Datasets:

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“An organization that harnesses data as an asset, to drive sustained innovation and create actionable insights to supercharge the experience for their customers so they demand more.”

( Mindset + People + Process ) x Technology

Agenda

• The Modern Data Community – breaking the monolith • Data Producers – data product owners • Platforms – from maintenance to customer experience • Consumers – diversity and high velocity decision making • Automation – invent and simplify a balance between governance and agility • Tenets • Getting Started

The Modern Data Organization Breaking the monolith

A shift to microservices

• Decoupled architecture of single-purpose

services

• Business logic and data only accessible

through hardened APIs

•

Increased speed, agility, and innovation

Create a Community with a Data Marketplace Data-driven organizations enable agility by pushing responsibility to the edges, to the producers and consumers of data

Producers “Teams that want to share data”

Lake House Platform “Team that runs the marketplace”

Consumers “Teams that want to use data”

Lake House

• Domain expertise

• Build security controls

• Execute business priorities

• Data ownership and governance

• Build and run the platform

• Business analytics development

• Data quality

• Metadata Management

• Simplify on-boarding

• Enterprise datasets

• Data Discovery

• Data pipeline development

• Training and community

• Creation of new insights

Level of decentralization depends on maturity of skills, complexity of business, domain knowledge required, and pace of tech change

ENGIE builds the Common Data Hub on AWS, accelerates zero-carbon transition Challenge ENGIE’s decentralized global customer base had accumulated lots of data, and it required a smarter, unique approach and solution to align its initiatives and to efficiently provide data across its global business units.

Solution ENGIE built its Common Data Hub data lake on AWS, enabling the company’s business units to collect and analyze data to support a data-driven strategy and to lead the zero-carbon transition.

Result • Collected 95 TB of data across 351 projects • Automated energy predictions • Maximized wind farm energy production

Benefits Since implementing the CDH, ENGIE’s renewable fleet of wind farms, solar farms and hydroelectric dams is significantly more efficient. If you improve the availability and performance of an asset that's worth $100 million or $500 million by just 1% because you tap into the right data— well, I’ll let you do the math. Yves Le Gélard CDO and CIO.

Amazon Kinesis Data Streams

Amazon Redshift

AWS Glue

Amazon Athena

Amazon S3

Amazon SageMaker

Data Producers Data product owners

Producers “Teams that want to share data”

• Domain expertise

• Data ownership and governance

• Data quality

• Metadata Management

• Motivations • Domain knowledge • Metadata • Quality and reliability • Data access • New skills

Product-Oriented Operating Models

1 Re-envision the world as products

2 Organize teams around products.

3 Bring the work to the teams.

4 Reduce risk

through iteration.

M O R F

m e t s y S

amazon.com

C a r t

S e a r c h

I t e m

A c c o u n t

P r o m o t i

o n s

A d v e r t i s i n g

D g

i t a l

A s s e t s

A d a p t i v e H o m e P a g e

O T

s t c u d o r P

amazon.com

m a e T d e s a B

y t i v i t c A

m a e T d e s a B

t c u d o r P

Business

PMO

Design

Dev

Mgmt

Ops

Full Stack. Two Pizzas.

k r o W e h t o t

m a e T g n i r B

m a e T e h t o t k r o W g n i r B

Work

h c t a B e g r a L

h c t a B l l a m S

e l c y C y r e v i l e D

e l c y C g n d n u F

Jan

Dec

e l c y C y r e v i l e D

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M V P

$$ $ Jan May Sept

Risk

Example Sources

Big Data Marketplace

Consuming Businesses

• Operates thousands of micro-services to serve millions of

customers.

• 50 petabytes of data, 600,000 user analytics jobs each day.

• 1,800 teams producing data, 3,300 teams analyzing and

acting on data.

Challenge

• On-prem Oracle did not scale, difficult to maintain, and

costly.

• Limited businesses ability to generate insights and deploy

ML.

Solution

• Built a data marketplace on AWS.

• Doubled the data stored (100PB), lowered costs, gained

insights faster.

• Move data across the business quickly and easily.

• Easily discover data with reduced latency for analytics

results.

Catalog browsing

Order placement

Transaction processing

Delivery scheduling

Video services

Prime registration

Marketplace Web Interface

Marketplace APIs

Discovery service

Data Ingestion

Workflows service

Subscription Service

Data Quality / Curation

Data security and governance

100PB

Platforms From maintenance to customer experience

Lake House platform “Team that runs the marketplace”

Lake House

• Build security controls

• Build and run the platform

• Simplify on-boarding

• Enterprise datasets

• Training and community

• Life after maintenance • Customer centricity • Abstract complexities • Community building • New skills

Transparency and predictive intelligence in production plant

Challenge • Increased complexity in production.

