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Data centric business and knowledge graph trends


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The deck for my kickoff keynote at the Data-Centric Architecture Forum, February 3, 2020. Includes related data, content, and architecture definitions and fundamental explanations, knowledge graph trends, market outlook, transformation case studies and benefits of large-scale, cross-boundary integration/interoperation.

Published in: Data & Analytics

Data centric business and knowledge graph trends

  1. 1. Alan Morrison Data-Centric Architecture Forum Fort Collins, Colorado February 2020 Data-centric business and the knowledge graph
  2. 2. PwC | Data-Centric business and the knowledge graph 1. Defining “data”, “content,” etc. 3 2. Defining data centricity 9 3 Why the market will need data-centric approaches 17 4. Transformative case studies that point the way forward 29 5. Conclusion: Long-term benefits of a new data foundation 48 Agenda 2
  3. 3. Defining “data”, “content,” etc.
  4. 4. PwC | Data-Centric business and the knowledge graph What is data? Not surprisingly, opinions differ. Data science data prep answer Noisy text, audio or images that needs to be scraped from the source, scrubbed, flattened, filtered, clustered and shoehorned into tables for quick, one- time processing. Messy data that doesn’t fit constitutes noise. Knowledge science answer Living organic matter that can be embraced and enriched. Reuse with labels rather than tables by modeling the context using shared standards. Then blend the model and the instance data together in a graph. This allows continual enrichment of relevant relationships via inferencing—a living, growing graph of understanding. A chemical engineer’s approach A soil scientist’s approach 4
  5. 5. PwC | Data-Centric business and the knowledge graph Content management = data management Content = Meaningful, human-readable data + logic in the form of text, images, audio, video (or combinations of these) Knowledge graphs = Meaningful, machine readable data + logic in the form of modeled, any-to-any connected, contextualized entities, their properties and relationships Content can be modeled and then read by machines the same way as other data + logic. The same techniques can apply.
  6. 6. PwC | Data-Centric business and the knowledge graph What is a knowledge graph, and how are knowledge graphs evolving? 6 Multi-model: KV, RDB, doc, graph Document- graph hybrids Named property graphs Semantic labeled, directed graphs (RDF/OWL) Multi-model knowledge graph-- RDF as nurturing parent, rest as unruly childrenRDF-based open standards knowledge graph schema:Person schema:Organization mi6:JamesBond mi6:worksFor mi6:MI6 . mi6:JamesBond rdf:type schema:Person . mi6:MI6 rdf:type schema:Organization . rdf:type rdf:type mi6:JamesBond mi6:MI6 mi6:worksFor TopQuadrant, 2019 @ArthurAKeen and Jan Stuecke @arangodb, 2020
  7. 7. PwC | Data-Centric business and the knowledge graph Data model evolution is picking up as well ADEPT: includes A fixed schema of 7 relational “channels” and 40 building blocks “Sense” of the channels: Thing = the part Use = the whole Variety = the symbol Word = the meaning Subject = the individual Object = the group Source = the context Source: Greg Sharp of ADEPT, 2020 Planck Inst. mathematicians now representing The Periodic Table of Elements as a series of hypergraphs. This particular representation depicts the elements related by their chemical bonds. Source: Formal structure of periodic system of elements Wilmer Leal and Guillermo Restrepo, Proceedings of the Royal Society A, 03 April 2019 7
  8. 8. PwC | Data-Centric business and the knowledge graph As a hypergraph modeling medium, powers the OpenCTI (Cyber Threat Intelligence) knowledge sharing platform. Source: and, 2019 and 2020 8
  9. 9. Defining data centricity
  10. 10. PwC | Data-Centric business and the knowledge graph What is data-centric? • Not application-centric • Places a focus on the full data lifecycle • Allows emancipation and reuse of semantics and logic trapped in applications • Enables rationalization of most code and data repositories in information systems 10
  11. 11. PwC | Data-Centric business and the knowledge graph The problem of logic and data siloing – App-centric system-level complexity and disconnectedness spinning out of control (Result – Table and code sprawl) 11 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 Componentized 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
  12. 12. PwC | Data-Centric business and the knowledge graph The solution – Data-centric architecture reduces both application and database sprawl 12 Trapped app code and databases Application centric versus Data centric Semantic model/rules Data lake or hub Applets    Applications for execution only Models exposed with the data
