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Data deck - CV - AXA - CVC

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Identifying data points that materially improve underwriting

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Data deck - CV - AXA - CVC

  1. 1. 1 Evolving Applications of Alternative Data Sets April 2016
  2. 2. Thesis 2 1 Recent software & hardware advancements have made large datasets easier to collect and analyze; firms are finding new datasets and new ways to apply insights learned, especially in the insurance, lending, and hiring sectors 2 In lending, creditors can better understand applicant risks by analyzing non-traditional datasets and use this information to target unrepresented potential borrowers, or to reduce interest rates charged existing borrowers 3 In insurance, new data allows insurers to better understand the people or property being insured, enabling better risk management (such as improved preventative healthcare) and more efficient pricing of insurance products 4 In jobs & hiring, alternative datasets give employers valuable insights about an applicant using behavioral and social information, as opposed to relying on static, structured indicators of past job and school performance 5 Startups can succeed in niche segments by building scalable products that rely on utilizing previously unused or unobserved datasets; incumbents need to leverage their already large customer bases to collect new data while preventing customer attrition
  3. 3. Advancements in Data Collection and Analysis 3 Smartphones, Wearables and Internet-of-Things (IoT) Smartphones and Wearables • Location data can be collected in real-time by smartphones or automobiles as well as through POS systems and APIs provided by credit card networks (eg: Mastercard’s Locations API) • This can help businesses provide relevant services by understanding the locations a customer frequents • Medical and fitness data is continually recorded through motion and health sensors built into devices • Doctors can monitor health markers like heart rate in real time as opposed to traditional static readings • Insurance companies can dynamically adjust pricing and better understand their liabilities using this data Internet-of-Things (IoT) • Enterprise IoT sensors on machinery and other equipment can help manufacturing companies critically examine their supply chain from end-to-end and lower their costs • Consumer IoT devices such as smart cars, thermostats and motion sensors collect time and location data regarding sleep, movement, work and activity among other everyday tasks • This data can provide businesses such as e-commerce companies and advertisers a more complete picture of the lifestyle, habits and preferences of an individual • Businesses can use this data for better targeted advertising, dynamic pricing and promotions based on variability in an individual consumer’s preferences and demand over time-of-day or over longer periods Social Data Social Data of Individuals • Advancements in text, speech and image analytics using natural language processing and artificial intelligence provide businesses with several tools to analyze social media data • This can give businesses unique insights about one’s activities and personality, which is especially significant for recent graduates and lower-income individuals whose data has not been collected significantly through traditional channels • Examples: • Alternative lenders can evaluate credit risk by analyzing one’s social media activity and immediate social network as well as by using social finance apps like Venmo to get a non-traditional view into a user’s expenditures • Life and Health Insurance companies can use social data to adjust pricing based on one’s lifestyle and food habits Social Data of Businesses • Social data is also gaining prominence as a barometer for general sentiment surrounding businesses • Key data sources include number of social media followers of a company, online posts of customers as well as employees about the company and direct online interactions with customers • This data can be analyzed to obtain insights into employee and customer satisfaction of a company and can potentially be used to evaluate it’s financial stability and the price of it’s equity • Example: Buffalo Wild Wings’ Q3’15 decline in profitability was closely matched by a decline in tweets related to the company
  4. 4. Advancements in Data Collection and Analysis Source: Frost & Sullivan, Cisco, Wikibon 4 Global Big Data Market 7.6 19.6 33.31 43.4 55.2 2011 2013 2015 2017 2019 Billions of USD Data Analysis Big Data Analytics • Modern Big Data software apply data sets and application functions on many different machines, which accomplish the task in parallel, reducing inefficiencies and calculation time • Recognition of patterns within the abundance of data collected, often using machine learning algorithms, is key to making the data actionable for businesses • Example: Treato, a social health startup, utilizes machine learning to identify drug side-effects and prescription patterns using data from social networks and patient health forums Examples of Powerful Big Data Software • Apache Hadoop – Software using parallel data execution frameworks to process persisted big data sets • Apache Spark – Similar to Apache Hadoop but processes data within memory itself to reduce latencies • Apache Storm - Used for analysis/filtering on streamed data (rather than simply persisted datasets) • HPCC Systems – Parallel-processing computing platform that is flexible for cloud support • Grid Gains – Software that is specialized for transactional and analytical processing (which are the main uses of Big Data) • Mesosphere DCOS – Software that consolidates resources across a distributed system for physical and virtual applications • Concord.IO – Used for real-time data procesing like Apache Storm but provides added speed improvements Global Data Traffic 20.0 32.8 72.4 109.0 168.0 2011 2013 2015 2017 2019 Exabytes of Data Global data traffic has doubled in the last two years alone and is forecast to double again by 2019 With rising demand for data analytics, the global big data market is expected to surpass $50B by 2019
