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Integrate 2015:
Big Data: Big Investment Opportunities
September 2015
Glenn Solomon
Managing Partner, GGV Capital
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
Why Has Big Data Become a Big
Target for VC
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
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
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
How We Look at the Big Data
Marketplace
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
Where are VCs Investing
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
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
How is Big Data Being Used in
Companies
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
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
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
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
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
Where Do We See the Opportunities
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
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
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
Big Data Risks and Opportunities
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
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
• 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)
Thank you!
Twitter: @GlennSolomon
Blog: goinglongblog.com

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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)

Notes de l'éditeur

  1. Here’s an infographic from one of our portfolio companies – Domo, on how much data we generate every minute of the day.
  2. And the driving forces behind the wealth of data we generate, are the mobile devices, which is 2x of PC by 2019, and the explosion of IoT, which is 10x of PC and 5x of mobile by 2019.
  3. Overall, companies are increasingly investing in Big Data, as can be seen from the left chart. The right chart shows where different industries stand in terms of their readiness to invest in big data. Across these industries, more than 50% of the respondents are seeing the value of big data.
  4. This is how we look at the Big Data marketplace: companies that store data, companies that analyze data, companies that help you make decisions with data, and vertical uses of data, such as sale and marketing, advertising, security, retail, finance, and healthcare.
  5. Most of the companies from this group are in the storage space.
  6. Companies in the group are a combination of analyzing data, making decisions with data, and vertical uses of data.
  7. Netflix has used the data they harnessed around viewers patterns to predict that a show directed by David Fincher featuring Kevin Spacey would be a big hit – which gave birth to House of Cards! With big data predictive analysis, Netflix has 70% success rate renewing shows for second season, versus 30% from traditional TV series. Remember to bring up: Stitch Fix (apparel store for women ran by 50 data scientists) Opendoor and daughter wanting to be a data scientist
  8. LinkedIn collects data from users to recommend people you may know as a way to lower the friction for user acquisition. They are also feeding some data they collected back to users, such as the “who’s viewed your profile” feature, to create a more engaging experience for users.
  9. Disney has made a huge push on the MyMagic+ system, which helps Disney understand visitors better, such as location data and purchase behavior. As a result they are able to manage in-park traffic flow and target marketing more efficiently.
  10. Coca Cola and their “Black Book Model” has enabled consistent quality of orange juice despite the whims of Mother Nature. It takes into account 1 quintillion decision variables, and using precise and dynamic formula to decide where to source and how to blend for the best quality juice.
  11. Munis are also benefiting from big data – taking the New York Fire Department as an example, through an algorithm of 60 different factors, they calculate a risk score for the city’s 330k buildings, and rank them accordingly for more frequent inspection and more firefighting resources.
  12. The blue bars are where we see the opportunities, and the companies underneath are examples in those spaces. Domo – business management platform for decision makers Periscope – sophisticated tools targeting data scientists and data-driven teams
  13. The blue bars are where we see the opportunities, and the companies underneath are examples in those spaces. AlienVault – single platform that combines log management and SIEM, easy to use and deploy for mid-market enterprises Tamr – leverages machine learning to connect and curate massive variety of data, saving data scientists 80% of their time previously spent on prepping the data into usable format
  14. The blue bars are where we see the opportunities, and the companies underneath are examples in those spaces. Luminoso – specializes in deriving insights from the most unstructured, unpredictable and nuance context - customer feedback DataScience – brings a highly qualified and on-demand team of data scientists to you
  15. We are constantly battling between the benefits big data brings and the privacy and security concerns – especially as data is getting more personal and behavioral Data wants to be more open, but companies that have benefited from proprietary information are pushing back
  16. 80/20 rule for tradeoff between precision and optionality Although we are talking about big data today, it’s helpful to take a step back and emphasizing that data is here to better understand and better serve customers
  17. Storage and tech to process data are readily available to us; people and skills are the new big challenge to derive insights from data More data can potentially lead to more confusion and conflicting decisions, so getting the right information to the right person at the right time is crucial