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

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 polls when they are provided • If you are viewing this webinar in a group  Complete group attendance form with • Title & date of live webinar • Your company name • Your printed name, signature & email address  All group attendance sheets must be submitted to training@bkd.com within 24 hours of live webinar  Answer polls when they are provided • If all eligibility requirements are met, each participant will be emailed their CPE certificates within 15 business days of live webinar 1

7/25/2016 Today’s Topics Building a Foundation Emerging Technologies Analytics Framework Fraud Risk Management Reputational Risk Management Organizational Dynamics Open Data Building a Foundation 2

7/25/2016 Why This is Important Tools & Techniques Tools Techniques Artificial Data Analytics Data Visualization Intelligence Structured Unstructured Visual Relationship (ACL, IDEA, (Tableau, Analysts’ (Machine Learning, Data Data Analytics Analytics Mapping Social Media, Arbutus) Notebook) Analytics Sentiment) 3

7/25/2016 Definitions 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 ... What is Analytics? 4

7/25/2016 We are… “Big Data” in Perspective 5

7/25/2016 “Big Data” in Perspective Total Information Awareness 6

7/25/2016 Data Volumes are Increasing Source: https://nsa.gov1.info/utah-data-center/udc-photo.html What are you doing to become data-driven? 7

7/25/2016 Important Emerging Technologies New & Developing Technologies • Textual analytics • Machine learning  Supervised  Unsupervised • Advanced analytics  Predictive  Decision trees 8

7/25/2016 Network Relationship Mapping Emotion Named Entity Detection Extraction Textual Analytics Social Media Predictive Extraction Coding Topic Mapping Machine Learning 9

7/25/2016 Machine Learning • Supervised  Give examples & answers, machine finds more like it • Unsupervised  Give data, machine finds patterns & applies its own rules Machine Learning: Clustering 10

7/25/2016 Advanced Analytics: Outlier Detection Advanced Analytics: Logistic Regression 11

7/25/2016 Advanced Analytics: Correlation Application Framework 12

7/25/2016 The Three Vs for Identifying Opportunities 1. Viable  Problem is suited to available tools 2. Valuable  Is it worth doing? 3. Vital  Technology is key to success Strategic Question 13

7/25/2016 Strategic Question Objectives Strategic Question Objectives Data 14

7/25/2016 Strategic Procedures Question Objectives Data Procedure Development Ad Hoc Individual Automated Individual Automated Groups Continuous Analytics 15

7/25/2016 Strategic Procedures Question Objectives Data Strategic Procedures Analyze Question Objectives Data 16

7/25/2016 Strategic Procedures Analyze Question Objectives Data Manage Strategic Procedures Analyze Question Objectives Data Manage 17

7/25/2016 Fraud Risk Management The Fraud Triangle Perceived Perceived pressure opportunity facing to commit individual fraud Fraud Person’s rationalization or integrity 18

7/25/2016 Fraud Example City of Dixon, Illinois 19

7/25/2016 Corruption Connections: Network Relationship Analysis 20

7/25/2016 Relationship Analysis Example Relationship Analysis Example 21

7/25/2016 Relationship Analysis Example Relationship Analysis Example 22

7/25/2016 Relationship Analysis Example Tone Detection 23

7/25/2016 Emotions: Tone Detection & Sentiment Emotions: Tone Detection & Sentiment Anger Positive Frustration Negative Anxiety/nervous Tension Vague/evasive Conspiratorial Sadness Intimacy 24

7/25/2016 Sentiment Analysis Tone Detection Example 25

7/25/2016 Tone Detection Example Reputational Risk Management 26

7/25/2016 Reputational Risk Monitoring Objectives • Identify issues & respond before they become crises • Proactive approach Approach • Monitor trends & changes in patterns Reputational Risk Monitoring – Metrics • Overall sentiment trend • Nature of activity • Key emotional drivers • Location of activity & influencers • Influencers • Influencer relationships • Proliferation of activity 27

7/25/2016 When Where What Why Reputation Who How Data 28

7/25/2016 Organizational Dynamics Actions Impacting Organizational Dynamics • Harassment • Favoritism • Intimidation • Bullying • Discrimination 29

7/25/2016 Organizational Dynamics – Metrics • Communication patterns between categories • Tone of communications across levels • Identification of office “power brokers” 30

7/25/2016 Open Data Move Toward Open Data • Increased transparency • Knowledge to the masses • Crowdsourcing data analytics • Forcing governments to become data-driven 31

7/25/2016 Using Open Data for Data-Driven Insights Examples Building Permits 32

7/25/2016 Renovations New Construction 311 Calls 33

7/25/2016 311 Calls Financial Performance 34

7/25/2016 Financial Performance Employee Counts 35

7/25/2016 Closing Thoughts for the Day What’s the Focus? • Not designed as intrusion of privacy • Not reading everyone’s email • Looking for signals • Patterns are key 36

7/25/2016 Challenges to Overcome • Policies around data ownership & use • Ethical considerations • Legal implications New Mindset • Communications are used to transact business • Corporate assets are used to transact business • Transacting business = business transaction 37

7/25/2016 QUESTIONS? CONTINUING PROFESSIONAL EDUCATION (CPE) CREDITS BKD, LLP is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.learningmarket.org. The information in BKD webinars is presented by BKD professionals, but applying specific information to your situation requires careful consideration of facts & circumstances. Consult your BKD advisor before acting on any matters covered in these webinars. 38

7/25/2016 CPE CREDIT • CPE credit may be awarded upon verification of participant attendance • For questions, concerns or comments regarding CPE credit, please email the BKD Learning & Development Department at training@bkd.com. THANK YOU! FOR MORE INFORMATION 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 39