Presentation by Dave Marples on the Business aspect given during the Big data for Transport webinar held on 11 December 2015.

1. The Business Angle
What does Big data mean for
enterprises?
Dave Marples, Technolution B.V.
dave.marples@technolution.eu

2. Why?
Businesses live or die based on what they know –
that might be knowledge of their customers, their
operating environment, their products or their
competitors.
Business that do not take advantage of every source
of information they can lay their hands on expose
themselves to risk and operational compromise.
Big data is just one more source – but the information

3. Roles in a Big Data Ecosystem
variously taken by companies, Research Institutions and Public Authorities
 Publishers: Make data sets available for
others to make use of. The sources of data.
 Distillers: Combine, refine and render down
data to extract useful information from it. The
processors of data.
 Consumers: Use published data sets to
make or inform decisions or actions. The sinks
of data.

4. Typical Data Publishers

5. Typical Data Distillers

6. Typical Data Consumers
Image Credits: http://www.coreyfishes.com/koken/content/french-trawlerman/lightbox/ http://wsaforum.org/

7. Key Objections…
 Making my data available will just help my
competitors
 Making data available will damage my business,
my model or my reputation
 My business depends on confidentiality and/or
privacy – I can’t jeopardize that!
 Who on earth would want my data?

8. Real Risks
 There is a risk you are releasing more
information than you think you are…
 Your data may not be ‘clean’ enough to be
useful to others, and it may damage your
reputation or business model to make that fact
public!
http://fortune.com/2014/06/30/big-data-dirty-problem/
 The process of releasing your data may cost
you money for little benefit

9. Real Opportunities
 People will find value in your data that you never
knew was there, or which can only be realized in
combination with other data sources
 The right data will drive the behaviors you desire,
and discourage the ones you don’t
 There is a business in distilling (and thus adding
value) to raw data – those that can find the
information in data will benefit from it (c.f.
stockbrokers)

10. Challenges
 Once you make your data available, you need
to publicise the fact that its there, what it is
and how to access it
 You need to find mechanisms to extract value
from the data release; perhaps by increased
sales, perhaps through better market
presence, perhaps through better systemic
performance or less error cases
 Its still unclear how users can guided to
reliable data sources, especially when they
are ‘hidden’ by app frontages

11. Follow Neelie Kroes
If you love your data, set it free!