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Big Data, Big Investment


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GGV Capital Managing Partner Glenn Solomon presented this overview of Big Data and its investment opportunities at
DataWeek, September 29, 2015.

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Big Data, Big Investment

  1. Integrate 2015: Big Data: Big Investment Opportunities September 2015 Glenn Solomon Managing Partner, GGV Capital
  2. Table of Contents Why Has Big Data Become a Big Target for VC How We Look at the Big Data Marketplace Where are VCs Investing How is Big Data Being Used in Companies Where Do We See the Opportunities Big Data Risks and Opportunities
  3. Why Has Big Data Become a Big Target for VC
  4. Why Has Big Data Become a Big Target for VC Source: Domo, “Data Never Sleeps 3.0” We are surrounded by a wealth of data we create from our everyday activities
  5. Why Has Big Data Become a Big Target for VC Source: BI Intelligence Explosion of IoT Mobile Devices By 2016, IoT > Mobile + PC combined IoT devices are generating billions of gigabytes of data everyday
  6. Big Data is Driving Value for Organizations across Different Areas Source: Gartner 2014 Increasing Number of Organizations are Investing in Big Data Across Different Areas, >50% of Respondents See the Value of Big Data
  7. How We Look at the Big Data Marketplace
  8. How We Look at the Big Data Marketplace Storing Data Making Decisions with Data Analyzing Data Vertical Market Uses of Data Sales & Marketing Security Finance Advertising Healthcare Retail & Supply Chain
  9. Where are VCs Investing
  10. Most Established Companies and Cumulative Funding Source: Crunchbase, Company websites $1.6Bn $1Bn $311M $248M $215M $207M $190M $174M $0 $300 $600 $900 $1,200 $1,500 $1,800 Palantir Cloudera MongoDB Hortonworks New Relic AppDynamics DataStax MapR
  11. Source: Crunchbase, Company websites $484M $259M $201M $180M $176M $155M $129M $129M $129M $121M $118M $106M $101M $0 $100 $200 $300 $400 $500 “Up and Comers” and Cumulative Funding
  12. How is Big Data Being Used in Companies
  13. How is Big Data Being Used in Companies With huge user base and long hours of streaming, Netflix can collect data from everyone on viewing patterns: • Time spent on shows / movies, and location • Types of entertainment (documentary, comedy, drama, horror, etc.) • Favorite starring cast • When users stop watching a show, etc. Using Big Data to Create a Show People Will Like 62.3 million total Netflix users people watch Netflix for ~ 90 minutes per day Big Data Started in 1997 as a pay- per-rental via mail company Now has evolved into an on- demand streaming service with thousands of movies and TV shows • Engage users better on current shows • Recommend shows they may like • And create a show people will like! Directed by David Fincher Featuring Kevin Spacey With Big Data, 70% of Netflix original shows are renewed for second season, compared to 30% from traditional TV series Source: Company website
  14. How is Big Data Being Used in Companies (cont’d) Source: Company website; “Data Jujitsu: The Art of Turning Data into Product” “People you may know” Asking a set of questions such as: • “what do you do” • “where do you live” • “where did you go to school” • using friend’s friend connections  More friendly faces up front to keep users engaged with the product Collecting Data from Users “Who’s viewed your profile” • By giving data back to users, LinkedIn creates a more engaging experience for users • Brings more revenue and make it more profitable for both users and company Feeding Data Back to Users
  15. How is Big Data Being Used in Companies (cont’d) Disney created a big data platform to store, process, analyze and visualize all data that is generated through the MyMagic+ system Disney MagicBands and MyMagic+ System Disney collects tons of valuable data through MagicBands and MyMagic+ system - a gigantic database that captures every move of the visitors of the park • Real-time location data • Purchase history • Information about the visitors • Entertainment ride patterns, etc. Insights from big data enables Disney to make smarter decisions: • Audience analysis & segmentation • Recommendation engine based on in-park traffic flow • Better and targeted marketing messages and offerings • And many more…The MagicBands (part of MyMagic+ System) are linked to credit card and function as a park entry pass as well as a room key Source: Company website
  16. How is Big Data Being Used in Companies (cont’d) Solution: The Black Book model, which analyzes up to 1 quintillion decision variables and combines various data sets such as: • satellite imagery • weather data • expected crop yields • acidity or sweetness rates • regional consumer preferences • 600 different flavors profiles of an orange Results: • Precise & dynamic formula on how to blend orange juice for consistent taste, down to pulp content, for the $2Bn orange juice business • After hurricane or freeze, this algorithm can re-plan the business in 5-10 minutes Problem: Inconsistencies in orange juice due to variations in orange crop, sourcing, and seasonality, etc. Goal: Consistently deliver optimal blend of orange juice, “despite the whims of Mother Nature” Orange Juice and the “Black Book Model” Source: Company website
