Derby Tech Forum

1. Derby Tech Forum May 16, 2013

2. Greg Brendel | VP Sales & Marketing

3. Agenda Greg intro                 a. Thanks for everyone coming b. Why we have the Derby Tech Exec Forum c. Introduce V-Softers in audience d. Drink tickets to use (but come back into room to participate) e. Introduce Panelists f. Q&A period at the end (write them down) g. Slides available as an email for attendees after event Panelists presentations                 a. Bob Doligale - Splash Analytics b. Chris Schremser - CTO ZirMed c. Brian Oldham - CTO Appriss Q&A and Networking- Bar open *Fabulous prizes awarded at the end of the program (question askers are apt to win) *Survey – paper survey for suggestions (other topics, locations, and speakers)

4. Who We Are • 16 years of IT Excellence. • Headquartered in Louisville, KY with locations in Tampa, FL, Madison, WI, Atlanta, GA, Toronto,Canada, and Hyderabad, India. • Minority Woman owned private firm. • Revenue in excess of $35M annually. • Over 500 contractors currently in place in Fortune 500 and 1000 companies across the county, • The main strength of our company is our equal dedication and commitment to both our clients and our consultants: • The consultant is committed to the project, • The client achieves their objectives, and • V-SOFT has developed loyal partnerships with both the client and the consultant. • We provide technical skills that span the entire range of development environments from mainframe legacy systems to state-of-the-art E-Commerce systems.

5. Who We Work With

6. Our Partners

7. Over 18 years of firsthand entrepreneurial experience creating and building companies. A strong proponent of company culture centered around staying client-focused and continually providing incremental value to customer relationships.

Began his career with GE in the Manufacturing Management Program. Spent 7 years in various engineering, finance, and operations management positions before leaving to co-found ACCENT Marketing Services in 1994.  Bob is a Bellarmine MBA and graduate of the University of Illinois at Urbana-Champaign. Company Founded: 2011 Number Employees: 10 + www.SplashAnalytics.com Bob Doligale Co-Founder & President Splash Analytics

8. At ZirMed my role is that of a lead architect responsible for the strategic vision of the technology platform, ensuring that all factors of engineering mesh cohesively in order to deliver a superior finished product. I also serve as chief security officer and am responsible for ensuring that all systems meet complex regulations, including HIPAA, HITECH, PCI, and others. Chris is a University of Evansville graduate Company Founded: 1999 Number Employees: 300 + http://public.zirmed.com/ Chris Schremser CTO ZirMed

9. I am a results oriented and experienced Chief Technology Officer (CTO) with over 12 years experience in identifying, implementing and managing technologies that create top line business growth or improve efficiencies. I am a proven operational leader with experience in managing technical operations and call center staff.

I consider myself as a strategic and analytical leader with a proven ability to lead diverse teams to deliver critical solutions. I have a strong business background and demonstrated track record of partnering with business leaders to deliver successful solutions in the marketplace. Brian is a Purdue University graduate Company Founded: 1994 Number Employees: 400 + www.appriss.com Brian Oldham CTO Appriss

10. Bob Doligale | President

11. Who We Are • A data analytics company founded with a mission of making profitable use of the millions of data points that our clients capture every day. • An experienced group with over 50 years of combined marketing, operations and data analytics expertise utilizing industry leading tools and techniques. • An accomplished team that has consistently helped clients, big and small, across multiple industries meet and exceed their goals by leveraging analytics and business operations expertise.

12. What We Do Our expertise lies in our ability to uncover hidden patterns, trends and inconsistencies in data, from all sources, allowing us to build predictive models that can be used to improve profit and operational effectiveness across an organization. Utilizing Advanced Analytics Techniques: • Predictive Modeling • Customer Segmentation Analysis • Customer Profiling • Cluster Analysis • Pricing Optimization • Multivariate Test Design • Lifetime Value Analysis • Prospect Lead Scoring • Lead Valuation • Cross-sell/Up-sell Optimization

13. How We Help We are experts at helping organizations seize opportunities and achieve their potential by applying our proven tools and techniques to answer everyday business questions, like: • How can I sell more products? • Which discharged patients are most likely to return? • To whom, how and when should I communicate my marketing messages? • Which transactions are possibly fraudulent? • How can I reduce customer attrition? • What anomalies exist in an insurance/warranty claim? • How do I maximize my return on marketing investment? • How could I improve my operational efficiencies? • What price point will maximize profitability?

14. How We Are Different There are many analytics companies out there with smart analysts running the latest and greatest tools. Our difference lies in our marketing and business management experience. We don’t simply generate the best-fit model and turn it over with a stack of charts and graphs. We leverage the insights gleaned in the data, while working with your team, to develop actionable strategies that will maximize your profitability. Most often the optimal solution lies in a coupling of the quantitative, which is discovered in the data, with the qualitative, which is learned through years of marketing and operations experience.