  13. 13. PwC | Data-Centric business and the knowledge graph Data centricity allows scalable data modeling alternatives 13 1. Relational databases don’t treat relationship data as a first-class citizen 2. As a result, most companies have buried or are missing the relationship data they need for contextualization 3. Tables alone don’t help you dynamically model your data or share the models 4. Managing large numbers of tables soon gets unwieldy 5. Limiting your database resources to tabular methods ensures you won’t take full advantage of today’s compute, networking and storage Relationship richness Relationship sparseness Static selective fragmented labor intensive Additive Index friendly Immutable versioning possible More dynamic More inclusive More integrated More machine assisted Relational: Row and column headers And up-front taxonomies Document: Nested, cumulative hierarchies Graph: Any-to-any relationships PwC, 2016 When overused, RDBMSes perpetuate the provincial data mentality of the 1980s, back when computing didn’t scale Lots of data is missing from relational datasets—namely the contextual clues needed for disambiguation via entity resolution and, therefore, large-scale integration
  14. 14. PwC | Data-Centric business and the knowledge graphPwC The mobile data flood level keeps rising…. 14
  15. 15. PwC | Data-Centric business and the knowledge graphPwC …and not all data can or should be archived or reused 15 Data model type Key-value/Column Document Graph StreamCapture/Collate Aggregate Transact Network` Reuse NewSQL + Blockchain Raw Less structured Perishable Single-use Massive Refined Structured Less perishable Many uses Nurtured Long-term reusabilitySimple persistenceDataset uses Characteristics Too perishable or massive for reuse? Non-perishable and suitable for reuse?
  16. 16. PwC | Data-Centric business and the knowledge graph So what’s data-centric architecture? 16 Architecture that 1. MInimizes code sprawl and data siloing 2. Preserves and enriches semantic and other metadata (à la the soil scientist’s approach) 3. Enables coding efficiency and reusability via knowledge graph model-driven development 4. Ensures improved levels of data and logic accessibility 5. Boosts the richness, quality, efficiency, security and throughput of data to be ingested by AI systems 6. Meets or exceeds the standards on data sourcing, quality, integration/movement, persistence, master data management, metadata management and semantic reconciliation, data governance, security, and delivery set forth in the DAMA Data Management Book of Knowledge Reference sources: McComb, Dave. 2018. Software Wasteland: How the Application-Centric Mindset is Hobbling our Enterprises. McComb, Dave. 2019. The Data-Centric Revolution: Restoring Sanity to Enterprise Information Systems. The Data-Centric Manifesto at
  17. 17. Why the market will need data-centric approaches
  18. 18. PwC | Data-Centric business and the knowledge graph In the mirrorworld, everything will have a paired twin. Kevin Kelly in Wired Feb 12, 2019 June 2019 18
  19. 19. PwC | Data-Centric business and the knowledge graph What’s a digital twin? Depends on who you ask 19 • GE: “At its core, the Digital Twin consists of sophisticated models or system of models based on deep domain knowledge of specific industrial assets. The Digital Twin is informed by a massive amount of design, manufacturing, inspection, repair, online sensor and operational data.” • Goals: Predictive analytics, knowledge representation, etc. • From “What is a digital twin?” GE Digital, 2019 Finger Food, “We Are Industry-leading Digital Twin Holographic Service Providers…. Imagine taking all of your disparate data sets from multiple spreadsheets and diagrams and combining them into one live-streaming visual holographic representation of your data – at full scale.” Goals: “We can take your data from your spreadsheets and turn it into clear, actionable context like never before…” From “Digital Twin Solutions to Improve your Bottom Line,” Finger Food Advanced Technology Group,“ 2019
  20. 20. PwC | Data-Centric business and the knowledge graph Consider how long it took to build out the world’s oil & gas infrastructure. Now think about where we are with traditional data management: • How do we free ourselves from legacy IT? • How do we build sharable digital twins? • How do we scale a shared data infrastructure? The mirrorworld poses a massive global data infrastructure challenge 20
