Having it All is not Having it All at All!

1. Having it All is not Having it All at All! Problem Formulation in the Face of Overwhelming Quantities of Data

2. A journey of discovery… Where’s the fire? START FROM THE BEGINNING -- “Before the beginning of great brilliance, there must be Chaos.” -- (I Ching) “At the beginning of the 21th century, the population of the Earth [was] 6.300.000.000., who annually experience a reported 7,000,000 -8,000,000 fires with 70,000 –80,000 fire deaths and 500,000 –800,000 fire injuries. Dr. Ing. Peter Wagner 2006 ”

3. Data everywhere.

4. Who knew?

5. Gone are the days when there was a single source of “truth”… Harvard R.G. Dun Credit Report Collection Baker Library Entries in a book on Australia business owners Entry on Hannah Griffith, a milliner in Springfield, Ill. In 1869 “about to marry a fellow [of] no account.” An entry two years later noted with some relief, that that plan had fallen through. Entry about one J. B. Alford, who sold groceries and liquors: June 1870 “This man is said to be in thriving circumstances. He has some Real & personal estate & I think it is safe to trust him.” About a storekeeper in Halifax County, N.C. – June 1873: “purchaser or stolen goods, a great scamp.” "is not much of a businessman, but had some capital, it is said, advanced by his father, who is reputed well off“ -- About J.D. Rockefeller – who turned out to be a good credit risk; 1863 was the year he set up a refinery that blossomed into Standard Oil.

6. Hold on… things are changing.

7. Framing our case for change… The Operating Environment • We all know that the world is changing • We are aware that the rate of change is increasing at an unprecedented rate • We see new types of data, technologies, and behaviors every day • More and more, we are tasked with discerning the discoverable need from the articulated want The Case for Change • What has made us successful so far is insufficient • We now have the ability to succeed… or fail, much faster • The connectedness of information and the ways in which it is changing is impacting the risk and opportunity space in ways we are only beginning to understand

8. Sometimes, a picture is worth a thousand words. • The largest corpus of data preceded the event • Most data created about the event had significant, and asymmetric latency • The rate of “data decay” attributable to the participants in the event is significant Lately, a thousand pictures are taken in the time it takes to speak a single word! • What about the digital footprint of all of the smartphones? • What about the social networks the crowd? • What about the metadata in the photos? • What are the opportunity costs to other activities?

9. Asking the right question What if there were no hypothetical questions? What if the Hokey Pokey really is what it’s all about? How deep would the ocean be if sponges didn’t live there? How many more of these silly questions till the next slide?

10. Questions about risk and opportunity are at the heart of our focus. What about fraud? Should I extend credit? Which customers should I call on next What is the right credit limit? What do my best customers look like? Which prospects are most promising?

11. It is extremely important to frame the question in the right context.

12. The right universe of data is often implied by the scope and context of the question. Firmographic Foundational • Data in hand • Discoverable data • Computable data • Extent, unavailable data (opportunity cost) • Understanding of cause systems • Relevant theory Telephone Business Name SIC Employee Size Linkage Year Started Address Primary Contact Sales Revenue D&B Proprietary information

13. Leveraging the “V’s” to get to the best answer Velocity: Can the rate of change of data itself be part of the answer? Volume: How much data is “too much” to see the answer? Veracity: How do I adjudicate the truth when the malfeasants are learning so much faster? Variety: How can heterogeneous and unstructured data inform new ways of inquiry?

14. A good example can be seen in tracking mergers, acquisitions, and divestitures. A typical M&A takes 6-9 months from announcement to deal completion • Some take longer, or may never close • Regulatory requirements sometimes drive pre- and post- close changes over years Family trees updated as the deal completes • Average update within 10 days • Linkage updates frequently precede official registry changes • Updates include re-linking records, re-structuring tree levels, taking entities to out of business and creating new entities Announced restructuring and re-organizations often take 6 months to 2 years 14

15. Traditional analysis of this data can reveal interesting risks National Government: Republic of Venezuela 3 additional subsidiary levels Propernyn B.V. Netherlands PDV AMERICA, INC Oklahoma, USA CITGO PETROLEUM CORPORATION Texas, USA

16. Combining the articulated want (family tree) with the discoverable need (what’s really going on)… The story is true. The names have been changed to protect the innocent.. Monsanto 500 member family tree Largest Genetically modified food producer Mediquip 1000 Employees 49% 30% AdvDesigns AG 30 Employees R&D Stem Cell Rsrch Frankfurt, Germany Medi-Cell 125 Employees Lab Equip Mfr. Abayance, FL Ceramics Inc 50 Employees Glass Mfr Wichita, Kansas Pending Decision: Underwrite Directors and Officers Policy