15. How We Do It CUSTOMER DATA MODELING SCORING ANALYTICAL PLATFORM Response Model Customer Value Model Low Medium Customer Value Model High Scoring Process Data Response Model Segment Cross-Sell Model Product B Product C Product A High Low Cross-Sell Model

16. Enough about us, let’s talk Big Data…

18. Cheap Storage = More Data • In 1987, a 10MB full height hard drive sold for about $800. At that rate, storing the 4 Zettabytes of data accumulated to date would cover the entire state of Texas to a depth of 8 feet at a cost of $360 million-billion, or roughly 5,000 times the GDP of the entire world. • Fortunately, technology has driven the cost of data storage down so low that the same volume of data could be housed easily in the Yum! Center for a mere $208 billion. • Today you could personally store every song ever recorded on your home PC for about $800. • By 2020, the amount of data collected is expected to grow 9-fold to 35 Zettabytes. • With storage media under a nickel per gigabyte, it’s now cheaper to simply capture and store everything forever.

19. More Data = More Insight • Data analytics opportunities abound as companies move past the “What Happened?” mode of reporting to the “What Will Happen Next?” perspective made possible with predictive analytics. • Because “the data is the data”, proper analytics provides unbiased insight into the questions traditionally debated on gut feel, instinct, and anecdotal evidence. • Federal, state, and local governments are now commonly publishing public-sector data (Louiestat.gov, Data.gov). • User-generated data is allowing for real-time analytics and decision making (Waze, TSA wait times, theme park line waits).

20. Traditional Data Modeling • For the past decade, our traditional engagements involved querying multiple database tables via SQL, extracting and joining the datasets, splitting data into training and validation sets, and building predictive models. • Tools used include SQL to extract data and SPSS Modeler, SAS, Octave, and R to build predictive algorithms. Analysis is generally reported in Tableau when applicable. • Techniques are primarily linear and logistic regression, k-nearest neighbors, principal component analysis, etc. with a goal of solving for some desired outcome (likelihood to purchase, best customer profile, attrition risk, underwriting exposure, etc.).

21. Big Data Modeling • Our recent activity is geared toward supervised machine learning on both structured (SQL) and unstructured (noSQL) data in a distributed data store (Hadoop, Hbase, Dynamo, MongoDB). • Tools include the previously mentioned plus Mahout, Knime, and Python to support MapReduce functionality. • Techniques still include linear and logistic regression with the addition of high-dimension classification techniques such as Random Forest, Support Vector Machine (SVM), Naive Bayes, and Locality-Sensitive Hashing (LSH). • Binary and multinomial classification models seem to dominate (fraud/no fraud, expect readmit/don’t expect readmit, etc.).

22. Discussion • Create and commit to a “data-centric” business philosophy, regardless of the size of your company. Competitive advantages will be huge for those that capture and effectively use data. • Don’t let “perfection be the enemy of the better.” Better to build on small successes vs. suffer from “analysis-paralysis.” • Data scientists will be in short supply as companies build infrastructure to support their data initiatives, so don’t be afraid to use strategic partners. • While more and more tools are evolving in the open source arena, proper implementation will remain a challenge.

23. Agenda • Agenda Placeholder • Agenda Placeholder • Agenda Placeholder • Agenda Placeholder The Vs of Big Data Chris Schremser | CTO

24. ZirMed’s History and Market Position Today ZirMed’s solutions connecthealthcare providers with insurance companies, patients, and each other to optimize collections and managetheir businesses effectively Health Information Company Revenue Cycle Technology 7000 Customers 1,300 Payers 160,000 Providers Financial Services Claims Clearinghouse 500 SoftwareSystems 450,000,000 AnnualTransactions

25. How ZirMed Operates in the Industry ZirMed offers the healthcare industry a pragmatic information exchange that bring together siloed data to drive better clinical and financial performance Anesthesiology ZirMed Link Platform = 100% Physical Therapy Billing Services Surgery Centers Other Hospitals Specialists Emergency Radiology DME/HME Skilled Nursing Consumer Portals Retail Clinics Other Physicians Long Term Care Physician Practices Hospital Ambulance Payers Diagnostic Centers Public Health Home Health

26. ZirMed Solution Overview • Payer Payment • paper EOB conversion • ACH deposits • automatic reconciliation and posting Information and Data Management for Healthcare • Claims Management • claims • remittances • coding tools • PQRS • Patient Payment • patient statements & communications • online payments • payment portal • lockbox services Our Cloud-Based Solutions surround, supplement, modernizes and protect the investments in your core systems. • Front Office • eligibility • patient estimation • ZPay collect • self pay 360 • Clinical Support • patient referral management • care transition • CCD delivery • universal access network Enterprise MPI Analytics Suite ZirMed University Developers Suite

27. Healthcare Market Conditions Healthcare is one of the most dynamic and challenging industries in the U.S. today … And yet healthcare technology lags behind other industries by years if not decades. TECHNOLOGY REQUIREMENTS Lack of interoperability, 5010, ICD-10 ECONOMICS New Payment Models, Bundled Payments, Performance-based CONSUMERISM Patient Engagement, Transparency, Patient-to-Provider Communications DEMOGRAPHICS Aging Demographics, Population Health Needs REGULATIONS HIPAA, Health Reform, Meaningful Use

28. Gartner’s definition of “Big Data” What is Big Data? “Big Data” is high-volume, -velocity and –variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. The particular use of data in healthcare necessitates expansion of the definition… Healthcare information assets must be continually verified and evaluated for truth and tested for use cases and protected against any unintended or incidental disclosure.