  21. 21. PwC | Data-Centric business and the knowledge graph Why treating smart data as a strategic asset is so critical right now 21 Challenge of the 2020s: Feeding your AIs enough relevant, quality data • Emerging tech often gets adopted just in pockets, • That’s particularly the case with AI. • Retraining, hiring new people, or buying more tools isn’t enough. • Many never figure out how to take advantage of important AI-enabling tech. They’ll just use it in ad- hoc projects or subscribe to AI-enhanced apps. • But the impact on decision making will be minimal without an industrial-scale approach to data and flow. Opportunity of the 2020s: Pipelines, distribution networks and volumes of quality, contextualized smart data flowing to the point of need The challenge we face is the same as the oil and gas industry faced in the 1920s: • Collecting enough raw material • Refining and enriching it • Distributing it to the places that need it most • Creating enough supply to generate massive demand and drive down the cost of AI
  22. 22. PwC | Data-Centric business and the knowledge graph Ontologies should underlay all interactive digital twins The world is a giant AnyLogic simulation, with computational physics added…. My ontology would look a lot like the AnyLogic building blocks.” --Brett Forbes, Cloud Accelerator, Jan. 2020 22 Brett Forbes, Cloud Accelerator, 2020 Supply chain GIS, AnyLogic, 2017
  23. 23. PwC | Data-Centric business and the knowledge graph Graph databases can help to encourage a smart data integration, enrichment, and reuse mentality 23
  24. 24. PwC | Data-Centric business and the knowledge graph Emerging techs – How are all these things interrelated? Are they addressable too? Knowledge graphs—the manifestation of a data- centric architecture--can empower the other technologies in these ways: 1. Accelerate machine learning training set development 2. Enable multi-domain virtual assistants/chatbots 3. Add reasoning to conversational ai platforms 4. Become means of sharing and interoperation of digital twins 24
  25. 25. PwC | Data-Centric business and the knowledge graph Emerging markets — related to most relevant hype cycle techs 25 Total projected revenue: $58.2 billion (2021) Source: Tractica, Grandview Research and PwC analysis, 2019
  26. 26. PwC | Data-Centric business and the knowledge graph Summary: A very large available market, but low awareness of how KGs can help 26 4% 5% 5% 8% 8% 9% 14% 13% 8% 26% Summary of global target markets for knowledge graph technology, 2021 Digital twins PaaS--data mgmt. DaaS (org. domain) Virtual assistants Conversational AI Deep learning PaaS--integration, orchestration Info mgmt software Integration software DBMS software Total: $205 Billion Sources: Gartner (hype cycle only), IDC, Tractica, PwC analysis, 2019
  27. 27. PwC | Data-Centric business and the knowledge graph Data-centric design at the micro level brings human and machines together, with the humans helping the machines build and scale relationship data 27 Relationship logic to shared at scale needs to be created in human-machine feedback loops and embedded in a standard form at the data layer for full reuse—not trapped in app silos Relationship- sparse, but highly articulated data context that humans need to help machines refine and enrich Relationship- rich smart data that uses description or predicate logic to scale integration, context and interoperation
  28. 28. PwC | Data-Centric business and the knowledge graph The key opportunity – Large-scale integration and model-driven intelligence in a de-siloed and de-duplicated way 28 Previously dominant 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 On the rise and rapidly improving 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 Perceiving Learning Abstracting Reasoning Perceiving Learning Abstracting Reasoning Perceiving Learning Abstracting Reasoning Example: Consumer tax software Example: Facial recognition using deep learning/neural nets John Launchbury of DARPA (, Estes Park Group and PwC research, 2017 Nascent, just beginning 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: Explains first how handwritten letters are formed so machines can decide- less data needed, more transparency.
  29. 29. Transformative case studies that point the way forward
  30. 30. PwC | Data-Centric business and the knowledge graph Banks, for example, typically choose one of three digital transformation directions 30 “Bank to the future: Finding the right path to digital transformation,” PwC FSI White Paper, 2018 Ironically, none of these, including“digital native,” implies data-centric.
  31. 31. PwC | Data-Centric business and the knowledge graph Wrap and digitize allows component-by-component transformation 31 “Bank to the future: Finding the right path to digital transformation,” PwC FSI White Paper, 2018 Properly utilized, cross- enterprise knowledge graphs would broaden and deepen the impact of existing transformation efforts considerably. Thing is, so few people are aware of the inhibiting effects of an application-centric architecture and how radically a true data- centric alternative could improve matters.