  5. 5. Significance of Alternative Data Sets 5 Industry Application ________________________________ Major Tasks Requiring Data ______________________________________________________________ Traditional and Alternative Data Sets ______________________________________________________________ Employee Evaluation, Compensation and Hiring Employee Performance Evaluation, Evaluation and Hiring of Job Applicants, Wage Determination Performance Data, Sales Data, Employee Survey Data, Social Media Data, Wage, Attrition & Revenue Analytics Insurance Evaluation of Financial Status of Applicant, Calculation of Probability of Claims, Matching Timing of Assets and Liabilities Social Media Data, Medical Records, Wearable Device Data, Auto Records and Driver Tracking Data Supply Chain Planning and Scheduling, Purchase and Inventory Optimization, Demand Responsiveness Real-time Inventory and Supplies Data, IoT Sensor Data from Machinery and other Moving Equipment Text Analytics Customer Relationship Management, Competitive Business Intelligence, Brand Reputation Awareness Customer Survey Data, Social Media Data for Individuals and Businesses Alternative Lending Identity Verification, Evaluation of Credit Risk, Determination of Ideal Lending Structure and Terms for Specific Borrowers Social Media Data, Earnings & Spending Data, Personal Background Data, Expected Career Path Information
  6. 6. Emerging Uses of Alternative Data Sets 6 Industry Application ________________________________ Example Use Cases _____________________________________________________________ Emerging/Potential Use Cases ________________________________________________________________ Employee Evaluation, Compensation and Hiring Visier utilizes a cloud-based platform to aggregate employee data and provide predictive analytics on issues such as employee attrition Speech and image recognition to analyze qualitative metrics such as confidence, tone of voice, posture, and body language can help companies automate parts of the hiring process to reduce costs Insurance MetroMile uses in-car hardware to monitor driving habits and evaluate the safety of its policyholders. Premiums are adjusted based on driver performance and charged per mile driven Health insurers can use data from wearables, sleep data, and mobile data to get a more complete understanding of a policyholder’s lifestyle and better understand the timing of its claims Supply Chain Sight Machine has developed tools specifically designed to aggregate and analyze data generated by factory sensors, machines, cameras, PLCs, and robots Manufacturing equipment can be equipped with sensors providing feedback on the quality of its own operation as well as the employee managing it, to optimize task allocation and performance Text Analytics Clarabridge uses machine learning and natural language processing to aggregate and analyze customer responses from surveys to better help businesses process and utilize feedback Text analytics can be used to evaluate the content of social media posts, which has uses in insurance, lending, employee evaluation & hiring and several other areas Alternative Lending Earnest and SoFi use data to evaluate career prospects, earnings and savings history to evaluate lenders. Trustingsocial focuses on social data to determine rates in emerging markets Lenders can utilize social media and location data to learn the spending locations and habits of consumers to better evaluate credit risk based on expenditure estimates
  7. 7. Innovative Applications of Collected Data 7 Company ____________________________ Funding ____________________________ Business Focus _______________________________________ Innovative Use of Data __________________________________________________________ Earnest $24.1 million Alternative Lending Evaluates credit risk using savings habits, educational background, and career path in addition to financial history and income SoFi $1.8 billion Alternative Lending Sets interest rates based on future earnings evaluated using career experience, monthly income vs. expenses, education Trustingsocial Undisclosed Alternative Lending Evaluates consumer credit risk in emerging markets by analyzing social, web, and mobile data using machine learning CloverHealth $100 million Health Insurance Health insurer focused on analyzing patient data to optimize preventative care measures, increasing health outcomes and profitability Affirm $320 million Online Purchase Financing Instant credit for online purchases, with interest rates based on traditional metrics as well as social media data