29. V foundations of Big Data

30. Volume • 2,500,000,000,000,000,000 bytes of data created in 2012 • 90% of the World’s data is merely 2 years old • Big Data is invading virtually every business sector • Data scientists are the most in demand job over the next 5 years • A single patient stay generates thousands of data elements, including diagnoses, procedures, medications, medial supplies, lab results and billing. • All data must be validated, processed and integrated • Then look at hundreds or thousands of patients

31. Visualizing an Internet Minute

32. Demand for Big Data

33. Velocity • Velocity of data is more than speed. • Velocity means how fast the data is being produced • It is about the rate of changes, about linking data sets that are updating at different speeds and about bursts of activities. • The ability to process data when an event changes or activate new data upon a change in a data event. • Traditional data warehouse analytics tend to be periodic – daily, weekly, monthly or even quarterly • Clinical decisions require up-to-date information. • Current data is needed to support automated decision-making.

34. Variety • Big Data is about the ability to aggregate many types of data, both structured and unstructured, such as: • Multimedia • Social Media • Blogs • Web Server logs • Financial transactions • Tracking information • Audio/video streams • Web content • E mail • Dictation (audio & text) • Claim data • Clinical data • Pharma data • Monitoring and meter-collected systems

35. Platform Variety

36. Veracity • Data is pure, true and correct • Data can be corrupted • Troubles arise when sources of data and the actual data is not understood and evaluated • Moving to a data driven organization creates new risks of acting on corrupt or misinterpreting data • Veracity leads to increasing data intelligence

37. Degree of Data Intelligence

38. Vulnerability • Big Data will turn privacy upside down. • Valuable patient data is toxic if breached • AMA tiered penalties for even an incidental breach range from $100 to $1.5 million • Data use and sourcing maps should be included with every data dictionary • Don’t be scared of the stuff you don’t know, be scared of the stuff that is logical

39. Vulnerability – Data Loss

40. Recommendations • Build a Data Science Team • Big Data = Technology • Create a data strategy based on the organizations technical and business maturity • Create a list of quick, big data wins and execute those successes quickly • Visualization

41. Brian Oldham | CTO

42. Appriss… Delivers deeply imbedded, SaaS public safety applications Unique data that solves problems for both government and commercial markets Which produce… Leveraged to… Prevent fraud Manage risk Fight crime Ensure compliance Indispensible solutions, unique data, outstanding financial results 42

43. Appriss Highlights Unique Data Incarceration “One of a kind” company Consumer Purchases Citations Warrants Booking Photos Collision Reports Parolee records • Leading provider of SaaS based public safety solutions • Unique data assets created as a by product of our solutions. • Unmatched brands within state and local markets • Dominant market positions with little or no competition • Exciting financial performance – with accelerating growth and profitability Deeply imbedded Solutions Trusted Customers Strong partners Exceptional Growth 21%+ 2012 revenue growth 28%+ 2012 EBITDA growth 17 years of experience delivering national scale public safety solutions ‘10 ‘11 ‘12 ‘13 ‘14 43

44. Accelerating growth via “data for service” business model A powerful data generation platform that cannot be reproduced Future Launch of free mobile publishing platform for Sheriffs 2012 Commercial risk and compliance platform launched Partners with NABP to launch Prescription Drug Diversion Platform 2011 Nations first statewide electronic warrant platform Acquisition of OPS - the nations only outsource provider of statewide collision report repositories 2010 2010 Appriss begins transformation of business model from fee for service to data for service 2009 National PSE Tracking solution launched by OTC drug manufacturers 2009 New investors (Bain, JMI) begin to focus the company on leveraging data 2007 2000 JusticeXchange launched to provide law enforcement with access to proprietary offender data 1995 VINE is launched and our national offender data network begins 44

45. Big data? • Volume • Large scale data (Terabytes and Petabytes) • Velocity • Billions of transactions (metering, mobile, etc) • Variety • Strings, numbers, XML, photos, video, more

46. Big Data at Appriss Risk and Compliance engine - New Technologies Consumer app – usage patterns Data platform for most Appriss apps

47. Non-FCRA data store FCRA data store Other apps to transition in 2014 Application Enterprise Service Bus Application Enterprise Service Bus Provisioning, deployment, routing Management Tools Secure Framework Monitoring, Audit log, Security Data Enterprise Service Bus (ESB) (Business Logic, Monitoring, Access Control, Audit Logging) Collision Offender and other third party data Additional data sources will be added in 2014 Offender 47

48. Key Learnings • Organization • Re-learn • Find evangelists – give them space • Approach • Experiment • Don’t analyze too much • Educate and engage your business • Find like minded businesses and share notes • Re-think where you get input

49. Questions?

50. For more information please contact us 502-425-8425 -or- Sales@ vsoftconsulting.com Thank you for attending.