  17. And Not Just Companies…Even Municipalities Benefit The use of big data has brought the following benefits: • Identify fire hazards based on algorithm • Reduce the number of fires • Fires are less severe as a result • Save on personnel and firefighting resources New York Fire Department has captured 60 different factors that could contribute to the likeliness of having a fire, such as: • Average neighborhood income • Age of the building • Whether it has electrical issues • Number and location of sprinklers • Presence of elevators • Each one of the city’s 330,000 buildings is ranked in order of the risk of fire • New York Fire Department uses the risk score to determine which buildings get inspected first Present • Inspections were almost random except for high-priority buildings like schools and libraries Past Big Data Source: Company website Risk Score
  18. Where Do We See the Opportunities
  19. Where Do We See the Opportunities • Targeting sophisticated data analysts on data-driven teams • Connects directly to databases • Fast, customizable visualization, easy collaboration, and superior SQL editing experience • Business management platform • Focuses on the needs of the decision-makers in a business, as opposed to existing data management procedures and policies • Connect, Prepare, Visualize, Engage and Optimize Tools for Data ScientistsTools for Business Executives Disclosure: GGV is an investor in Domo
  20. Where Do We See the Opportunities (cont’d) Readying Massive Data Intelligently • USM (Unified Security Management) that provides comprehensive, centralized and affordable security visibility • Combines log management and SIEM with other security features for complete security monitoring • Single platform, easy to use and deploy, perfect fit for mid-market enterprises Solving Big Problems – e.g. Security Disclosure: GGV is an investor in Alienvault • Curates massive variety of internal and external data • Reduces time and effort required for analytics and other applications critical for business growth • Leverages machine learning algorithms to identify data sources, understand the relationships between them, and connects siloed data
  21. Where Do We See the Opportunities (cont’d) Data Scientist as a ServiceNuanced and Unstructured Data -> Insights • Provides actionable insights, not more dashboard reports • Helps companies quickly understand what they need to do based on the data shown, so companies can spend less time analyzing and more time implementing • Highly trained on-demand team of Data Scientists backed by powerful tools • Captures and analyzes feedback from social media, blogs, forums, surveys, etc. to attain deep understanding of customer and marketplace feedback • Big data  big insights, helping companies understand how customers feel by deriving meaning from the most unstructured, unpredictable, and nuanced and subtlest context, so they can take action with maximum impact
  22. Big Data Risks and Opportunities
  23. Big Data, Big Risks and Even Bigger Opportunities • Trade-off between privacy / security and the benefits of wider pool of data • Behavioral data collected has more direct impact to the end consumers, and can lead to more sophisticated attacks on targeted consumers • As information becomes more readily accessible across sectors, it can threaten companies that have relied on proprietary data as a competitive asset • Companies that have benefited from information asymmetries are prone to disruption Information Asymmetries to be Disrupted Privacy / Security vs. Benefits of Data
  24. Big Data, Big Risks and Even Bigger Opportunities (cont’d) • The purpose of collecting data is to better serve customers not the other way around • Consumers are becoming more aware of the value of their data and less willing to give sensitive data for free • To turn data annoyance into empowerment, companies should engage users, enlist their help, give them control, and even reward them with data / insights they like to see in return • Getting the exact result vs. having a good set of options • If data is presented to users directly, such as search engine, should aim to maximize precision • In the case of ads where the relationship between ads and your interest is obfuscated, can compromise on precision to achieve broader optionality Customers Come First, Data Second Tradeoff between Precision & Optionality
  25. • More is not always better - more data can lead to more data quality issues, confusion and lack of consistency in business decision making, especially with conflicting data • The challenge of getting the right information to the right person at the right time is expanded due to the sheer size of big data • Storage is relatively cheap, and the technology to process data is available on demand • But what about people and skills? Having the right people and right skills to analyze and take action on the data is the new big challenge Data++ = Confusion++ and Consistency-- People and Skills are the New Challenge Big Data, Big Risks and Even Bigger Opportunities (cont’d)
  26. Thank you! Twitter: @GlennSolomon Blog: