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Data-Centric Business Transformation Using Knowledge Graphs

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From a talk at the Data Architecture Summit in Chicago in 2018--reviews digital transformation and what deep transformation really implies at the data layer. Cross-enterprise knowledge graphs are becoming feasible and can be a key enabler of deep transformation.

Published in: Data & Analytics
  • Video and deck of an earlier related talk can be found here: https://lnkd.in/gXxhaMD and https://www.slideshare.net/AlanMorrison/collapsing-the-it-stack-clearing-a-path-for-ai-adoption
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Data-Centric Business Transformation Using Knowledge Graphs

  1. 1. Data-centric architecture in business transformation Data Architecture Summit 2018 Chicago, IL https://www.pwc.com/us/en/services/consulting/technology/emerging-technology.html October 11, 2018
  2. 2. PwC | Data-Centric architecture in business transformation Covered in today’s talk • Introduction: Scoping the opportunity • Transformation styles and their limitations • Innovation types • Deep transformation and a path to AI • Transformative knowledge graphs • Conclusion: Pitching to the boss 2PwC | Data-Centric architecture in business transformation 2
  3. 3. PwC | Data-Centric architecture in business transformation Introduction – Scoping the opportunity 3PwC | Data-Centric architecture in business transformation 3
  4. 4. PwC | Data-Centric architecture in business transformation Source of a widening corporate inequality gap Top 20 public/private internet companies by market valuation ($B) 4
  5. 5. PwC | Data-Centric architecture in business transformation Widening corporate inequality examples – Top versus bottom of global 100 5 337 329 417 559 416 469 725 604 754 851 40 61 69 64 70 81 85 76 88 97 0 200 400 600 800 1000 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Market caps of top and bottom companies Number 1 Number 100 8,402 17,438 20,035 0 3,000 6,000 9,000 12,000 15,000 18,000 21,000 24,000 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Total market cap of Top 100 companies as at 31 March Apple Apple Apple Apple AppleApple AppleExxon MobilPetroChina Exxon Mobil MarketCap($bn)MarketCap($bn)
  6. 6. PwC | Data-Centric architecture in business transformation Largest changes in market cap by global company cross industry, 2018 6 (1) Change in market cap from IPO date (2)Market cap at IPO date Source: Bloomberg and PwC analysis Company name Location Industry Change in market cap 2009-2018 ($bn) Market cap 2018 ($bn) 1 Apple United States Technology 757 851 2 Amazon.Com United States Consumer Services 670 701 3 Alphabet United States Technology 609 719 4 Microsoft Corp United States Technology 540 703 5 Tencent Holdings China Technology 483 496 6 Facebook United States Technology 383(1) 464 7 Berkshire Hathaway United States Financial 358 492 8 Alibaba China Consumer Services 302(1) 470 9 JPMorgan Chase United States Financials 275 375 10 Bank of America United States Financials 263 307 Known knowledge graph builders Operator of Taobao and AliBot KG builder v Known KG builders
  7. 7. PwC | Data-Centric architecture in business transformation Transformation styles and their limitations 7PwC | Data-Centric architecture in business transformation 7
  8. 8. PwC | Data-Centric architecture in business transformation Digital transformation – Data can get buried in the mix 8 Leadership transformation Omni-experience transformation Worksource transformation Operating model transformation Information transformation • IT strategy and governance • Leading in 3D • Strategic architecture • Services transformation • Innovation strategies • Customer experience • Device/mobility strategies • Devices: PCs, mobility, wearables, AR/VR • Social business • eCommerce • Vendor sourcing and management • IT talent and skills • Outsourcing services • IT organizational development • Technology training • Enterprise infrastructure • AppDev and app provisioning • DevOps • Cloud strategies • Transformative tech: IoT, Robotics and 3D printing • Enterprise/ nextgen security • Enterprise applications • Information and data transformation • Big data and analytics • Cognitive computing Research & Advisory Services, IDC, 2018 Digital transformation—IDC’s essential advisory themes
  9. 9. PwC | Data-Centric architecture in business transformation Banks, for example, typically choose one of three digital transformation directions 9 “Bank to the future: Finding the right path to digital transformation,” PwC FSI White Paper, 2018
  10. 10. PwC | Data-Centric architecture in business transformation Wrap and digitize allows component-by-component transformation 10 “Bank to the future: Finding the right path to digital transformation,” PwC FSI White Paper, 2018
  11. 11. PwC | Data-Centric architecture in business transformation Bigger picture transformation – Moving to where the new business will be, with a new data foundation 11 • The shape of digital business is radically different than what’s come before. • In order to compete, companies will have to move to where the new opportunities are. • Relationship-rich data at scale makes it possible to get to these opportunities. • Knowledge graph models as a base for digital business makes scaling relationship-rich data possible.
  12. 12. PwC | Data-Centric architecture in business transformation Innovation types 12PwC | Data-Centric architecture in business transformation 12
  13. 13. PwC | Data-Centric architecture in business transformation Most innovations are incremental, adding to the stack, with data as an afterthought (Type I innovation) 13 Hardware DBMS OS Custom code Hardware Lots of OSes 1,000+ SQL/ NoSQL DBs Custom code ERP+ suites Hardware A few more OSes More DBMSes Custom code ERP+ suites Hardware Lots more OSes 5,000+ databases Component - ized suites Custom code Cloud layer Hardware More types of OSes 10,000+ DBs + blockchains Multicloud layer Suites as services Various SaaSes Custom code Hardware A few DBMSes A few OSes ERP+ suites Custom code Threat of more application centric sprawl Early1990s Late 1990s 2000s 2010s1973-1990sPre 1970 2020s
  14. 14. PwC | Data-Centric architecture in business transformation Most of the IT workforce just adds to or keeps track of the sprawl 14 0.01 0.9 2 1.6 3.2 US IT workforce in 2016 (in mIllions) Semantic data Data related (less semantics) General Network/hardware Software Total IT workforce = 7.7 million (= 5 percent of the US overall workforce in 2016) Sources: US Bureau of Labor Statistics and PwC estimates, 2018
  15. 15. PwC | Data-Centric architecture in business transformation The US as a whole has more opioid abusers than it does IT workers 15 0 50 100 150 200 250 300 350 IT workers All opioid abusers Disabled and over 65 years old Entire US workforce Entire US population Number of IT workers in the US in 2016, in context (in millions) US Census Bureau, Bureau of Labor Statistics, and Health and Department of Human Services, 2018
  16. 16. PwC | Data-Centric architecture in business transformation Object virtualization (Type II) manages complexity, just so IT can get its arms around the sprawl 16 EnterpriseWeb and PwC, 2015
  17. 17. PwC | Data-Centric architecture in business transformation Type III – Data-centric architecture reduces both application and database sprawl 17 Applications for execution only Models exposed with the dataTrapped app code and databases Application centric Data centricversus Data lake or hub Semantic model/rules   Applets
  18. 18. PwC | Data-Centric architecture in business transformation Identify and declare the few hundred business rules you need as a model 18 “In every company I’ve ever studied, there are only a few hundred key concepts and relationships that the entire business runs on. Once you understand that, you realize all of these millions of distinctions are just slight variations of those few hundred important things.” --Dave McComb, author of Software Wasteland, quoted in Strategy + Business See “Are you Spending Way too Much on Software at https://www.strategy-business.com/article/Are-You-Spending- Way-Too-Much-on-Software?
  19. 19. PwC | Data-Centric architecture in business transformation Call the model to reuse those rules whenever necessary 19 “You discover that many of the slight variations aren’t variations at all. They’re really the same things with different names, different structures, or different labels. So it’s desirable to describe those few hundred concepts and relationships in the form of a declarative model that small amounts of code refer to again and again.” --Dave McComb (as previously cited) See “Are you Spending Way too Much on Software at https://www.strategy-business.com/article/Are-You-Spending- Way-Too-Much-on-Software?
  20. 20. PwC | Data-Centric architecture in business transformation Deep transformation and a path to AI 20PwC | Data-Centric architecture in business transformation 20
  21. 21. PwC | Data-Centric architecture in business transformation What AI needs versus what it has What it needs: Contextualized, disambiguated, highly relevant and specific integrated data, flowing to the point of need What it has: Single batch datasets cleaned up to be good enough by data scientists, who spend 80% of their time on cleanup What it needs: Knowledge engineers, and many bold Data Visionaries in addition to big D Data Scientists, data-centric architects, pipeline engineers, specialists in many new data niches What it has: A growing group of tool users versed only in probability theory, neural networks, python and R, including small D data scientists, engineers and architects, plus scads of entrenched application-centric developers 21 Finance Operations Marketing Input Output Input layer Hidden layer 1 Hidden layer 2 Output layer
  22. 22. PwC | Data-Centric architecture in business transformation The real inhibitors to adoption aren’t technological – they’re rooted in tribal biases and resistance to change 22 Tribalism CollectivismIndividualism Anarchy TotalitarianismLocus of inertia Daniel Quinn, Beyond Civilization and Alice Linsley, “Daniel Quinn: A Return to Tribalism?”, college-ethics.blogspot.com, 2018
  23. 23. PwC | Data-Centric architecture in business transformation Tribalism – Machine learning edition 23 Symbolists Bayesians Connectionists Evolutionaries Analogizers Use symbols, rules, and logic to represent knowledge and draw logical inference Assess the likelihood of occurrence for probabilistic inference Recognize and generalize patterns dynamically with matrices of probabilistic, weighted neurons Generate variations and then assess the fitness of each for a given purpose Optimize a function in light of constraints (“going as high as you can while staying on the road”) Favored algorithm Rules and decision trees Favored algorithm Naïve Bayes or Markov Favored algorithm Neural network Favored algorithm Genetic programs Favored algorithm Support vectors Source: Pedro Domingos, The Master Algorithm, 2015 More at “Machine learning evolution”: http://usblogs.pwc.com/emerging-technology/machine-learning-evolution-infographic/, PwC, 2017
  24. 24. PwC | Data-Centric architecture in business transformation Tribalism – Data integration edition 24 Trend toward more data centricity this way Application-centric RESTful developers Relational database linkers Data-centric knowledge graphers Application-centric ESB advocates Computerscience wiki.org, 2018 TIBCO, 2014 Oracle DBA’s Guide, 2018 User Scott Select FROM emp Local database PUBLIC SYNONYM Emp - > emp@HQ.ACME >COM Database link (undirected) Remote database EMP table Portals Net Application B2B Interactions Enterprise data Business process management Web Services Mobile Applications DEE Application ERP CRM SFA Legacy System ESBCustom environment Common environment API Unstructured Data Semi- structured Data Structured Data Schema mapping based on ontologies Entity Extractor informs all incoming data streams about its semantics and links them Unified Views RDF Graph Database PoolParty Graph Search Semantic Web Company, 2018
  25. 25. PwC | Data-Centric architecture in business transformation Crossing the chasm between the tribes Reducing the amount of unfamiliarity developers confront--familiar document means to achieve comparable ends to graph: • Semantic suites that use the JSON format and familiar hierarchies: SWC’s PoolParty is an example • GraphQL: A popular document shape language that talks to APIs using SELECT-like statements and tree shapes; backend-agnostic; just uses a mental model for graph; addresses the API endpoint proliferation problem • Accessible web as database methods: JSON-LD and Schema.org, etc. vocabularies • Document “schemas” via data objects: JavaScript objects to developers = documents to NoSQL DB types; Object Data Modeling instead of database semantics • Mongoose or MongoDB JSON schema features + GraphQL: MongoDB object modeling and querying that can be used for subdocument filtering within a GraphQL context • HyperGraphQL: A GraphQL UI for Linked Data, restricted to certain tree-shaped queries • Universal Schema Language: Mike Bowers’ document/graph query and modeling language still in development • COMN: Ted Hills’ well-defined NoSQL + SQL data modeling notation 25
  26. 26. PwC | Data-Centric architecture in business transformation Transformative knowledge graphs 26PwC | Data-Centric architecture in business transformation
