mi dgs 16 presentation - data science and analytics – it’s a whole new ballgame by tiziana...

It ’s A Whole New Ballgame

Data Science and Analytics

Tiziana Galeazzi General Manager, DTMB

Yogi Muthuswamy Industry Director, Public Sector, Unisys

I t ’ s a w h o l e n e w b a l l g a m e

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Agenda

Video Introduction

Data Science - Applications

Digital Transformation and Digital Government

Michigan Digital Strategy and Enterprise

Information Management (EIM) Program

Use Cases

Techniques, Getting Started

Q&A

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Data Science - Definition

• Simulation• Complex Event Processing• Semantic Analysis• Multivariate Statistics• Network and Cluster Analysis

Data Science (or Advanced Analytics) is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations (Gartner)

Advanced analytics techniques include:• Machine Learning• Data / Text Mining• Sentiment Analysis • Pattern Matching• Forecasting• Visualization 4

Data Science - ApplicationsCommon applications of data science techniques and tools in our daily lives …

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Digital Transformation

By 2018, more than half of large organizations globally will compete using advanced analytics and proprietary algorithms, causing the disruption of entire industries (Gartner).

Companies can achieve competitive advantage by leveraging data, analytics and information

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Digital Transformation

• New strategies, tools, models, roles and skills are emerging

• Business leaders are taking a more active role on advanced analytics programs

• Accelerate our ability to solve complex problems by leveraging advanced analytics and big data

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Digital Government

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Digital Government – Transformation

Well-known startups such as Waze, Airbnb and Uber provide instructive examples of how Digital technologies can transform traditional service delivery models.

Each transformed their traditional business models by exploiting mountains of data and the vast connections among

people, businesses, and things.

Digital Government – Citizen Engagement

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Digital Government – Digital Organization

What does it mean to be a Digital Government Organization?

Digital Government empowers agencies to bring forward the most

advanced and innovative solutions for spending taxpayer dollars

wisely, serving citizens, and performing governments’ many missions.

Modernize and leverage legacy environments while identifying

opportunities to implement new hosting models.

Expand Digital capabilities throughout the enterprise by

building on lessons learned from earlier programs.

Collect and analyze enormous amounts of data to generate

insights for improving mission capabilities for warfighters, civilian

employees and government systems.

Digital Government – Transformation and Innovation

Successful organizations put in

place seven essential blocks for

expanding their digital operations and capabilities


Digital GovernmentMichigan Digital Strategy

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Michigan Digital Strategy• Launched in 2014• Provide digital services

where, when and how employees, business and citizens need them

Cloud First Strategy Mobile First Strategy Cyber Security Identity Management Enterprise Information

Management (EIM)

Customer Centric Government

michigan.gov/digitalstrategy

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Enterprise Information Management

• Governor’s Executive Directive for EIM• Data sharing, management and

governance framework:o Enhance Customer Experienceo Increase Transparencyo Improved Government Operations

• Established data governance model -each agency has a Chief Data Steward

• Completed statewide data inventory to determine ‘shareable’ data

• Completed first iteration of the enterprise Location Master service 25


Use Cases

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Advanced Analytics - Introduction

Use Cases – Improper PaymentsWhere Federal agencies can make poor decisions (~$125B in FY2014 – 4% of outlays of

“improper” payments)

• Technology is not the “silver bullet”

• To effectively decrease “improper” payments requires a combination of people, processes, and technologies.

People

Government Partnership

Technology

IRS Agents

Data Scientists

Former Federal Special Agents

Retired Senior State Investigators

ITIL Certified Professionals

Cyber Security Professionals

Fraud Analysts

Retired Nurses

Process

Source: www.paymentaccuracy.gov

Use Case – Counter Fraud Operational Model

• Statistical and Predictive Modeling

• Identify Resolution

• Content Analytics

• Link and Social Network Analysis

• Case Management

Use Case – Publicly available Data sets to provide insights

and transparency

• City of Chicago publishes “Data Sets” (over 1000)

• Analyzing public Data sets to gain insights

• Visualization techniques to make it engaging for citizens and others

• Relevant and meaningful information presented interactively

Quick and Agile Engagement Model –

Get Started..

Proof of Concept – Data Rationalization Deployment To Production

Identify Data Sets

Analyze Data SMEs

Define Data Products

Refine Predictive Models

Validate Analytic Results

Identify

Production

Data

Integrate Analytic Engine

Monitor

Refine

Measure

Proof of Concept 3 to 4 weeks

Limited Amount of Data – One or two ideas Production 6 weeks

Per Data Product

Refine and ImproveRefine and Improve

Technology – DIY or As a Service

Do it yourself:

• Identify the right products

• Integrate them with current BI environment

•Hire data scientists

•Create data products

Or get it as a service:

•Proven methodology

• Integrated best-of-breed products

•Cadre of data scientists

•Quick access to predictive models

Getting Started – Fraud Analytics Managed

Lifecycle Approach

• Four phased workshop based approach to understand the maturity level.

• Technology removed from the equation in the early stages - Business drives technology.

• Provide industry recommendations (i.e. known schemes, policy gaps, etc.) while listening and capturing feedback from SME’s. Guided discussion using a risk modeling tool for risk data collection, definition, and recommended prioritization.

Capabilities Assessment / Requirements Analysis / Data Discovery

Risk Models / Algorithms Development

Visualization and Report Development

Production Model Deployment

ITIL

Data Scientists – Gaining Insights

• Define the question

• Define the ideal data set

• Determine what data can be accessed

• Obtain and clean the data

• Exploratory data analysis

• Statistical prediction/modeling

• Interpret results, challenge results

• Synthesize/write up results

• Create reproducible code

• Distribute results to others

Techniques – Pharma Network Model

• Pharma network nodes provide insight into commonly related ticket issues

• Allows support agents to take a series of end user issues and better relate to the problem source

Techniques – Organization and Entity Analysis

• Analysts must spend time manually organizing records into entities

• Noisy large group of unlinked records

• Entity quality is an issue

• Processes are not repeatable

Techniques – Entity Analysis

Improving Analytics Opportunity

Superior organization of results

Analysts exploit a unified view of the

entities and their relationships

Focus on exploitation

Organization of results is automated

Analysts increase productivity

Picture represents 4,594 records

11 locations, 4 organizations and 8 persons

35 Relationships linking organizations, persons and locations

Multiple options to integrate with existing COTS or custom analytic tools

Thank You!

Tiziana Galeazzi General Manager, Department of Technology, Management and Budget, State of [email protected] Ph. (517) 241-3310

@tizianagaleazzi

Yogi Muthuswamy Industry Director, Global Digital Government,Enterprise Solutions, US & C, [email protected] Ph. (919) 225-7684

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mailto:[email protected]

mailto:[email protected]

mi dgs 16 presentation - data science and analytics – it’s a whole new ballgame by tiziana...

Government & Nonprofit