Chris Neumann provides insight on his experience of starting DataHero and offers guidance on how others can start their own tech company.

1. BUILDING A SUCCESSFUL
DATA STARTUP
Chris Neumann |
@ckneumann
© 2014 Datahero, Inc.

2. BUILDING
DATA COMPANIES
IS HARD
© 2014 Datahero, Inc.

3. STARTUPS BEGIN LIKE
THIS
© 2014 Datahero, Inc.

4. DATA STARTUPS ARE
ABOUT
TIMING
© 2014 Datahero, Inc.

5. IT’S ABOUT TIME
Successful data startups recognize
fundamental shifts in technology or business
processes
and create solutions that address new pain
points
© 2014 Datahero, Inc.

6. IT’S ABOUT TIMING
• The Data Warehouse market resulted from a need for
businesses to have a “single source of truth”
• The Big Data market resulted from the dramatic increase
in data volumes from machine-generated data
• The Cloud BI market is emerging as a result of the
decentralization of enterprise data as services move to
the cloud
© 2014 Datahero, Inc.

7. BIG DATA
© 2014 Datahero, Inc.

8. BEFORE
• Companies generally analyzed relatively
small volumes of (transactional) data
• Larger volumes of data could be stored in
data warehouses, but calculations were
relatively simple (aggregates, basic
business metrics, etc.
© 2014 Datahero, Inc.

9. THE SHIFT
• Machine-generated data, such as from web server logs,
provided far more granular information about customers
• Companies wanted to perform complex analysis on these
larger volumes of data in order to better understand
customer behavior
– There was also the potential for entirely new
businesses built around data
© 2014 Datahero, Inc.

10. THE CHALLENGE
• The volume of structured data companies wanted to
analyze was becoming larger than what could fit in a
single server
The rate of growth of data now exceeded the rate of
increase of storage density
• This shifted the performance bottleneck to the network
© 2014 Datahero, Inc.

11. THE SOLUTIONS
• First Generation: Fix it with hardware!
– Make servers bigger (Teradata)
– Make the network faster (Netezza)
• Second Generation: Fix it with software!
(Aster Data, Greenplum, Vertica)
– Be smarter about where we store the data
– Be smarter about when we move the data
– Be smarter about how we move the data
© 2014 Datahero, Inc.

12. THE SOLUTIONS
Of the five original “Big Data” startups:
4 were acquired in a span of less
than a year for more than $2.5B total
The fifth was one of the acquirers
© 2014 Datahero, Inc.

13. CLOUD DATA
© 2014 Datahero, Inc.

14. BEFORE
• The data business users wanted to analyze was
generated by on-premises software
• Centralized data stores (EDWs) were used to aggregate
data from a small number of strategic sources
• BI teams would create reports for business users to
access
© 2014 Datahero, Inc.

15. THE SHIFT
• Over the past five years, business software is being
replaced with cloud services, the vast majority of which
are departmental
• Company data is no longer stored primarily in on-premises
systems, but is increasingly found in the cloud
• For the first time in more than 20 years, company data is
becoming decentralized
© 2014 Datahero, Inc.

16. THE CHALLENGE
• Companies now have a large number of remote data
sources each used by a small number of users
For the first time ever, business users have direct
access to their data
• Users now have access to the data they want to work
with, but don’t have the tools to take advantage of it
© 2014 Datahero, Inc.

17. THE SOLUTIONS
• First Generation: Pull the data back!
– Custom integrations to pull cloud data down into on-premises data warehouses
– Put the data warehouse in the cloud…and then pull the rest of the data in (GoodData, Birst, RJMetrics)
• Second Generation: Leave the data where it is!
(DataHero, SumAll)
– Treat cloud business services as the systems of record
– Take advantage of existing security and permission models
– Eliminate the process bottleneck by empowering users to connect directly to the services they need
© 2014 Datahero, Inc.

18. CUSTOM CONNECTORS
• High-speed connectors built in collaboration with
partners for optimal performance
• Robust, extensible framework supports rapid
development of new connectors
• Secure integrations leverage partner security models
for consistent data visibility
DATA DECODER
• Advanced classification algorithms identify and
normalize data types across services and files
• Semantic types such as URLs, Email Addresses and
Lists extend traditional data types to provide added
metadata
• Confidence intervals drive an intuitive feedback
interface with users
EXTENSIBLE CONNECTION
FRAMEWORK
DATA DECODER
C
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THE SOLUTIONS

19. THE SOLUTIONS
INTUITIVE HTML5 INTERFACE
• Intuitive drag-and-drop interface created
through user-centric design process involving
thousands of hours of user testing and
hundreds of users
“NO CODE” DATA COMBINATIONS
• Intuitive interface enables business users to
combine (join) data across services and
spreadsheets without coding or SQL
• Recommendation engine suggests common
keys based on metadata derived by the Data
Decoder

20. IDENTIFY THE SHIFT,
THEN FIND THE PAIN
POINT
© 2014 Datahero, Inc.

21. BE THE PAINKILLER
• Every major shift in technology and/or
business process results in new
opportunities and new pain points
– Many of those pain points will have easy
solutions
– A few will require fundamentally new
approaches
© 2014 Datahero, Inc.

22. SO WHERE
ARE WE?
© 2014 Datahero, Inc.

23. © 2014 Datahero, Inc.

24. © 2014 Datahero, Inc.

25. © 2014 Datahero, Inc.

26. WE’RE HIRING!
DataHero.com/jobs
© 2014 Datahero, Inc.

27. chris@datahero.com
@ckneumann
© 2014 Datahero, Inc.