  27. 27. PwC | Data-Centric architecture in business transformation Types of logic most used in AI – Enabled systems 27 Rule-based systems (includes KR) “Handcrafted knowledge” is the term DARPA uses; rule-based programming + procedure replication in process automation, + some knowledge representation (KR) • Strong on logical reasoning in specific concrete contexts - Procedural + declarative programming + set theory, etc. - Deterministic • Can’t learn or abstract • Still exceptionally common and useful Statistical machine learning • Probabilistic • From Bayesian algorithms to neural nets (yes, deep learning also) • Strong on perceiving and learning (classifying, predicting) • Weak on abstracting and reasoning • Quite powerful in the aggregate but individually (instance by instance) unreliable • Can require lots of data Contextualized, model-driven approach • Contextualized modeling approach—allows efficiency, precision and certainty • Combines power of deterministic, probabilistic and description logic • Allows explanations to be added to decisions • Accelerates the training process with the help of specific, contextual human input • Takes less data Example: Consumer tax software Perceiving Learning Abstracting Reasoning Perceiving Learning Abstracting Reasoning Perceiving Learning Abstracting Reasoning Example: Facial recognition using deep learning/neural nets Example: Explains first how handwritten letters are formed so machines can decide based on these individual models—less data needed, more transparency. Previously dominant On the rise and rapidly improving Nascent, just beginning John Launchbury of DARPA (https://www.youtube.com/watch?v=N2L8AqkEDLs), Estes Park Group and PwC research, 2017
  28. 28. PwC | Data-Centric architecture in business transformation Transformation scalability – The AirBnB knowledge graph example “In order to surface relevant context to people, we need to have some way of representing relationships between distinct but related entities (think cities, activities, cuisines, etc.) on Airbnb to easily access important and relevant information about them…. These types of information will become increasingly important as we move towards becoming an end- to-end travel platform as opposed to just a place for staying in homes. The knowledge graph is our solution to this need, giving us the technical scalability we need to power all of Airbnb’s verticals and the flexibility to define abstract relationships.” --Spencer Chang, AirBnB Engineering 28 Events Neighborhoods Tags Restaurants Users Homes Experiences Places Airbnb Engineering, 2018 Markets
  29. 29. PwC | Data-Centric architecture in business transformation Most automated knowledge graph – Diffbot? “Diffbot’s crawler regularly refreshes the DKG with new information and its machine learning algorithms are smart enough to pass over sites with histories of producing ‘logically inconsistent’ facts. “‘That’s one of the reasons why we fuse information together from different sources,’ Tung said. ‘Our scale is such that there’s minimal potential for errors. We’d bet the business on it.’ “Diffbot launched in 2008 and counts 28 employees among its core staff of engineers and data scientists.” --Mike Tung of Diffbot, quoted in VentureBeat Diffbot claims an automated knowledge graph of 1 trillion + facts, designed to grow without humans in the loop. That compares with 1.6 billion crowdsourced facts in Google’s knowledge graph, according to VentureBeat. 29 Kyle Wiggers, “Diffbot launches AI-powered knowledge graph of 1 trillion facts about people, places, and things,” VentureBeat, 30 August 2018
  30. 30. PwC | Data-Centric architecture in business transformation Versus more explicit, precise, contextualized meaning with a triadic, Peircean knowledge graph and less than 1M concepts? “There are many different approaches for distinguishing a logical basis for ontologies, but Peirce basically says to base everything around 3s, explains [Mike Bergman of Cognonto]. That is, 1. the object itself; 2. what a particular agent perceives about the object; 3. and the way that agent needs to try to communicate what that is. ‘Without that triad it’s hard to ever get at differences of interpretation, context or meaning,’ he says, whether that be between something like events and activities or individuals and classes. Once you adopt that mindset, a lot of things that seemingly were irreconcilable differences begin to fall away, and the categorization of information becomes really very easy and smooth....” --Mike Bergman of Cognonto, quoted in Dataversity 30 Jennifer Zaino, “Cognonto Takes On Knowledge-Based Artificial Intelligence,” Dataversity, 23 November 2016
  31. 31. PwC | Data-Centric architecture in business transformation Contextual AI via a large knowledge graph at Fairhair.ai 31 Media Intelligence Apps Global Monitoring Analyze & Report Distribute Influence & Engage New Apps Employee App Freemium At-Powered Reporting Outside Insight Enterprise Solutions Custom Solutions 3rd party Apps PaaS 100M documents ingested daily 150 NLP/IR pipelines 100’s Billions of Searches Service Layer Context Building Enriching & Analysis Outside Data Streaming, Search, Analytics, APIs Building block to leverage the platform Knowledge Graph Enable cognitive applications on top of our data by connecting the dots Data Enrichment Platform Enrich, analyze & build by interoperating with all major players AI-driven data Acquisition Bring high quality outside to our repository with minimal human effort
