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Data


DATA50: THE WORLD’S TOP DATA STARTUPS

Jennifer Li, Sarah Wang, Jamie Sullivan
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Contents : Intro
 * Intro
 * The Data50 list
 * Capital raised by category
 * Data50 companies by geography
 * Data50 companies by founding year
 * Investment dollars are growing in every category


Over a decade after the idea of “big data” was first born, data continues to be
one of the most important and furiously growing innovation drivers across both
large enterprises and new startups. From providing pulse checks that are
foundational to business operations to intelligently automating daily tasks
through machine learning, data has become the central nervous system for
decision-making in organizations of all sizes. Moreover, the use of data now
reaches well beyond data scientists, data analysts, and data engineers —
everyone is a data producer and consumer. 



The result of this increased focus on data: The business of managing data has
already become one of the fastest growing areas of infrastructure, estimated to
be worth over $70B and accounts for over one-fifth of all enterprise
infrastructure spend in 2021. The beauty of this market’s formation is that it
marries the field of software engineering, analytics, and artificial
intelligence, while riding the tidal momentum of cloud computing. (For more on
the architectural evolution and driving forces behind this massive trend, see
this piece, Emerging Architectures for Modern Data Infrastructure, which was
just updated for 2022.)

The growth of the data industry has also given birth to some of the most
exciting and impactful enterprise software companies in the last few years.
Recent public juggernauts such as Snowflake and Confluent have already changed
the way thousands of businesses operate and millions of products are built.
However, most people are less familiar with the movers and shakers — the next
generation of category-defining companies.

To help cut through the noise after a record-breaking 2021 in which data
companies received tens of billions in venture capital investment — and an
already strong 2022 — we’ve compiled the inaugural class of the Data50. These
are the bellwether companies across the most exciting categories in data. In
aggregate, these 50 companies are valued at more than $100B and have raised
approximately $14.5B in total capital, with 20 having reached unicorn status by
2021.

Without further ado, we’re excited to introduce the Data50 of 2022. 


THE DATA50 LIST

Show
All categories
 * All categories
 * AI/ML
 * BI & Notebooks
 * Customer Data Analytics
 * Data Governance & Security
 * Data Observability
 * ELT & Orchestration
 * Query and Processing