  8. 8. Applications of Previously Unobserved Data 8 Company ____________________________ Funding ____________________________ Business Focus _______________________________________ Innovative Use of Data __________________________________________________________ ProducePay Undisclosed Agricultural Lending Collects and utilizes agricultural inventory data to provide next-day loans to farmers, using the produce that they ship as collateral PlaceIQ $27.0 million Location Data Service Uses location-tracking data to help companies obtain a spatial understanding of the digital activity of consumers MetroMile $14.0 million Automobile Insurance Pay-per-mile car insurance with pricing determined using an in-car device to track driver habits and safety Feedzai $26.1 million Fraud Detection Uses Machine Learning and Behavioral Analysis of consumer purchasing data to identify potentially fraudulent transactions DataWallet $320 million Online Marketplace for Data Helps better match the specific data needs of companies by compensating consumers for sharing their data
  9. 9. Alternative Datasets in Insurance, Lending, and Jobs & Hiring
  10. 10. Lending – Simplified Process Map Key data buckets and metrics in the current lending landscape 10 Business or Individual Seeks Traditional Loan Traditional Credit Analysis • Credit score based on past spending and borrowing habits • More comprehensive reporting expectations for businesses’ financial data Bank or Other Lending Institution Analyzes Creditworthiness • Historical spending and income data used to extrapolate future ability to make contractual payments for individuals and businesses Individual Seeks ‘Tech’ Loan Aggregates Credit Data • Existing tech-enabled lending platforms request a variety of financial, career-related, and personal data • Data in application, minimal monitoring Individual Lender or Market for ‘Tech’ Loans Analyzes Creditworthiness • Individual or platform providing loan assesses provided data • In many cases, personal data used to verify creditworthiness Feedback Platform Performance History • Some tech-enabled lending platforms provide historical data about loan performance based on their assigned ratings Feedback Write-Offs Drive Refinement • Feedback about a lender’s credit analysis model is based on past losses • Little analysis beyond changes in reported financials
  11. 11. Lending – New Datasets 11 Description Source of Data Merits Challenges Social media connectivity and popularity Social networks are used to hold individuals accountable to others and judge the responsibility of a potential borrower - those with creditworthy friends may be more creditworthy Social media data from sites like Facebook, Twitter, Instagram, and others Publicly available data is easy to access and analyze May be seen as invasive of personal privacy; inferences could be misleading Smartphone usage and location data Devices are used to analyze and track leisure habits and spending by location and product category which could help determine a borrower’s expenditures and thus, creditworthiness Smartphones, GPS devices, Credit Card spending data Increasing popularity of smartphones and functionality makes data accessible Developing usable model based on location and leisure data is challenging; could also be regulatory challenges Social media and employment data A better understanding of how individuals are linked socially as well as professionally could introduce opportunities to link people in a network for loans and potential partnerships Cross-referencing social connectivity data from social media sites and employment data Introduces social aspect to business lending; socializes, strengthens the incentive to repay Regulatory concerns; desire to separate professional and social lives Online data about a region’s economic activity and cost of living Social media indicators of regional employment, population, and cost of living in a region provide immediate indicators of job security and expenditures of borrowers in region Social employment data, social media text analytics, credit card companies to determine macro indicators Information is easily accessible and provides more immediate regional view Data may not be very in-depth and there are no required reporting standards
  12. 12. Insurance – Simplified Process Map 12 Property & Casualty Applicant Property-Linked Data • Age, Location • Property Condition Survey • Owner Records Driver-Linked Data • Insurance records • Make and model of car • Primary car use reasons Property & Casualty Insurer Collects Property/Driver- Linked Data • Historical data used to set pricing for premiums • Minimal thresholds determine eligibility for insurance coverage Life Insurance Applicant RX Lookups, Personal Health through Fluids Testing • Disjointed data from mix of self-reported and poorly organized health records • Timely reporting process involving significant patient input and effort Life Insurer Analyzes Prescription Data • Algorithms based on historical data used to set premiums • Regulations greatly restrict the type & amount of pricing discrepancies Feedback Static, Regulated Feedback • Prescription data is only updated when there is a recorded visit • No optimization of (or immediate feedback on) lifestyle choices Feedback Data is Mostly Static • Pricing is adjusted only in the case of an event/accident • Adjustments made only after a reported incident, lag between dangerous behavior and adjustment Key data buckets and metrics in the current insurance landscape
