Embracing Cloud Deployment for Big Data and DevOps

1. Embracing Cloud Deployment for Big
Data and DevOps
Steven Woodward
Technology Incubation Lab, CTO, AstraZeneca 22nd June 2016

2. CTO Office
2
Our team was established to create new value by catalyzing innovative,
emerging technologies across AstraZeneca
Technology
Incubation Labs
Competency
Centers
Enterprise
Architecture
Multi-disciplinary teams that test
new technologies and accelerate
platforms to build internal
expertise and hands-on experience
of potential game-changing
technologies whilst focusing on
immediate business problems
Established User Experience and
Mobility competency centers as
key strategic areas for future
success. They will develop and be
embedded into enterprise
capabilities with world-class
technology leadership
Providing enterprise leadership to
understand the pain points of the
business and ensure that proposed
technology changes are unified and
governed to maximise business
value as AZIT develop corporate
platforms, workflows and choices

3. Global Reach
Today we have 3 tech labs.
Presence in these key technology
clusters gives an early view of
emerging technologies,
companies and start-ups.
San Francisco is today’s Innovation Capital of
the World and our tech lab will facilitate links
with innovative research start-ups, venture
capitalists and global technology leaders. This
office provides new opportunities at the
forefront of healthcare digital innovation and
major breakthroughs in enterprise technology.
Cambridge is the most dynamic scientific
business cluster in the world. We are
surrounded by 19 science parks with over
1,500 high-tech companies, world class
academic institutions at the bleeding
edge of scientific research and key
research hospitals like Addenbrookes.
Shanghai is emerging as the top city for
tech innovation internationally and is
predicted to become the global technology
centre within 4 years. Our tech lab is able
to tap into novel scientific research &
development, advanced engineering,
health nanotechnology and robotics.

4. Scaling for Big Data

5. 200m+ Unstructured Documents
5
We have silos of unstructured content, both inside the company and in the cloud.
We initially focused on developing a big-data engine for unstructured R&D content for scientists.
Admin
Access
Data Sources
Data
Mappings
Configure
Permissions
Tag
Content
Sinequa
Index
Applications
Web Service

6. Big Data Engine
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We had developed a big data engine that powers multiple business
applications, not just enterprise search. This Swiss-army knife for
search let’s us tackle many problems
R&D News AlertsR&D ChemSearch
Find Partners
Mobile Apps Competitive Intel
Medical Affairs

7. Containers for Rapid Deployment

8. Elastic Cloud Performance
8
Sinequa now handles ~2500 queries per min. Designed from the start to
leverage cloud elastic scaling capabilities for responsive performance for 10
users or 70,000 users.
8
Happy UsersDockerElastic Container
Services
By leveraging container technology, we can spin up new services quickly. It’s very
cost-effective and enables new approaches to be easily included in your workflows
 Automated video transcription
 High precision text analytics

9. In-Video Search!
9
The Azure Media Services cloud platform is able to ingest audio and video files and
automatically generates a >90% accurate transcription (English only)
9
By combining Amazon, Docker and Microsoft Azure, we were able to go from first prototype
to global implementation in 7 weeks in our Enterprise search platform.

10. From Code to Container
10
We can write code in an IDE,
commit to Git Repo, Build a
Docker Container, test and
deploy to AWS in about 3
minutes!

11. Exponential Data Growth

12. Wearables, Sensors and Beacons
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• Disparate sensors form
cohesive systems

13. Artificial Intelligence
1313
Skills and Expertise Lookup:
Department Information Delivery:
Information Lookup:
• Uses machine learning and neural networks to help bring
context to user input
• Users a series of natural language processing algorithms to help
validate and clean the input data
• Connects to AstraZeneca API’s to gather individuals skillsets
• Built as a service, allowing multiple input channels
• Slack
• Chatter
• Skype

14. Predictive Modelling
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With larger, complex datasets, machine-learning techniques can accelerate and increase
accuracy of decision making, improving our productivity
Optimised models created using cloud elastic approaches offer another big data engine
opportunity across the enterprise:- salesforce optimisation, employee retention, resource
allocation, patient responder prediction, manufacturing/supply chain improvements.

15. Confidentiality Notice
This file is private and may contain confidential and proprietary information. If you have received this file in error, please notify us and remove
it from your system and note that you must not copy, distribute or take any action in reliance on it. Any unauthorized use or disclosure of the
contents of this file is not permitted and may be unlawful. AstraZeneca PLC, 2 Kingdom Street, London, W2 6BD, UK, T: +44(0)20 7604 8000,
F: +44 (0)20 7604 8151, www.astrazeneca.com
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We are currently scouting Federated Analytics, Mobile Security, Video
Compression & Optimization, Pre-emptive event driven actions….Please get in
touch if you can help
Thank You & Questions!
Steve Woodward steven.woodward@astrazeneca.com
Nick Brown nick.brown@astrazeneca.com