Embed Size (px)
Citation preview
1
Evolving Applications of Alternative Data SetsApril 2016
2
Thesis
1Recent 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
2In 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
3In 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
4In 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
5Startups 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
Advancements in Data Collection and AnalysisSmartphones, 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 DataSocial 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
Advancements in Data Collection and Analysis
Source: Frost & Sullivan, Cisco, Wikibon
Global Big Data Market
2011 2013 2015 2017 2019
7.6
19.6
33.31
43.4
55.2Billions of USD
Data AnalysisBig 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
2011 2013 2015 2017 2019
20.032.8
72.4
109.0
168.0Exabytes 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
Significance of Alternative Data SetsIndustry 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
InsuranceEvaluation 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 LendingIdentity 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
Emerging Uses of Alternative Data SetsIndustry 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
InsuranceMetroMile 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 ChainSight 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 AnalyticsClarabridge 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 LendingEarnest 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
Innovative Applications of Collected Data
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
TrustingsocialUndisclosed 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
Applications of Previously Unobserved Data
Company____________________________
Funding____________________________
Business Focus_______________________________________
Innovative Use of Data__________________________________________________________
ProducePayUndisclosed 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 InsurancePay-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
Alternative Datasets in Insurance, Lending, and Jobs & Hiring
10
Lending – Simplified Process Map Key data buckets and metrics in the current lending landscape
Business or Individual Seeks Traditional LoanTraditional Credit Analysis• Credit score based on
past spending and borrowing habits
• More comprehensive reporting expectations for businesses’ financial data
Bank or Other Lending InstitutionAnalyzes 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’ LoansAnalyzes 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
Lending – New DatasetsDescription 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
Insurance – Simplified Process Map
Property & Casualty ApplicantProperty-Linked Data• Age, Location• Property Condition
Survey• Owner RecordsDriver-Linked Data• Insurance records• Make and model of car• Primary car use
reasons
Property & Casualty InsurerCollects 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
Insurance – New DatasetsDescription 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
Insurance – New Datasets cont’dDescription 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
Jobs & Hiring – Simplified Process Map
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
Jobs & Hiring – New DatasetsDescription 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
Case Studies
18
Case Study: SoFi and EvenBackground
Location & HQ San Francisco, CA
Funding $1.37B in 6 Rounds from 19 Investors
Investors
Business DescriptionLeading 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 DescriptionAutomatically manages your personal bank account by making interest-free loans when pay is below average and savings when pay is above averageAlternative 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
19
Case Study: ProducePay & MightyBackground
Location & HQ Glendale, CA
Funding Undisclosed amount: 2 rounds, 7 investors
Investors
Business DescriptionProvides 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 DescriptionOnline 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
20
Case Study: Square & MetromileBackground
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 DescriptionInsures 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 portAlternative 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 DescriptionOffers 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 their POS devices used to develop inventory and sales management software
• P2P electronic loan service Square Cash, and short-term business loan service Square Capital using propriety database to manage 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
Who Will Win?
22
Incumbents vs. StartupsDiscussion
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 competitiveNetwork 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 datasetsPrivate 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
Key Determinants of Success - Startups
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
Key Determinants of Success - Incumbents
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
New Entrants
26
New EntrantsDescription 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, CAFounded in 2014CEO: 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, CAStill in stealth modeCEO: 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, MAFounded in 2013CEO: 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, CAFounded in 2012CEO: Taylor Brown
27
New EntrantsDescription 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, CAFounded in 2011Acquired in 2016By 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, CAFounded 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, CAFounded in 2012CEO: 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, CAFounded in 2012CEO: Sushil Thomas
28
New EntrantsDescription 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, CAFounded in 2013CEO: 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, CAFounded in 2011CEO: 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, CAFounded in 2013CEO: 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, CAFounded in 2014CEO: Jay Kreps
Ali Hamed | [email protected] | 818 307 7964 | @AliBHamedDrew Aldrich | [email protected] | 914 262 6688
| @DrewKAldrichAshin Shah | [email protected] | 607 379 2937
Reid Williamson | [email protected] | 508 733 6749