  13. 13. Insurance – New Datasets 13 Description Source of Data Merits Challenges Social media and text-based analytics data Text-based analytics of content such as social media posts helps insurers determine riskiness, aggression, or other factors that could affect insurability Social media websites and applications Assess underlying riskiness and aggressiveness of all types of policyholders Invasive into applicants privacy and may produce In-vehicle real- time location and performance data Real-time location and performance data allows for more precise pricing based on specific driver behaviors and travel through especially dangerous areas or road sections OBD-II sensors and eventually manufacturer- installed native vehicle devices Real-time data, geographic overlays allow for precise risk adjustments Manufacturer- installed devices reduce user input needed but raise privacy concerns Quantified self data about biological factors Data from wearable devices or smart appliances, purchase histories provide feedback about lifestyles and allow insurers to better understand their liability pools using predictive analytics Wearable devices, IOT sensor- equipped devices (smart beds, etc.), financial records Real-time data can help policyholders better understand lifestyle choices and adjust pricing Regulators and users may not be comfortable sharing and using personal data Smart pills and medicinal intake data Information about drug intake allows insurers to reward patients for sticking with prescribed medical regimens and alert care providers when patients deviate from these Sensor-equipped drug delivery units, smart pill boxes that track intake Minimally intrusive monitoring allows insurers to reward those who stick to medicine regiments Synchronizing insurers with prescription and device data; data use requires explicit user consent
  14. 14. Insurance – New Datasets cont’d 14 Description Source of Data Merits Challenges Active or passive monitoring of property and environment Data collected from sources such as drones, satellite imaging, and weather probes could provide immediate feedback about the status or risks of insured properties Camera-equipped drones, imaging satellites, weather satellites and probes Real-time updates of property risks and analysis of potential losses Active monitoring with drones or video may be seen as overly intrusive Purchases and receipt history Data about previous purchases from credit card receipts could be used to validate claims for lost property and the value of those claims Credit card or mobile payment histories and receipts Easily verifiable data with specific pricing data Must coordinate with transaction service companies, consumer privacy
  15. 15. Jobs & Hiring – Simplified Process Map 15 Internal Job Applicant Employee Data • Sales record • Client relationships • Past performance evaluations • Reputation amongst colleagues Hiring Manager Makes Decision Based on Proprietary Data • Employee data is analyzed to see if he/she is fit for promotion • Proprietary data allows for more in-depth knowledge of applicant External Job Applicant Personal Health Data • Resume • Referrals • Body language during in- person interview • Performance on an assessment (If given) Hiring Manager Makes Decision Based on External Data • Must predict applicant’s aptitude based solely on external data • Riskier since applicant has not worked there prior Feedback Inherently Static • Resumes can be out-of- date by the time applicant is interviewed • Referrals only glimpse into historic performance, may not predict future performance Feedback Updated Regularly • Employee metrics are often updated on fixed schedules, eg quarterly sales numbers, mid-year evaluations • Some of this data is subjective Key data buckets and metrics in the current jobs & hiring landscape
  16. 16. Jobs & Hiring – New Datasets 16 Description Source of Data Merits Challenges Social media and text-based analytics data Text-based analytics of content such as social media posts allows employers to determine personality of the applicant and whether it is suited for the job Social media websites and applications Assess the personality of applicants and determine fit Data quality varies significantly by user Smartphone productivity data Smartphone data related to time spent on different apps coupled with general organization patterns helps determine if an applicant will transfer these skills or lack thereof to the job Smartphone and specific app usage data Ties into the key functions of many employees Would be considered an invasion of privacy without permission Algorithmic Jobs Tests Pre-employment job tests that select candidates algorithmically based on their responses have been shown by NBER to result in hires that stay with the company longer and are more productive Generated by the job applicant when they fill out the pre- employment test More accurate than humans in predicting future tenure and productivity of employees “Algorithmic aversion” (trusting human instincts over computers) Body language and Voice Cameras help recognize nuances in both body movements as well as vocal inflection, picking up on subtle cues of the limbic system that are more honest than the words spoken by the applicant Camera (via applicant’s computer or placed at the site of interview) and software to analyze the audio/video Data will reveal a lot about applicant in a standardized fashion Candidates need to be comfortable with being recorded, requires specific technology
  17. 17. Case Studies
