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Brian Ellerman Head, Technology Scouting and Information Science Innovation, Sanofi
TechJunction Tucson 2015
The views expressed are those of the presenter and do not necessarily reflect those of Sanofi or its management.
At the time of presentation, the presenter held no material interest in any of the companies mentioned herein, other than his employer.
Technology Scouting
“Technology scouting can be regarded as a method of Technology forecasting or in the broader context also an element of corporate foresight. At the same time Technology Scouting also contributes to Technology Management by (1) identifying emerging technologies, (2) channeling technology related information into an organization, and (3) in a corporate context supporting the acquisition of technologies.”
Information Science Innovation
“Information science is an interdisciplinary field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval, movement, dissemination, and protection of information. Practitioners within the field study the application and usage of knowledge in organizations, along with the interaction between people, organizations and any existing information systems, with the aim of creating, replacing, improving, or understanding information systems. Information science is often (mistakenly) considered a branch of computer science; however, it predates computer science and is actually a broad, interdisciplinary field, incorporating not only aspects of computer science, but often diverse fields such as archival science, cognitive science, commerce, communications, law, library science, museology, management, mathematics, philosophy, public policy, and the social sciences.”
“Innovation differs from improvement in that innovation refers to the notion of doing something different rather than doing the same thing better.”
Source: Wikipedia
The great opportunity of big
data is to analyze seemingly
unrelated data, regardless of
source or size, and yield novel
insight and business value.
“Once there was a miller who was poor,
but who had a beautiful daughter. Now
it happened that he had to go and
speak to the king, and in order to make
himself appear important he said to him,
"I have a daughter who can spin straw
into gold."
The king said to the miller, "That is
an art which pleases me well, if your
daughter is as clever as you say, bring
her to-morrow to my palace, and I will
put her to the test."
And when the girl was brought to him
he took her into a room which was quite
full of straw, gave her a spinning-wheel
and a reel, and said, "Now set to work,
and if by to-morrow morning early you
have not spun this straw into gold
during the night, you must die."
Thereupon he himself locked up the
room, and left her in it alone.” ‘Rumpelstiltskin’ by Brothers Grimm.
http://www.enneagramplayground.com/the-enneagram-gallery/the-
enneagram-in-fairy-tales
The great opportunity of big data is to analyze
seemingly unrelated data, regardless of source or size,
and yield novel insight and business value.
Realizing this, however,
requires equally disparate data,
skills, and technology, some of
which simply do not exist inside
organizations.
Assertion 1: Realizing the opportunity of big
data requires the integration of existing and
novel data sources.
Observation: Novel data
sources are rarely free, easy to
acquire, or easy to curate.
Source: ‘Information Sources That May be Linked to an Individual for Use in Health Care’
Weber, Mandl, and Kohane; JAMA June 25, 2014, p. 2480
Source: GNS Healthcare
“CoMMpass is a longitudinal study of patients with
newly-diagnosed active multiple myeloma. The goal
is to map the genomic profile of each patient to
clinical outcomes to develop a more complete
understanding of patient responses to treatments. A
cornerstone of the MMRF’s Personalized Medicine
Initiative, the study will collect and analyze tissue
samples and genetic information from
approximately 1,000 newly diagnosed multiple
myeloma patients for at least eight years.”
Assertion 2: Realizing the opportunity of big
data requires the integration of disparate, often
novel or innovative technology.
Observation: Innovative
technology requires expertise to
implement, operate, and
support.
SELF-SERVICE • Datapedia • Data Ecommerce • Tools Provisioning
OPERATIONALIZED GOVERNANCE • Service Enablement
Capabilities • Data Management
Focused • Not IT functions
PLATFORM-AS-A SERVICE (PaaS) • Performance And Capabilities Focused • IT Functions • Infrastructure Team
Build
Reports &
Dashboards
Perform
Advanced
Analytics
Browse Data
Catalog, Search,
Request Data
Provisioning
Ingest &
Curate
Data
Data Standards
& Quality
Guidelines
Ontologies and
Metadata
Management
Usage Tracking,
Reporting &
Chargeback
Data and
Sandbox
Provisioning
Data
Lake
Process &
Transform
Data
Ingestion
Standards and
Guidelines
Platform
Evolution
Secure
Data
1
2
3
4 5
Computing
Resource
Management
Processing
and Workflow
Standards
| 15
Consumption:
Line of Business owned. BI, dashboards, reporting, stats.
Container:
IT owned. Enterprise Data Hub (Cloudera+). Hadoop. Etc.
Curation:
Data Science owned. Data cleansing and transformation
Data discovery, cognitive computing
‘The 10 Coolest Big Data Products Of 2014’
www.crn.com
http://dataconomy.com/how-facebook-deal-with-their-masses-of-user-generated-data/
Assertion 3: Realizing the opportunity of big
data requires the integration of disparate, often
missing skills.
Observation: Critical missing
skills can help address data and
technology, and business
strategy should drive which are
‘owned’ vs ‘rented.’
The great opportunity of big data is to analyze
seemingly unrelated data, regardless of source or size,
and yield novel insight and business value.
Realizing this, however, requires equally
disparate data, skills, and technology, some of
which simply do not exist inside organizations.
Conclusion: With coordination
and collaboration this approach
can enable key solutions and
enhance business value.
Data
Technology
Skills
Partnerships
21
Big Data Steering Committee
Operating Committee
Opportunity Area Coordination Enabler Coordination
Area 1
Core team
Key contributors /
Opportunity
leaders
Source: Cap Gemini S.A.
Area 2 Area 3 Area n