• factory operations need to be adaptable to changing sales and supply

demands.

• avoid points of failure, to fulfil customer orders consistently.

Solution • Total Intelligent Manufacturing product providing transparency into the end-to-end production operations in real time through a single pane of glass.

• Predicts points of failure in the production process ahead of time and

integrates into digital plant simulations.

• Creates a broader data collaboration capability connecting manufacturing

with supply chain and customer retail.

Planned Benefits • Increased plant productivity and steady state daily throughput.

• Equipment effectiveness with reduced outages and quality issues.

• Process agility and enhanced customer experience.

AWS Lake Formation

Amazon S3

Amazon Redshift

Amazon Athena

Amazon QuickSight

AWS Glue

Amazon SageMaker

Consumers Diversity and high velocity decision making

Consumers “Teams that want to use data”

• Execute business priorities

• Business analytics development

• Data Discovery

• Data pipeline development

• Creation of new insights

• Diverse personas • High velocity decisions • Data discovery • Native access • New skills

BMW Group uses AWS-based data lake to unlock the power of data

Challenge BMW Group’s rigid on-premises data lake was challenging their ability to scale to meet demand and accessing siloed data required long lead times.

Solution BMW Group decided to re-architect and move its on-premises data lake to the AWS Cloud—using a serverless architecture that offered agility, flexibility, and a modern web portal give users across the globe access to data.

Result

• Democratizes data usage at scale • Processes terabytes of telemetry data from millions of vehicles daily • Resolves issues before they impact customers • Accelerates innovation • Training 5,000 software engineers and applying AWS Working Backwards

methodology

• Identification of business challenges and develop new cloud-enabled

solutions

“To stay innovative, we are focusing on creating new digital and connected experiences and driving change in our value chain by enabling data-driven decisions.” Kai Demtröder, BMW Group vice president of data, AI

Amazon Kinesis Data Firehose

AWS Glue

Amazon SageMaker

Automation Invent and simplify a balance between governance and agility

Ingest at speed

• Balancing governance and agility • • Detecting quality issues • Understanding data at scale • Helping consumers find data

Brent Shafer, Chairman and CEO of Cerner, Talks About Using AWS to Transform Healthcare

Overview Cerner Corporation delivers healthcare technology globally to 3 million healthcare professionals and innovates to create a seamless and connected world in which everyone thrives. The company has spent the last four decades digitizing healthcare data and ridding clinician’s offices of manila folders and filing cabinets. Now, the collaboration of Cerner and AWS will deliver data that is more accessible and actionable and uses AWS AI and machine learning technologies to predict and potentially prevent health problems. Also, as part of its effort to modernize how it delivers solutions and improves patient outcomes, Cerner has been migrating its privately hosted platforms to AWS. One of the company’s goals is to bring more joy to the practice of medicine—to that end, Cerner is testing its Virtual Scribe technology using speech recognition and Amazon Transcribe Medical to dramatically reduce manual data entry and give doctors more time to spend with patients.

Watch now

“

We’re excited about how this collaboration helps us move closer to Cerner’s vision. Our vision is a seamless and connected world where everyone thrives.

Brent Shafer, Chairman and CEO

Company: Cerner Country: US Company: Cerner Employees: 24,400 Country: US Website: Cerner.com Employees: 24,400 Website: Cerner.com

About Cerner Cerner’s health technologies connect About Cerner people and information systems at Cerner’s health technologies connect thousands of contracted provider people and information systems at facilities worldwide dedicated to thousands of contracted provider creating smarter and better care for facilities worldwide dedicated to individuals and communities. creating smarter and better care for Recognized globally for innovation, individuals and communities. Cerner assists clinicians in making care Recognized globally for innovation, decisions and assists organizations in Cerner assists clinicians in making care managing the health of their decisions and assists organizations in populations. The company also offers managing the health of their an integrated clinical and financial populations. The company also offers system to help manage day-to-day an integrated clinical and financial revenue functions, as well as a wide system to help manage day-to-day range of services to support clinical, revenue functions, as well as a wide financial and operational needs, range of services to support clinical, focused on people. financial and operational needs, focused on people.

