of vehicles

• Data and Analytics

- OpenXC is an API for cars that can make telemetry data accessible to Android and desktop apps via a small hardware module

- Signals and status supported: steering wheel angle, torque, engine/vehicle speed, accelerator/brake status, odometer, ignition, fuel, windshield wiper, GPS, door, etc.

• Results

- Collected and analyzed Ford car data using Splunk to compare electric vs gasoline driving tendencies

- Identified the best place for police speed traps (but didn’t tell them!)

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Electing to Use Advanced Analytics

• Opportunity:

- Understand and connect with voters and donors at a more granular and personal level

- Improve on previous analytic agility and speed

• Data and Analytics:

- Integrated data from digital and other channels

- Continual voter modeling using KXEN to predict voter segment receptiveness and behavior

- 66,000 election simulations per night

• Results:

- Raised $1B in campaign funds with fewer resources by communicating with the right individuals at the right time on the right medium

- Grew digital fundraising by 20%, added 500,000 donors, increased ad. buying efficiency by 15%, and improved volunteer and per-donor rates over 2008

Obama for America Campaign

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Tomorrow's News, Today

• Opportunity:

- Identify systemic, related or mounting health, geopolitical or financial issues

• Data and Analytics:

- Continually capture, translate, classify, and analyze 40,000 local news reports in 43 languages from around the world

- Custom charting, alerts, animated map visualization of categories or search terms

- Predict and highlight emerging hot topics and trends

• Results:

- Enable politicians, insurers, investors, disease control institutions, and global support groups to respond quicker to local or global issues

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Analytics Is the Prescription for Patient

Medicine Compliance

• Opportunity:

- Identify and intervene with patients who are less likely to take their prescriptions correctly

• Data and Analytics:

- Patients taking meds. for high blood pressure, diabetes, high cholesterol, asthma, osteoporosis, and multiple sclerosis

- Analyzes 400 variables including prescription history, economic makeup of the patient's neighborhood

• Results:

- 90% compliance prediction accuracy

- Tailored manual interventions including phone calls and help with signing up for auto-refills

- Beeping bottle caps increased compliance by 2%; timers given to forgetful patients improved their compliance 16%

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Listening to United Voices

• Opportunity:

- Evolving from reactive to proactive responses to global issues

- Developing and guiding assistance programs

• Data and Analytics:

- Mining social networks to predict job losses,

spending reductions, or disease outbreaks within a region

- Natural language deciphering

• Result (TBD):

- Early warning signals to guide assistance programs for preventing regions from slipping into poverty, epidemics, or war

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Stretching Analytic Performance to

Measure Price Elasticity

• Opportunity:

- Business lacking the ability to react to market conditions and new product launches

• Data and Analytics:

- 8.9B sales line items, 1.4B SKUs, 1.8B rows of inventory, 3,200 stores

- Entire solution moved from mainframe to Hadoop - Calculating store-item price elasticity over 12.6B

parameters

• Results:

- Price elasticity now measured weekly against all data instead of quarterly against a subset

- $600K annual savings; 6,000 lines of batch code reduced to 400 lines of PIG

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Recommendations



Do not postpone the implementation of big data

analytics, but develop an information management

strategy first.



Identify big questions relevant to big data.



Understand big data technology capabilities and

manage organizational impacts.



Validate your big data assumptions in a proof of

concept.

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