"Big data" is a broad term that encompasses a wide range of data and contents. Big data offers new approaches to analysis and decision making. At first glance big data and IP may seem to be opposites, but have more in common than one may think. This talk will focus on how big data will impact, and be impacted, by IP. One of the biggest promises in big data is the possibility to re-use data produced via different sources, create new services or predict the future, via the analysis of correlations. In this context, how can companies protect information assets and analytical skills? What are the new skills required to search and analyze in real time a big amount of datasets ? Big data will change not only patents information, but will also generate new types of patents.

1. Big data : big issues for IP
Véronique MESGUICH
Consultant in competitive intelligence
ex copresident, ADBS (Association of information professionals)
www.adbs.fr

2. 
Consultant & trainer, expert in competitive
intelligence

Ex Co-president of ADBS : first european
association of information professionals

Co-writer of « Net recherche » : a
methodologic guide on how to find relevant
information on the Internet
Who am I ?

3. 
What is big data ?

New types of patents, and new types of patent
information

Technical and organizational breakthroughs

New skills for information professionals
Big data : a new paradigm for IP

4. What is big data

The 3 Vs of big data
Volume : petabytes of information
Velocity : data are created at very high speed,
and may be analyzed in real time
Variety : big data are heterogeneous

The analysis of big data can improve decision
making, predicting and forecasting,
productivity,marketing, e-reputation watch,
etc...

5. 
90% of all data available today were created in
the last two years.

One of the biggest promises in big data is the
possibility to reuse data produced via different
sources, create new services or predict the
future, via the analysis of correlations.

Big Data industry is expected to grow in the
next few years. The revenue from Big Data
could be worth $100 billion by 2018 (source:
ABIResearch).
Big data, big opportunities

7. Data may be...

Linked
Data linked via metadata and searchable via semantic
queries

Dark
Dark data are “the data being collected, but going
unused despite its value”. (Gartner Group)

Open
Open data : data available freely to anyone to use and
republish without restrictions from copyright or patents.
Open data are not always public data.

Smart
Smart data : data useful for decision making

8. 
Humans who search and
publish on the Internet (e-mails,
SMS, photos, videos...) especially
on social networks, and use
smartphones or “world wide
wear”

Sensors or machines create data
transmitted via the Internet

Data can be created by a group
of persons : establishing who
owns what can be very difficult
and challenging
Data may be created by...
Source : Forrester 2014

9. IP

Structured documents

Long lifecycle

Storing

Protection

Incentive for
innovation

Driven by government
and companies
Ip Vs Big data ?
Big data

Unstructured data

Real time

Sharing

Reuse

Decision making

Driven by people and
machines

10. 1970s : Online databases and information
services
1980s : Business intelligence, OLAP
1990s : The web appears
2000s : Social networks
2010s : Spread of big data
Impact of the evolution of tools
and methods on IP

11. 
Unstructured data/real time:
We deal not only with selected and structured
data, but heterogeous data, structured or non
structured, produced in real time
The algorithms Hadoop/Map Reduce can
provide real time collecting, indexing and storing

Cloud computing:
Cloud architectures are linked to big data. Agile
and powerful architectures are required to
optimize resources
Technological and organizational
breakthroughs

12. 
Social and collaborative methods:
Crowdsourcing, open innovation : innovations
can easily transfer inward and outward.

Data visualisation based on semantic analysis :
Correlations beetween data can be extracted
automatically

Automated analysis and discovery : text
mining, graph mining, knowledge
representation...
Technological and organizational
breakthroughs

13. Key issues : searching

Datasets are very heterogenous and, unlike
classical documents, are
not necessarily created for a specific purpose
by the traditional “gate keepers” (experts,
analysts, researchers…)

It requires new skills in searching
information

14. 
Can the patent system protect datasets, or
data processing ?

Are data patentable ? Is copyright applicable
to big data ?

Data are created, manipulated, enriched,
reused...

How can be patented the process of
assembling, enhancing or organizing data ?
Key issues : new data for IP, new IP
for data

15. 
The ownership of
data, and the right
to reuse them.

Do the data belong
to their
many creators ? Is
the concept of
copyright adapted to
data generated by
machines ?
Key issues : the ownership of data

16. Data scientist : core skills
(source: Radar O'Reilly)

Base in statistics, algorithms, data mining,
machine learning and mathematics

Knowledge of open-source tools : Hadoop,
Java, Python

Making data available to users : prototypes,
using external APIs, integration with other
services, visualisation

17. A new librarian : the Data librarian
Data Reference Services Librarian
Data Services Librarian
Social Science Data Librarian
Business and Social Sciences
Librarian
Science Research Librarian
Data and eScience Librarian
Science Data Librarian
GIS Librarian
Research Data Management
Librarian
Data Curation Librarian...
Quantitative Data Collections
Librarian
Librarian for Data Visualization
Assessment Librarian....

18. Data management
data management planning
issues such as copyright, intellectual property, licensing of data, embargoes, ethics and
re-use, privacy
storing and managing data during the research project (curation)
depositing data in archives at the end of the project, determining retention and disposal
open access and publishing of data
research organisation policies affecting data
Metadata management
creating and maintaining metadata
developing and applying metadata standards
Using data (data as a resource)
finding or obtaining data for re-use
citing data
data analysis tools and support services
data literacy (an extension of information literacy to include the ability to "access,
assess, manipulate, summarize and present data"
(Source : Australian National Data Service)
Data librarian : missions

19. Chief data
officer : missions

Missions : acquiring,
storing, enriching
and leveraging the
company’s data
assets.

Data inventory

Data governance

Not a core technical
profile

20. Chief analytics officer
Source : https://infocus.emc.com/william_schmarzo/new-roles-in-the-big-data-world/

Analytic assets: Collaborate with the data science team to inventory
analytic models and algorithms throughout the organization.

Analytics valuation: Establish a framework and process for determining
the relative value of the organization’s analytic assets.

Intellectual Property management: Develop processes and manage a
repository for the capture and sharing of organizational IP (check-in,
check-out, versioning).

Patent applications: Manage the patent application and tracking process
for submitting patents to protect key organizational analytics IP.

Intellectual Property protection: Monitor industry analytics usage to
identify potential IP violations, and then lead litigation efforts to stop or
get licensing agreements for IP violations.

Intellectual Property Monetization: Actively look for business partners
and opportunities to sell or license organizational analytics IP.

21. 
Big data will change not only
patents information, but will
also generate new types of
patents

IP should evolve according to
the development of the SMAC
model (social, mobility,
analytics and cloud)

It will require new skills for
information professionals
Summary