  18. 18. Case Study: SoFi and Even Background Location & HQ San Francisco, CA Funding $1.37B in 6 Rounds from 19 Investors Investors Business Description Leading online lender and the #1 provider of student loan refinancing with over $7 billion lent to date Alternative Pricing Data Application • Uses non-traditional information including education and employer data to look at ‘where you are today’ and ‘where you’re headed’ and potentially offer lower rates to students • Offering more products to existing customers instead of widening customer base by loosening credit standards decreases acquisition costs & provides SoFi a reliable history of repayment data on borrowers Background Location & HQ Oakland, CA Funding $1.5M in 1 Round from 13 Investors Investors Business Description Automatically manages your personal bank account by making interest-free loans when pay is below average and savings when pay is above average Alternative Pricing Data Application • Analyzes bank deposits to determine average paycheck over the past 6 months • Algorithm treats more recent paychecks with greater weight and analyzes expenses to determine weekly required income • Spending and income risk analysis allows Even to make short-term interest-free loans to make up for lower weekly paychecks Established student loan refinancer Predictive data: less risky student loans, allows for lower interest student financing Early-stage startup with many backers Income & spending data: low-risk interest- free loans to smooth personal income 18
  19. 19. Case Study: ProducePay & Mighty Background Location & HQ Glendale, CA Funding Undisclosed amount: 2 rounds, 7 investors Investors Business Description Provides inventory management and cash flow solutions to farmers allowing them to receive credit soon after shipment Alternative Pricing Data Application • Provides an online inventory management platform to buyers and sellers of produce that allows ProducePay to track farming, production, location and inventory data • ProducePay uses this platform to track when the produce of a non-US farmer reaches the US and thus arbitrages credit risk by lending to non-US farmers against their US assets (the US-based produce) Mighty Background Location & HQ New York, NY Funding $5.25 million Series A Investors Business Description Online marketplace that enables plaintiffs to access portion of future settlement to alleviate legal costs Alternative Pricing Data Application • Analyzes historical financial performance, credit ratings, attorney’s peer review rankings, and firm performance • Provides enhanced perspective of an applicant and potential settlement to reduce financing risk • Allows plaintiffs to bring better-funded cases against defendants, utilizing potential settlement gains immediately Early stage agricultural finance startup Early stage legal finance startup Production and consumption data helps de-risk international agricultural financing Analysis of legal data allows for lower risk, lower interest litigation financing 19
  20. 20. Case Study: Square & Metromile Background Location & HQ San Francisco, CA Funding Public company NYSE:SQ Investors Background Location & HQ San Francisco, CA Funding $14M in 2 rounds from 5 investors Investors Business Description Insures vehicles by charging a base rate premium plus a per-mile charge and monitors vehicle health and local driving hazards using vehicle’s OBD-II port Alternative Pricing Data Application • Per-mile insurance plans are a new way of pricing auto insurance, allowing drivers who use their vehicles less to save dramatically • Monitoring services allow Metromile to help keep drivers safe and reduce policy outlays • As cars are used less and shared more, flexible pricing options like that offered by Metromile become more important Business Description Offers full POS hard/software capable of credit transactions and inventory accounting with expansion into cash transaction services Alternative Pricing Data Application • Proprietary database of transaction volume from from their POS devices used to develop inventory and sales management software • P2P electronic loan service Square Cash, and and short-term business loan service Square Capital using propriety database to manage risk risk • Charges a percentage of amount transacted across all services and products offered Public transaction services company Early stage auto-insurance company Proprietary transaction database reduces risk of making short-term business loans Per-mile plans and vehicle monitoring make insurance flexible and preventative 20
  21. 21. Who Will Win?
  22. 22. Incumbents vs. Startups 22 Discussion Target Markets • Incumbents may be less concerned with new startups and more concerned with existing competitors adopting new technologies • Startups will tend to target new consumers or specific niches of bigger industries • Competitive landscapes may be able to support both incumbents and startups if there isn’t much direct competition • However, consolidation through mergers and startup acquisitions may make the industry competitive Network Effects • Incumbents can leverage large existing customer bases • Startups can develop new product features with explicit goal of achieving network effects, perhaps by trying to ‘own’ the customer by providing several additional services • Networked markets demand high invested capital and create winner-takes-all marketplace Ease of Integration • Incumbent’s customers may be unwilling to re-define how they engage with company • Startups can explicitly develop products to ease data collection and customer use and appeal to the millennial generation • Ease of collection critical for generating robust, unbiased datasets Private Data Security • Incumbents already trusted with personal data and many have established security systems • Startups may struggle with high fixed costs to implement security measures • Crucial for brand image to be associated with data security