17. Language, identity, and intention can significantly impact the complexity of the situation. Kawasaki (idiom)- “river beside mountainous terrain” “Ka-wa-sa-ki” 川崎重工咨询 “Chuanxi Zhonggong zuishin” (aka Kawasaki Heavy Industries Consulting) 株式会社カワサキモータースジャパン “Kabushikigaisha Kawasaki Mōtāsu Jyapan ” (aka Kawasaki Motors Japan) 川崎重工業株式会社 “Kawasaki Jūkōgyō Kabushiki-gaisha” (aka Kawasaki Heavy Industries) 川崎涂料有限公司 “Chuanxi chuliao Youxian Gonxi” (aka Kawasaki Paint Co, Dongguan) 한국가와사키 “Hanguggawasaki” (aka Kawasaki Korea) KAWASAKI KK (Local electricians in a suburb of Kawasaki) D&B Proprietary information

18. People are strange… Digital natives vs. digital immigrants Multiple names Privacy and other statutory constraint Overlapping “identities”

19. As the boundary between people and small business becomes increasingly blurred, we continue to focus on the concept of People In The Context of Business Cleanse, de-dupe, identity resolution and enrichment services for your contact data Understand when people move from organization to organization Sharpen the line between the individual and the business when engaging small businesses Malfeasance and fraud are perpetrated by people, not by businesses. This solution reveals relationships that will help all of us more effectively identify potential for bad behavior. THE CHALLENGE THE GOAL THE VALUE #1 – the “John Smith” problem – multiple people with the same name #2 – the “Ann Taylor” problem – data about businesses named after people Many people connected to one business Many businesses connected to one person #3 – the “Sybil” problem – one person with multiple persona or names Businesses connected through people A single view of customers and prospects, both in the context of entities and people will drive key actionable outcomes for your business. Caroline M Smith 302 N Liberty St. Albion, IA Addr Type: Residential Caroline Smith University of Iowa 21 E Market St. Iowa City, IA Addr. Type: Commercial People connected through associations with other people Carrie Smith Meredith Corporation 1716 Locust St. Des Moines, IA Addr. Type: Commercial Carrie Smith Tenderheart Daycare 2635 Cleveland Dr. Adel, IA Addr. Type: Commercial D&B Proprietary information

20. Creating the foundation for People in the Context of Business. D&B Proprietary information

21. Predictions, predictions… I’ll bet you knew this was coming Learning from the way things move, even if you don’t understand them fully… seriously? How do you predict something that has no precedent?

22. High Predictive Content Traditional Business Data Non- Traditional Insight Low Robust Predictive Data Available Limited Data Available No Data Available Commercial signal and proxy are now added to existing predictive attributes to provide deeper insights and even more predictive analytics. Signal & proxy sources add significant decisioning content on small businesses with limited or no traditional predictive data footprint

23. ‘Signals’ aggregated and analyzed over time, correlated with other data sources expose hard-to-find patterns. BIG DISPARATE SOURCES OF DATA Customer Cross-border Inquiries Global Trade Experiences SIGNAL EXTRACTION ADVANCED ANALYTICS PREDICTIVE MODEL GAINS Call Center Activity Customer Match Inquiries Other Proprietary Sources Customer Portfolio Monitoring Third Party Exchange Phone and Email Connectivity Testing Transactional WorldBase Updates We’re harnessing the massive flow of data through our systems and distilling the signals that describe a company’s behavior. This is helping to increase levels of precision in predictive models. Intelligence Engine Traffic D&B Proprietary information

24. Extending the deployed capability to better understand malfeasance… Data Collection & Input • Apply learning and integrate new targeted severe risk prevention and detection rules in data supply processes and platforms Continuous Improvement D&B Proprietary information

25. Combining people, linkage, and daily signals to quickly recognize and analyze patterns and take action… “Ring Leaders” In the above use-case, with millions of payment experiences a week, we were able to quickly identify and analyze a suspicious pattern and take action Not only on all related cases but also the “three ring leaders” D&B Proprietary information

26. Data sensing: Advanced analytics also play a significant role in acquiring new data sources. Other Data Shipment Data Multi-national footprint? Labor Mkt Data Sentiment Data Comprehensive coverage across all verticals and sizes of business? Scale Gov’t Data Depth Merchant Data Positive correlation with trade or other predictors to serve as a proxy? Value Utility Data

27. Some current efforts under way to utilized this hybrid capability… D&B Proprietary information

28. We are increasingly faced with information that is rich, varied, and replete with opportunity – our focus is shifting from “hunting and gathering” to new challenges. New Techniques to address Big Data New approaches to Discovery, Curation, and Synthesis Data sensing at the “Event Horizon”

29. “And now we welcome the new year, full of things that have never been” – Rainer Maria Rilke