Presentation given by Chris Taggart, CEO and Co-Founder of OpenCorporates at Open Knowledge Festival, Geneva, September 2013 Discussing benefits and quality of open corporate hierarchy (network) data

1. Open Data
Not Just Good. Better

2. Open Data is Good!
http://www.flickr.com/photos/stolidsoul/433129708/sizes/o/in/photostream/

3. But we’re not the ones
we need to convince
http://okfestival.org/open-government-data-camp/

4. Most people don’t
care about ‘open’
http://www.flickr.com/photos/erlin1/9312646298/sizes/l/in/photostream/

5. Even though open
data is better
(than closed/proprietary)

6. Even though open
data is better
(than closed/proprietary)
• Better for innovation

7. Even though open
data is better
(than closed/proprietary)
• Better for innovation
• Better for competition

8. Even though open
data is better
(than closed/proprietary)
• Better for innovation
• Better for competition
• Better for efficiency

9. Even though open
data is better
(than closed/proprietary)
• Better for innovation
• Better for competition
• Better for efficiency
• Better for sharing (esp cross-
organisation or cross-border)

10. But open has a secret
weapon
http://www.flickr.com/photos/x-ray_delta_one/8493335701/sizes/l/in/photostream/

11. It’s better quality too
http://www.flickr.com/photos/infusionsoft/4484373179/sizes/l/in/photostream/

12. Problem Cause
Data accuracy
Data is re-keyed. Few eyeballs.
Often little downside to lying
Gaps in data
High (& often duplicated) cost of
data entry. Limited to payers
Lack of granularity
Legacy systems/data models hard
to reengineer in closed world
Errors go uncorrected Few feedback mechanisms
Black box/No
provenance
Can’t reveal (sometimes dubious)
sources. Limits usefulness/trust
Isolated
Proprietary IDs are internal
identifiers & are barriers to
sharing & improved data quality
Common proprietary
data quality issues

13. Problem Cause
Data accuracy
Data is re-keyed. Few eyeballs.
Often little downside to lying
Gaps in data
High (& often duplicated) cost of
data entry. Limited to payers
Lack of granularity
Legacy systems/data models hard
to reengineer in closed world
Errors go uncorrected Few feedback mechanisms
Black box/No
provenance
Can’t reveal (sometimes dubious)
sources. Limits usefulness/trust
Isolated
Proprietary IDs are internal
identifiers & are barriers to
sharing & improved data quality
Common proprietary
data quality issues

14. Problem Cause
Data accuracy
Data is re-keyed. Few eyeballs.
Often little downside to lying
Gaps in data
High (& often duplicated) cost of
data entry. Limited to payers
Lack of granularity
Legacy systems/data models hard
to reengineer in closed world
Errors go uncorrected Few feedback mechanisms
Black box/No
provenance
Can’t reveal (sometimes dubious)
sources. Limits usefulness/trust
Isolated
Proprietary IDs are internal
identifiers & are barriers to
sharing & improved data quality
Common proprietary
data quality issues

15. Problem Cause
Data accuracy
Data is re-keyed. Few eyeballs.
Often little downside to lying
Gaps in data
High (& often duplicated) cost of
data entry. Limited to payers
Lack of granularity
Legacy systems/data models hard
to reengineer in closed world
Errors go uncorrected Few feedback mechanisms
Black box/No
provenance
Can’t reveal (sometimes dubious)
sources. Limits usefulness/trust
Isolated
Proprietary IDs are internal
identifiers & are barriers to
sharing & improved data quality
Common proprietary
data quality issues

16. Problem Cause
Data accuracy
Data is re-keyed. Few eyeballs.
Often little downside to lying
Gaps in data
High (& often duplicated) cost of
data entry. Limited to payers
Lack of granularity
Legacy systems/data models hard
to reengineer in closed world
Errors go uncorrected Few feedback mechanisms
Black box/No
provenance
Can’t reveal (sometimes dubious)
sources. Limits usefulness/trust
Isolated
Proprietary IDs are internal
identifiers & are barriers to
sharing & improved data quality
Common proprietary
data quality issues