  23. 23. Key Determinants of Success - Startups 23 Description Merits Challenges Novel Data Must utilize data that was either previously unobservable and is valuable in analysis or data that was previously observable and valuable, but unused Utilizing new datasets can provide more accurate risk measurement, that can translate to lower rates for customers Identifying useful data is difficult and it is costly to develop analysis tools with new insights Customer Ownership Providing additional services, creating high switching costs will help startups retain customers and fully utilize customer acquisition expenses Retaining customers builds large network of data, optimizes acquisition costs Building additional products costly, switching costs reduce customer satisfaction Competitive Pricing Capability Startups can leverage new datasets to provide similar services to incumbents at reduced rates Startups can capture market share from incumbents through lower pricing If replicable, creates race to the bottom and continually decreasing prices over time
  24. 24. Key Determinants of Success - Incumbents 24 Description Merits Challenges Switching Costs Incumbents with a large customer bases may find it more economical to develop switching costs than to develop or acquire a products to compete with new entrants More economical than developing or acquiring new product or service Reduces customer satisfaction, fewer customer acquisitions than new products Internal R&D Capabilities & Cost Ability to integrate new datasets with existing products & customers reduces development and integration risks associated with M&A Using existing resources requires less capital investment Internal development may not necessarily succeed, opportunity cost of not spending more on existing segments of the business Acquisitions Purchasing other companies is an easy and popular way for incumbents to achieve novel data gathering and analysis capabilities Foregoes the risk of experimental internal development not succeeding Expensive, integration issues, regulatory hurdles
  25. 25. New Entrants
  26. 26. New Entrants 26 Description Funding Background Blue Shift Re-imagining how businesses engage users to make them frequent customers, automating segment-of-one marketing Raised $10.6M in 2 rounds from 4 investors backed by NEA, Nexus, Great Oaks Silicon Valley, CA Founded in 2014 CEO: Mehul Shah Node.io Using online data to understand relationships between people, companies, and keywords Raised $8.3M in 2 rounds, investors include NEA, Avalon, Canaan Partners San Francisco, CA Still in stealth mode CEO: Falon Fatimi Tamr Enterprise data unification software that integrates data for business analytics Raised $41.2M in 4 rounds from 7 investors backed by Google Ventures and NEA Cambridge, MA Founded in 2013 CEO: Andy Palmer FiveTran Zero-configuration data integration: data connector for extracting value from diverse cloud & database sources and loading it into Amazon Redshift data warehouse Raised an undisclosed amount in 2 rounds from 2 investors from Y Combinator San Francisco, CA Founded in 2012 CEO: Taylor Brown
  27. 27. New Entrants 27 Description Funding Background DataHero Cloud-based service collects data from disparate sources and presents an easy-to- use dashboard for professionals with a range of backgrounds and expertise Raised $10.3M in 3 rounds from 7 investors backed by Foundry Group San Francisco, CA Founded in 2011 Acquired in 2016 By Cloudability Kyvos Insights Developed online analytical processing software for interactive, multidimensional analysis on structured and unstructured Hadoop data Raised undisclosed amount from undisclosed investors San Jose, CA Founded in 2012, exited stealth mode in June, 2015 ThoughtSpot Providing users with access to range of data analytics using simple search interface Raised $40.7M in 2 rounds from 6 investors backed by Lightspeed, Khosla Palo Alto, CA Founded in 2012 CEO: Ajeet Singh Arcadia Data Visual analytics software that overcomes traditional challenges with Hadoop data by using Hadoop as operating system Raisd $11.5M in 1 round form 3 investors backed by Intel, Mayfield, and Blumberg San Mateo, CA Founded in 2012 CEO: Sushil Thomas
  28. 28. New Entrants 28 Description Funding Background Interana Events-based software analyzes streaming data to understand customers and product usage Raised $28.2M in 2 rounds from 8 investors backed by Index, Battery Ventures Redwood City, CA Founded in 2013 CEO: Ann Johnson Looker Saas company providing embeddable analytics software that unifies data form multiple sources Raised $96M in 4 rounds from 6 investors backed by Kleiner Perkins, First Round Santa Cruz, CA Founded in 2011 CEO: Frank Bien AtScale Software allows commonly used business intelligence tools to access data in Hadoop clusters Raised $9M in 2 rounds from 4 investors backed by XSeed, UMC, Storm, AME Cloud San Mateo, CA Founded in 2013 CEO: Dave Mariani Confluent Technology and services to help companies adopt Apache Kafka, critical and highly scalable tool for analyzing high-volume streaming data Raised $30.9M in 2 rounds from 4 investors backed by Index, Benchmark Mountain View, CA Founded in 2014 CEO: Jay Kreps
  29. 29. Ali Hamed | ali@coventure.vc | 818 307 7964 | @AliBHamed Drew Aldrich | drew@axastrategicventures.com | 914 262 6688 | @DrewKAldrich Ashin Shah | ashin.shah@cornellvc.com | 607 379 2937 Reid Williamson | reid.williamson@cornellvc.com | 508 733 6749

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