| Data Driven Organization |
| The Modern Data Community |
|
|
| Steve Cooper, Worldwide Lead Data-Driven Everything (D2E) |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| “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.” |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| ( Mindset + People + Process ) |
| x Technology |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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 |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| The Modern Data Organization |
| Breaking the monolith |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| A shift to microservices |
|
|
| • Decoupled architecture of single-purpose |
|
|
| services |
|
|
| • Business logic and data only accessible |
|
|
| through hardened APIs |
|
|
| • |
|
|
| Increased speed, agility, and innovation |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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 |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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. |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| Amazon Kinesis Data Streams |
|
|
| Amazon Redshift |
|
|
| AWS Glue |
|
|
| Amazon Athena |
|
|
| Amazon S3 |
|
|
| Amazon SageMaker |
|
|
| Data Producers |
| Data product owners |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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 |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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 |
|
|
| s |
|
|
| 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 |
|
|
| 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 |
|
|
| s |
|
|
| m |
| a |
| e |
| T |
| d |
| e |
| s |
| a |
| B |
| - |
| y |
| t |
| i |
| v |
| i |
| t |
| c |
| A |
|
|
| s |
|
|
| 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 |
|
|
| 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 |
|
|
| i |
|
|
| Jan |
|
|
| Dec |
|
|
| e |
| l |
| c |
| y |
| C |
| y |
| r |
| e |
| v |
| i |
| l |
| e |
| D |
|
|
| e |
| l |
| c |
| y |
| C |
| g |
| n |
| d |
| n |
| u |
| F |
|
|
| i |
|
|
| M |
| V |
| P |
|
|
| M |
| V |
| P |
|
|
| M |
| V |
| P |
|
|
| $$ |
| $ |
| Jan May Sept |
|
|
| $ |
|
|
| Risk |
|
|
| Risk |
|
|
| Risk |
|
|
| Risk |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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. |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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 |
|
|
| S3 |
|
|
| 100PB |
|
|
| Platforms |
| From maintenance to customer experience |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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 |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| • 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. |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| AWS Lake Formation |
|
|
| Amazon S3 |
|
|
| Amazon Redshift |
|
|
| Amazon Athena |
|
|
| Amazon QuickSight |
|
|
| AWS Glue |
|
|
| Amazon SageMaker |
|
|
| Consumers |
| Diversity and high velocity decision making |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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 |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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 |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| Amazon Kinesis Data Firehose |
|
|
| AWS Glue |
|
|
| Amazon SageMaker |
|
|
| Automation |
| Invent and simplify a balance between |
| governance and agility |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| Ingest at speed |
|
|
| • Balancing governance and agility |
| • |
| • Detecting quality issues |
| • Understanding data at scale |
| • Helping consumers find data |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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 |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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 |
|
|
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
|
|
| 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 |
| |
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
| |
| Getting Started |
| |
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
| |
| 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. |
| |
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
| |
| 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 |
| |
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
| |
| Thank you |
| |
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
| |
| Appendix |
| |
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
| |
| 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 |
| |
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
| |
| 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/ |
| |
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
| |
| 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). |
| |
| © 2021, Amazon Web Services, Inc. or its Affiliates. |
| |
| 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 |
| |
| |