Apply Filter
Clear all filters

RankCompanyCategoryLocationValuation RangeWebsite

1
Databricks
Query and Processing San Francisco, CA $5B+ Databricks 2
Fivetran
ELT & Orchestration Oakland, CA $5B+ Fivetran 3
Scale.ai
AI/ML Palo Alto, CA $5B+ Scale.ai 4
OneTrust
Data Governance & Security Atlanta, GA $5B+ OneTrust 5
Dbt labs
ELT & Orchestration Philadelphia, PA $1B-$5B Dbt labs 6
Starburst
Query and Processing Boston, MA $1B-$5B Starburst 7
Collibra
Data Governance & Security Brussels, Belgium $5B+ Collibra 8
Dremio
Query and Processing Santa Clara, CA $1B-$5B Dremio 9
Dataiku
Query and Processing New York, NY $1B-$5B Dataiku 10
Hugging Face
AI/ML New York, NY $250-999M Hugging Face 11
DataRobot
Query and Processing Boston, MA $5B+ DataRobot 12
Primer.ai
AI/ML San Francisco, CA $250-999M Primer.ai 13
Snorkel
AI/ML Palo Alto, CA $1B-$5B Snorkel 14
Anyscale
AI/ML San Francisco, CA $1B-$5B Anyscale 15
Firebolt
Query and Processing Tel Aviv, Israel $1B-$5B Firebolt 16
Astronomer
ELT & Orchestration Cincinnati, OH $100-$249M Astronomer 17
Alation
Data Governance & Security Redwood City, CA $1B-$5B Alation 18
Weights & Biases
AI/ML San Francisco, CA $1B-$5B Weights & Biases 19
Sigma Computing
BI & Notebooks San Francisco, CA $1B-$5B Sigma Computing 20
Monte Carlo
Data Observability San Francisco, CA $250-999M Monte Carlo 21
OctoML
AI/ML Seattle, WA $250-999M OctoML 22
Census
Customer Data Analytics San Francisco, CA $250-999M Census 23
Hex
BI & Notebooks San Francisco, CA $250-999M Hex 24
Hightouch
Customer Data Analytics San Francisco, CA $250-999M Hightouch 25
Amperity
Customer Data Analytics Seattle, WA $1B-$5B Amperity 26
BigID
Data Governance & Security New York, NY $1B-$5B BigID 27
Privacera
Data Governance & Security Fremont, CA $250-999M Privacera 28
Immuta
Data Governance & Security Boston, MA $250-999M Immuta 29
Bigeye
Data Observability San Francisco, CA $250-999M Bigeye 30
Matillion
ELT & Orchestration Greater Manchester, United Kingdom $1B-$5B Matillion 31
Heap
Customer Data Analytics San Francisco, CA $1B-$5B Heap 32
Tecton
AI/ML San Francisco, CA $250-999M Tecton 33
Imply
Query and Processing Burlingame, CA $250-999M Imply 34
Sisu Data
BI & Notebooks San Francisco, CA $250-999M Sisu Data 35
Rudderstack
ELT & Orchestration San Francisco, CA $100-$249M Rudderstack 36
ActionIQ
Customer Data Analytics New York, NY $250-999M ActionIQ 37
ClickHouse
Query and Processing Portola Valley, CA $1B-$5B ClickHouse 38
Airbyte
ELT & Orchestration San Francisco, CA $1B-$5B Airbyte 39
Rockset
Query and Processing San Mateo, CA $250-999M Rockset 40
Labelbox
AI/ML San Francisco, CA $250-999M Labelbox 41
Explorium
AI/ML San Mateo, CA $250-999M Explorium 42
Rasa
AI/ML San Francisco, CA $100-$249M Rasa 43
Prefect
ELT & Orchestration Washington, DC $250-999M Prefect 44
Materialize
Query and Processing New York, NY $250-999M Materialize 45
Coiled
AI/ML New York, NY $100-$249M Coiled 46
Preset
BI & Notebooks San Mateo, CA $100-$249M Preset 47
Metabase
BI & Notebooks San Francisco, CA $100-$249M Metabase 48
Iterative.ai
AI/ML San Francisco, CA $100-$249M Iterative.ai 49
Robust Intelligence
AI/ML San Francisco, CA $100-$249M Robust Intelligence 50
Fiddler
AI/ML Mountain View, CA $100-$249M Fiddler


Show lessShow more


METHODOLOGY

Data50 companies were founded after 2008, have raised new funding in the last
two years, and their employee base is growing at at least 30% YoY. Their
products are horizontal technologies serving data or data application teams
across industries.

Rankings are based on a blend of most recent valuation, company size, employee
growth over the last two years, years in operation, and current revenue scale.
Employee data is based on publicly available data from LinkedIn. Funding data is
based on publicly available data from Pitchbook and Crunchbase, and is accurate
as of March 22, 2022.

Note that this list does not include transactional database companies such as
CockroachDB, PlanetScale, and Yugabyte because usage of the data with those
technologies is inherently transactional instead of analytical.

Looking under the covers, we’ve broken down the Data50 into seven
subcategories. 

 1. Query and processing technology is the core engine to access, aggregate, and
    compute data. It involves two main classes: batch processing (e.g.
    Databricks and Starburst) and real-time processing (e.g. ClickHouse and
    Imply). The latter has been gaining more attention over the past few years,
    driven by increasing demand for real-time applications.
 2. AI/ML (artificial intelligence and machine learning) includes software that
    applies algorithmic modeling and machine learning for processing large scale
    data. This space is maturing and flourishing as evident from the sheer
    volume of companies that made the list. Some of the players are focused on a
    particular type of data (e.g. Rasa and Hugging Face for natural language),
    while others are focused on different areas, like the productization of AI
    (e.g. Scale, Tecton, and Weights and Biases) or acting as the “compute
    layer” for running AI workloads (e.g. Anyscale).
 3. ELT & orchestration enables the movement of data. It is the transportation
    layer that guarantees data arrives at its destination accurately and on
    time. This category evolved from the traditional ETL vendors that are built
    upon on-prem drag-and-drop interfaces. The new class of players, on the
    other hand, are mostly cloud-native (e.g. Fivetran and dbt),
    developer-friendly (e.g. Astronomer and Prefect), and handle more complex
    dependencies across different data environments.
 4. Data governance and security are becoming critical concerns as the data
    stack becomes increasingly complex and more stakeholders are involved.
    Governance tools are required — especially in highly regulated industries —
    to secure data and maintain compliance throughout the data lifecycle (e.g.
    OneTrust and Collibra). This category is relatively new and typically serves
    large enterprise companies that are under regulatory oversight.
 5. Customer data analytics has traditionally been owned by marketing teams.
    However, due to its increased importance, data teams are now more involved
    in integrating customer data with central data platforms. This category is
    focused on capturing customer data (e.g. Rudderstack and ActionIQ) or
    operationalizing that data to serve front-line business use cases (e.g.
    Census and Hightouch).
 6. BI & notebooks cover the consumption layer of data. Even though it is a
    well-established category, new players such as Preset or Metabase are taking
    an open source-first approach and appeal to technical data engineers, as
    well as business intelligence teams. The fast-changing nature of data needs
    also creates more demand for iterative and interactive notebooks (e.g. Hex)
    and automatic insight generation (e.g. Sisu).
 7. Data observability draws inspiration from best practices in the software
    engineering stack. As the data stack becomes increasingly interdependent on
    up and downstream tooling, and the accuracy of data has broader impact,
    observability emerged as the newest category to provide monitoring and
    diagnostic capability across the data flow.