  32. 32. PwC | Data-Centric architecture in business transformation Montefiore’s semantic data lake 32 HL7 feed Web services EMR LIMS Legacy OMICs CTMS Claims Annotation engine HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop HDFS Hadoop AllegrographAllegrographAllegrographAllegrograph Allegrograph SDL loader ML-LIB/R SPARQL Prolog Spark Java API Various data sources, some structured, some not, now all part of a knowledge graph with a simple patient care- centric ontology Hadoop cluster with high- performance processors and memory Scalable graph database supporting open W3C semantic standards Standard open source querying,ML and analytics frameworks, API accessibility Doctors can query the graph or harness ML + analytics and receive answers from the system at the point of care via their handhelds. The system also acts as a giant feedback-response or learning loop which learns from the data collected via user/system interactions. Montefiore Health, Franz, Intel and PwC research, 2017
  33. 33. PwC | Data-Centric architecture in business transformation Siemens’ industrial knowledge graph 33 AI Algorithms 1 09:00 – Analyze Turbine data hub 2 11:00 – Configure Configure turbine 3 12:00 – Maintain Master data Mgmt. 4 13:00 – Mitigate Financial Risk Analysis 5 15:00 – Contact Expert & Communities 6 18:00 – Guide Rules & Regulations 3 4 5 4 2 1 6 Industrial Knowledge Graph “Deep learning fails when it comes to context. Knowledge graphs can handle context and enable us to address things that deep learning cannot address on its own.” --Michael May, Head of Company Core Technology, Data Analysis and AI, Siemens
  34. 34. PwC | Data-Centric architecture in business transformation Pharma knowledge graphs for patient safety Challenges 34 Solutions Drug safety Heightened focus on safety Evolving regulatory demands Increasing public scrutiny Focus on analytics Increased sharing & transparency Doing more with the same or less Graph integration Natural language processing Data cleaning during analysis In-memory query engine PwC and Cambridge Semantics, 2018
  35. 35. PwC | Data-Centric architecture in business transformation NuMedii’s precision therapeutics knowledge graph 35 goTerm Calcium ion binding 2201 Protein binding Extracellular region ENSG 00000138829 Extracellular matrix disassembly Extracellular matrix Organization proteinaceous extracellular matrix positive regulation of bone mineralization Fibrillin - 2 Extracellular matrix Structural constituent Extracellular matrix micro fibril Camera-type eye development CHEMBL_TC_ 10038 Go Function Reference_ Gosubset prok 100001650 100001532 100001739 100000687 100002060 Ontotext and NuMedii, 2018
  36. 36. PwC | Data-Centric architecture in business transformation Thomson Reuters’ financial knowledge graph as a service 36 Thomson Reuters, 2018
  37. 37. PwC | Data-Centric architecture in business transformation Conclusion – Pitching to the boss (in five minutes) 37PwC | Data-Centric architecture in business transformation 37
  38. 38. PwC | Data-Centric architecture in business transformation “What’s a knowledge graph?” Starting your 5-minute pitch Minute 1: Define and describe the value proposition of graphs. • Graphs articulate relationship-rich data • Compare graphs with tables: Relationships are what’s missing from most data • Describe relationships using graphs so machines can mine them - essential to digitization • Only way to build web-scale context in data is to enrich relationships • Without context, your organization can’t mature to advanced analytics or real AI • Document trees (e.g., taxonomies) are a stepping stone to graph models • Graphs are the parents that bring the data model family all together, Tinker Toy-style • Bottom line: Knowledge graph = a machine readable, extensible model of your organization • Build and maintain your advanced analytics/ AI foundation with that graph model 38 Graph: Any-to-any relationships Document: Nested, cumulative hierarchies Relational: Row and column headers and up-front taxonomies Relationship richness Relationship sparseness Static Selective Fragmented Labor intensive Additive Index friendly Immutable versioning possible More dynamic More inclusive More integrated More machine assisted
