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

Source: McKinsey Global Institute

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: McKinsey Global Institute

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.

Source: McKinsey Global Institute

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.’

Source: McKinsey Global Institute

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.

Source: McKinsey Global Institute

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

Questions?