Tenets

Tenets for a Modern Data Community

• We enable highly agile organizations by empowering at the edges. Empowered

organizations require players to accept greater responsibility.

• Domain-relevant, high quality, discoverable, and trustworthy data is the basis

of successful communities. This is the responsibility of Data Producers.

•

Innovation is organic. It requires connecting ideas, data, tooling, and know- how. The Platform teams need to abstract complexity from this equation.

• High velocity decisions sustain organizations. This requires Data Consumers to

“experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight.*”

Bezos, J (2017). 2016 Letter to Shareholders. Available at: https://ir.aboutamazon.com/annual-reports-proxies-and-shareholder-letters/default.aspx

Getting Started

Recommendations

• Think big, start small, scale fast.

• Work backwards from customer challenges.

• Form a multi-disciplinary teams including business, technology, and data skills.

•

Incentivize your data producers by creating metrics on the availability and completeness of their data.

• Build a community, celebrate success by publishing blogs and writing stories

about what you’re doing.

• Automate tasks to increase adoption.

Want to build a data vision and strategy?

Have a strategy and need help executing it?

Joint engagements with business and technology stakeholder alignment

Joint engineering engagements between customers and AWS technical resources

Create an organizational vision for innovation with data to drive business outcomes

Create tangible deliverables to accelerate strategic databases, analytics, and ML initiatives

Define the first pilot, learn, and build

Leave with an architecture, working prototype, path to production, and deeper knowledge of AWS services

Jumpstart the data flywheel

Come with an idea, leave with a solution

Thank you

Appendix

Improving Clinical Trials to Cure Cancer: Fred Hutchinson Podcast on Amazon Comprehend

Challenge The mission of Fred Hutchinson Cancer Research Center is the elimination of cancer and related diseases as causes of human suffering and death. For cancer patients and the researchers dedicated to curing them, time is the limiting resource. The process of developing clinical trials and connecting them with the right patients requires research teams to sift through and label mountains of unstructured clinical record data.

Solution With Amazon Comprehend Medical's Entity extraction API built for Health, Fred Hutch can extract disease conditions, medications, treatment outcomes, or PHI from medical records for each patient to measure a operational metric.

Listen to the podcast

“

When I think about the real win of natural language processing and machine learning in the clinical domain, it’s really about speeding things up.

Emily Silgard, Data Science Manager

Company: Fred Hutchinson Cancer Research Center Country: US Employees: 3,500 Website: FredHutch.org

About Fred Hutch At Fred Hutchinson Cancer Research Center, home to three Nobel laureates, interdisciplinary teams of world-renowned scientists seek new and innovative ways to prevent, diagnose and treat cancer, HIV/AIDS and other life-threatening diseases. Fred Hutch’s pioneering work in bone marrow transplantation led to the development of immunotherapy, which harnesses the power of the immune system to treat cancer.

JP Morgan Chase: Enterprise wide innovation with data + control “Most modern organizations recognize that their data benefits their entire enterprise. Data has value to the individual business process that produces it, but data’s additional potential can be realized when it’s combined with other data assets.” Anu Jain – Head of Enterprise Data Technology

• JPMC is comprised of multiple lines of business (LoBs) and corporate

functions (CFs) that span the organization.

• The regulated nature of the industry requires effective data risk

management with controls to mitigate exposure.

Challenge • Needed to enable data consumers across JPMC’s LoBs and CFs to

more easily find and obtain the data they need.

• Whilst maintain control and visibility of data usage.

Enabled easy discoverability and data sharing across the enterprise

Outcome • • Gave data owners control and visibility to managing their data effectively Cataloguing provides a single point of visibility for where data is used •

https://aws.amazon.com/blogs/big-data/how-jpmorgan-chase-built-a-data-mesh-architecture-to-drive-significant-value-to-enhance-their-enterprise-data-platform/

Balance governance with Agility

A health IT company providing solutions to empower clinicians, and patients, is a great example of modern data governance automation.

Solution •

Ingest widely distributed patient and hospital data

• Near-real-time predictions about patient care and hospital operations e.g.

hospital capacity and length of patient stay

• Data privacy is central to the solution, allowing researchers to work on de-

identified patient health data

• HIPAA compliance to adequately safeguard protected health information

(PHI).

Managing 27,000 facilities

Optimize unique needs of 150M individuals

Aggregating EHR, Claims, and Personal Data to create a consolidated, longitudinal personal record

Personalized reminders

Pace of innovation reduced from 6 months to 4 weeks