17. Problem Cause
Data accuracy
Data is re-keyed. Few eyeballs.
Often little downside to lying
Gaps in data
High (& often duplicated) cost of
data entry. Limited to payers
Lack of granularity
Legacy systems/data models hard
to reengineer in closed world
Errors go uncorrected Few feedback mechanisms
Black box/No
provenance
Can’t reveal (sometimes dubious)
sources. Limits usefulness/trust
Isolated
Proprietary IDs are internal
identifiers & are barriers to
sharing & improved data quality
Common proprietary
data quality issues

18. Problem Cause
Data accuracy
Data is re-keyed. Few eyeballs.
Often little downside to lying
Gaps in data
High (& often duplicated) cost of
data entry. Limited to payers
Lack of granularity
Legacy systems/data models hard
to reengineer in closed world
Errors go uncorrected Few feedback mechanisms
Black box/No
provenance
Can’t reveal (sometimes dubious)
sources. Limits usefulness/trust
Isolated
Proprietary IDs are internal
identifiers & are barriers to
sharing & improved data quality
Common proprietary
data quality issues

19. A concrete example:
corporate networks

20. Hugely important
(and valuable)
• The dataset we need to understand
the corporate world
• Who we (or the government) is really
doing business with
• Political influence/donations/lobbying
• Tax/resource extraction
• Corporate Governance
• Credit risk

21. But proprietary datasets
on this are problematic
• Expensive, so relatively few users
• Huge gaps in data
• Uses proprietary IDs (so not clear
what it’s refers to)
• Restrictive licences
• Opaque – no info re calculations,
provenance or confidence

22. But proprietary datasets
on this are problematic
• Expensive, so relatively few users
• Huge gaps in data
• Uses proprietary IDs (so not clear
what it’s refers to)
• Restrictive licences
• Opaque – no info re calculations,
provenance or confidence
Result: low-quality data

23. The open data
alternative

24. The open data
alternative
Enabled by a grant from the
Alfred P Sloan Foundation

25. Data from disparate
public sources

27. finding
new
insights

28. no such
company

29. ...and
errorstoo
no such
company

30. What a modern financial
company looks like (highly simplified
& truncated views)

31. What a modern financial
company looks like (highly simplified
& truncated views)

32. What a modern financial
company looks like (highly simplified
& truncated views)

33. What a modern financial
company looks like (highly simplified
& truncated views)
private
unlimited
company

34. Crowd-sourcing?

35. Ninja-sourcing!
http://www.flickr.com/photos/danielygo/5531024732/sizes/l/in/photostream/

38. The company that wants to know
your network... every friend...
every interaction
http://www.flickr.com/photos/jeffmcneill/5260815552/sizes/l/
why bother?

39. Facebook, Inc
This is what we got from
their SEC filings as text

40. Facebook, Inc
(and turned into data)
This is what we got from
their SEC filings as text

41. Facebook, Inc
Pinnacle Sweden AB
Vitesse LLC
Facebook Operations LLC
Facebook Ireland Limited
Edge Network Services Limited
Andale Acquisition Corp
(and turned into data)
This is what we got from
their SEC filings as text

42. Facebook Ireland Limited
Edge Network Services Limited
Pinnacle Sweden AB
Vitesse LLC
Facebook Operations LLC
Andale Acquisition Corp
Then we started
investigating
Facebook, Inc

43. Facebook Ireland Limited
Edge Network Services Limited
Then we started
investigating
Facebook, Inc

44. Facebook, Inc
Facebook Ireland Limited Edge Network Services Limited

45. Facebook, Inc
Facebook Ireland Limited Edge Network Services Limited
Facebook Cayman
Holdings Unlimited
IV
Facebook Cayman
Holdings Unlimited II
Facebook Cayman
Holdings Unlimited lll
Facebook Ireland Holdings
Randomus Investments Limited
Facebook International
Holdings II Ltd
Facebook International
Holdings I Ltd
Facebook Cayman
Holdings Unlimited I

46. Want to help?
jobs@opencorporates.com
investigators@opencorporates.com