  39. 39. PwC | Data-Centric architecture in business transformation “Who’s using knowledge graphs?” Only 9 out of 10 of the most value-creating companies in the world 39 Company name Location Industry Change in market cap 2009-2018 ($bn) Market cap 2018 ($bn) 1 Apple United States Technology 757 851 2 Amazon.Com United States Consumer Services 670 701 3 Alphabet United States Technology 609 719 4 Microsoft Corp United States Technology 540 703 5 Tencent Holdings China Technology 483 496 6 Facebook United States Technology 383(1) 464 7 Berkshire Hathaway United States Financial 358 492 8 Alibaba China Consumer Services 302(1) 470 9 JPMorgan Chase United States Financials 275 375 10 Bank of America United States Financials 263 307 v Known knowledge graph builders Operator of Taobao and AliBot KG builder Known KG builders Minute 2: List top users and explain why they’re using knowledge graphs. • Apple using knowledge graph for Siri for years, doubling down on Siri knowledge engineers • Alibaba's AliMe bot platform uses linked general and d0main-specific ontologies • Amazon hiring knowledge engineers for Alexa ML training set acceleration • Tencent one of the funders of Diffbot, an automated knowledge graph with 1 trillion facts • Both Facebook and Google accelerate and enrich ML training set dev with their graphs • B of A and Citibank both invested in the development of FIBO (1) Change in market cap from IPO date (2) Market cap at IPO date Source: Bloomberg and PwC analysis
  40. 40. PwC | Data-Centric architecture in business transformation Innovation at scale can happen when machines learn from graphs Minute 3: Machine learning on its own can’t build context. Top organizations understand that. • Humans need to help machines identify and use context via the graph model of the organization • Properly designed, the model can be extensible and scalable with humans and machines working together • Contextual computing is the third wave of AI, according to DARPA—need to reposition for that wave 40
  41. 41. PwC | Data-Centric architecture in business transformation With a knowledge graph base, companies can skate to new business models = Deep transformation Minute 4: • Once relationship data-enabled, organizations play different roles than they've been accustomed to in the digitized ecosystem. • Some because of their data collection heritage can become data providers. • Others take up roles in the data supply chain, or position themselves as industry platforms or marketplaces. • Why are top companies able to cross industry boundaries? • Why can unicorns extend the reach of their business models? 41
  42. 42. PwC | Data-Centric architecture in business transformation How transformative knowledge graphs work in practice – Leading examples Minute 5: Tell the stories of key companies and their knowledge graph journeys 42 Three deep transformation examples AirBnB: Pursuing the goal of becoming an end-to-end travel platform Montefiore Health: Moving to a pay-for-performance business model and a new level of profitability Siemens: Scaling smart building with the help of knowledge graphs Three intelligent assistant examples Alibaba: Extending the IA footprint of AliBot in general-purpose customer service with graph models Amazon: Boosting the performance of Alexa, particularly improved interpretation and response Apple: Acquired Lattice Data in 2017, investing in new Siri knowledge engineering workforce hires in 2018 Companies like these are already skating to new growth and profitability. Data, many say, is the new oil. But most importantly, relationship-rich data is the lubricant for your digital business engine.
  43. 43. PwC | Data-Centric architecture in business transformation Bonus points – Deep transformation via knowledge graph is also a path to scalable data management and security • Relationship data has long been overlooked, but specifying relationships is how you build context • Connected, relationship-rich data will be seen as the most important asset for companies • Can’t have governance without connected data, because disconnected data = sprawl • Can’t have security if you can’t find or scale the unique identification and contextualization of your data, so you can detect, track, trace, and know where it belongs • Can’t have connected, meaningful data without a semantic model • Can’t compete in the digital ecosystem and cross boundaries without meaningful data connectivity • When it comes to enabling the scale and level of AI your company needs, including for data management and security, knowledge graph + machine learning clears a path 43
  44. 44. PwC | Data-Centric architecture in business transformation Result – Graphs (including hybrids) complete the picture of your transformed data lifecycle and how it’s managed 44
  45. 45. Questions or comments? © 2018 PwC. All rights reserved. PwC refers to the US member firm or one of its subsidiaries or affiliates, and may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see www.pwc.com/structure for further details. pwc.com Alan Morrison Sr. Research Fellow PwC | Integrated Content | Emerging Tech Mobile: +1(408) 205 5109 Email: alan.s.morrison@pwc.com PricewaterhouseCoopers LLP 488 Almaden Blvd., Suite 1800 San Jose, CA 95110 USA https://www.linkedin.com/in/alanmorrison @AlanMorrison quora.com/profile/Alan-Morrison

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