Using Big Data to Make a Big Difference in Government Jeremy - - PDF document

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Using Big Data to Make a Big Difference in Government Jeremy - - PDF document

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7/25/2016 Using Big Data to Make a Big Difference in Government Jeremy Clopton, CPA, CFE, ACDA, CIDA Director Big Data & Analytics, Digital Forensics jclopton@bkd.com TO RECEIVE CPE CREDIT Participate in entire webinar Answer

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Using Big Data to Make a Big Difference in Government

Jeremy Clopton, CPA, CFE, ACDA, CIDA

Director Big Data & Analytics, Digital Forensics jclopton@bkd.com

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Building a Foundation Emerging Technologies Analytics Framework Fraud Risk Management Reputational Risk Management Organizational Dynamics Open Data

Today’s Topics Building a Foundation

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Why This is Important

Tools

Data Analytics (ACL, IDEA, Arbutus) Data Visualization (Tableau, Analysts’ Notebook) Artificial Intelligence (Machine Learning, Social Media, Sentiment)

Techniques

Structured Data Analytics Unstructured Data Analytics Visual Analytics Relationship Mapping

Tools & Techniques

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Big Data Information of extreme size, diversity & complexity

  • Gartner, Inc.

Source: http://www.gartner.com/technology/topics/big-data.jsp

Data Analytics …processes & activities designed to obtain & evaluate data to extract useful information & answer strategic questions...

Definitions What is Analytics?

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We are… “Big Data” in Perspective

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“Big Data” in Perspective Total Information Awareness

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Data Volumes are Increasing

Source: https://nsa.gov1.info/utah-data-center/udc-photo.html

What are you doing to become data-driven?

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Important Emerging Technologies

  • Textual analytics
  • Machine learning
  • Supervised
  • Unsupervised
  • Advanced analytics
  • Predictive
  • Decision trees

New & Developing Technologies

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Textual Analytics

Network Relationship Mapping Named Entity Extraction Predictive Coding Topic Mapping Social Media Extraction Emotion Detection

Machine Learning

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  • Supervised
  • Give examples & answers, machine finds more like it
  • Unsupervised
  • Give data, machine finds patterns & applies its own rules

Machine Learning Machine Learning: Clustering

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Advanced Analytics: Outlier Detection Advanced Analytics: Logistic Regression

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Advanced Analytics: Correlation Application Framework

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  • 1. Viable
  • Problem is suited to available tools
  • 2. Valuable
  • Is it worth doing?
  • 3. Vital
  • Technology is key to success

The Three Vs for Identifying Opportunities

Strategic Question

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Strategic Question Objectives Strategic Question Objectives Data

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Strategic Question Objectives Data Procedures

Ad Hoc Individual Automated Individual Automated Groups Continuous Analytics

Procedure Development

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Strategic Question Objectives Data Procedures Strategic Question Objectives Data Procedures Analyze

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Strategic Question Objectives Data Procedures Analyze Manage Strategic Question Objectives Data Procedures Analyze Manage

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Fraud Risk Management The Fraud Triangle

Fraud

Perceived pressure facing individual Perceived

  • pportunity

to commit fraud Person’s rationalization

  • r integrity
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Fraud Example City of Dixon, Illinois

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Corruption Connections: Network Relationship Analysis

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Relationship Analysis Example Relationship Analysis Example

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Relationship Analysis Example Relationship Analysis Example

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Relationship Analysis Example Tone Detection

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Emotions: Tone Detection & Sentiment

Anger Frustration Anxiety/nervous Tension Vague/evasive Conspiratorial Sadness Intimacy Positive Negative

Emotions: Tone Detection & Sentiment

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Sentiment Analysis Tone Detection Example

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Tone Detection Example Reputational Risk Management

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Objectives

  • Identify issues & respond before they become crises
  • Proactive approach

Approach

  • Monitor trends & changes in patterns

Reputational Risk Monitoring

  • Overall sentiment trend
  • Key emotional drivers
  • Influencers
  • Proliferation of activity
  • Nature of activity
  • Location of activity &

influencers

  • Influencer relationships

Reputational Risk Monitoring – Metrics

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Reputation Data

Who What When Where Why How

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Organizational Dynamics

  • Harassment
  • Intimidation
  • Bullying
  • Discrimination
  • Favoritism

Actions Impacting Organizational Dynamics

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  • Communication patterns between categories
  • Tone of communications across levels
  • Identification of office “power brokers”

Organizational Dynamics – Metrics

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Open Data

  • Increased transparency
  • Knowledge to the masses
  • Crowdsourcing data analytics
  • Forcing governments to become data-driven

Move Toward Open Data

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Examples

Using Open Data for Data-Driven Insights

Building Permits

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Renovations New Construction 311 Calls

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311 Calls Financial Performance

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Financial Performance Employee Counts

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Closing Thoughts for the Day

  • Not designed as intrusion of

privacy

  • Not reading everyone’s email
  • Looking for signals
  • Patterns are key

What’s the Focus?

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  • Policies around data
  • wnership & use
  • Ethical considerations
  • Legal implications

Challenges to Overcome

  • Communications are used to transact business
  • Corporate assets are used to transact business
  • Transacting business = business transaction

New Mindset

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QUESTIONS?

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attendance

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please email the BKD Learning & Development Department at training@bkd.com.

CPE CREDIT

FOR MORE INFORMATION

THANK YOU!

Jeremy Clopton, CPA, CFE, ACDA, CIDA Director | BKD, LLP Practice Leader – Big Data & Analytics, Digital Forensics E: jclopton@bkd.com W: http://bkd.com/bigdata T: @JeremyClopton L: http://www.linkedin.com/in/jeremyclopton