Even though the main market tailwind driving adoption is the increasing volume
and usage of data, the underlying drivers differ for each category. For example,
the advances happening in the querying and processing space are mainly driven by
the separation of compute and storage, movement to the cloud, and cheaper
computing power. Meanwhile, the adoption of operational tooling in data
governance and data observability is largely driven by the growing operational
use cases and complexity of data workflows.


QUERY AND PROCESSING COMPANIES HAVE RAISED THE LION’S SHARE OF CAPITAL

The query and processing category only accounts for one-fifth of the companies
in Data50, but the amount of capital — almost 50% of all funding — invested in
this category is staggering. Even though this data is influenced by Databricks’s
recent $1.6B funding round, the category would still account for 37% of all
funding — more than twice that of the next category — without it.

When looking at the categories by company count, the distribution is more
balanced. AI/ML is the biggest category by the number of companies, largely
because the space is still evolving and requires a new separate set of tools to
train, measure, and productionize models. (For more on how this space is
evolving, read Emerging Architectures for Modern Data Infrastructure.)


THE DATA50 IS CLUSTERED IN THE BAY AREA

Of the 50 companies, 47 (94%) are based in the United States and three are
international. The majority of the companies, 33, are based in the San Francisco
Bay Area, while nine are along the I-95 corridor in Washington, D.C.,
Philadelphia, New York, and Boston. Two are based in Seattle, one is based in
Cincinnati, and one is based in Atlanta. 

Such distribution is heavily impacted by where the large-scale data ecosystem
resides historically (Oracle and Teradata were both founded in the Bay Area, for
example). However, we’re seeing more data companies popping up across the globe
(e.g. Firebolt and Matillion) as data engineering talent and demand for data
tooling reach nearly every continent.


AI/ML CATEGORY DROVE SPIKE OF NEW DATA COMPANIES IN 2019

The majority of the Data50 companies were founded after 2014, with a peak around
2019, driven by the explosion of AI/ML tooling. In fact, many more data
companies were founded after 2019, but because we’re focused on companies that
have reached a certain scale, most newer companies don’t appear on this list
yet.


INVESTMENT DOLLARS ARE GROWING IN EVERY CATEGORY

Looking at per category investment, the most notable trend is that AI/ML
companies are picking up more investor interest than ever, mostly concentrated
in the early stage. The same holds true for ELT and orchestration – largely
driven by mega rounds from Fivetran and dbt. Query and processing companies
continue to attract big dollars, although the companies tend to be in the later
stage.

We firmly believe the next 10 years will be the decade of data, encompassing
infrastructure, applications, and everything in between. As a result, we’ll
continue to see record-breaking growth, funding, and market capitalization,
which we will track annually in this list. Congratulations to all the companies
in the first Data50 class!


Posted March 23, 2022

 * Jennifer Li is a partner at a16z, where she focuses on enterprise companies.
   Prior to joining the firm, she worked for AppDynamics and Solvvy as a product
   manager.
   
   Follow Twitter

 * Sarah Wang is a general partner at a16z, where she focuses on growth stage
   investments. Prior to joining the firm, she worked for TA Associates, DCM
   Ventures, Eagle Cliff Partners, BCG, and Morgan Stanley.
   
   Follow Twitter

 * Jamie Sullivan is a partner on the a16z Growth investing team, focused on
   late-stage companies in consumer, enterprise, and fintech. Prior to a16z, he
   worked at private equity firm Leonard Green & Partners.
   
   

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