  32. 32. PwC | Data-Centric business and the knowledge graph Merck Group regulatory pharma graph data management 32 Goals: End-to-end regulatory MDM • Compliance, incl. traceability • Harmonizing data sources and related processes • Informs risk management, development, lifecycle mgmt. • Overall business agility Means: ISO controlled vocabularies + Cambridge Semantics Anzo • Map structured and unstructured data • Enrich with semantics • Open standards for interoperability Open question: • How can these methods blend with blockchain supply chain strategy?Courtesy of Cambridge Semantics, 2020
  33. 33. PwC | Data-Centric business and the knowledge graph Ericsson’s connected data logistics chain 33 Producer Terminal operator Shipping line Customs Declarant Trucking company Warehouse Last mile Consumer Confirm move Payment ETA of shipment ETA of container Order Inspection results Booking Transport order Export declaration Import declaration Packing list B/L Confirmation Alison Goodrum, Stardog, 2019
  34. 34. PwC | Data-Centric business and the knowledge graph Goals: 1. Relationship enable the integration of a dozen disparate datasets that contain clues about the 10,000+ different skills PwCers offer. 2. Unify the integration with a purpose-built graph data model, or ontology. 3. Deliver uniquely relevant answers to questions about skills and abilities of interest. Means: Tap the ontology building and expand AI enabling expertise of our partner Semantic Arts + Franz Interview stakeholders who Gain access to each data source. Work with the Cross-Line of Service Data Platform (XDP) team to ingest all data sources using Workbench. Design and build the context and integration graph. Provide natural language means of querying the graph and retrieving results (such as via chatbot). Challenges: • Access to data sources requires finding the owners of the source, negotiating access and obtaining either excerpts or live access • Many sources could be off limits or considered too sensitive, even for a short-term pilot • The most useful data could be social media data that platform owners and others habitually mine, and yet…. • Are data protection laws putting those sources beyond reach of a regulated entity such as PwC? Expertise location knowledge graph pilot for PwC US November 2019 – June 2020 34
  35. 35. PwC | Data-Centric business and the knowledge graph Step One: Collect enough of the right kinds of data for the purpose • Morgan Stanley understood the Operational Risk function did not have enough data to work with. • Accordingly, it created applications to capture the data it needed. • However, the siloed data lacked connections, relationship richness and a larger business context. Step Two: Establish a way to home in even on weak, but suspicious signals in relevant, relationship-rich data across applications via a risk ontology. • It also knew important information could be revealed that was not originally explicit in the data. • MS realized the necessity of uncovering the kinds of connections that exist when assessing operational risks. Step Three: Build a flexible, scalable, and reusable weak- signal detection and analysis platform. • MS grasped the need for creating a platform, processes and enriched, curated, validated data that could be shared and reused. • An ontology was key to revealing and being able to analyze even weak, infrequent relationships in a shared, reusable way. Goal of Morgan Stanley risk modeling and integration: Build the context from the data, ID relationships and mine for fresh, relevant connections in previously siloed data, in a reusable, flexible way 35
  36. 36. PwC | Data-Centric business and the knowledge graph Morgan Stanley’s operational risk context: A machine-readable business context 36 Jason Marburg, Morgan Stanley, and Michael Uschold, Semantic Arts, “Representing Operational Risk in an RDF Graph,” presented at Graphorum, October 16, 2019. 3p vendor/supplier 3P service ProcessTechnology asset Risk & control self-assessment Risk in context of a process Control Incident Issue Action plan This simplified diagram illustrates some of the main concepts and relationships articulated in Morgan Stanley’s Operational Risk Ontology (ORO), which consists of 350 classes, 350 properties, and 800 relationships. Semantic Arts, a PwC partner, led the development of the ORO. PwC (Josh Rattan and team) advised Morgan Stanley on risk strategy and information governance. Is realization of Is assessment of Is assessment of Depends upon Depends upon Is part of Pertains to failure of Depends upon Provided by RemediatesIs identified Issue with Is identified Issue with Has root cause
  37. 37. PwC | Data-Centric business and the knowledge graph When we can represent the knowledge, and we can use it to reason, then we should learn to improve what we know. --Tom Dybala of Resolvian 37
  38. 38. PwC | Data-Centric business and the knowledge graph Problem: Cryptic nature of AML investigation alerts, etc. • Investigators spend inordinate amounts of time reviewing alerts and documenting their conclusions • Regulators demand evidence of compliance and ample documentation of the methods applied during an investigation • Investigations need rigor and the ability to learn from rare true cases Solution: Knowledge graph- based support system for investigators • Knowledge graph continually improves ability to provide recommendations and explain decisions • Pulls up and auto-inserts supporting sections of regulations into reports • Otherwise explains conclusions drawn in decision support mode • Contrasts regulation-based deduction with predictions learned from historical data Benefit: Automated compliance assurance, substantial efficiencies and new business model potential • Most knowledge stored in systems rather than in consultants’ heads • Oodles of time saved on rote work • Cross-domain IP can be integrated into the knowledge graph • Portable abstract knowledge can be reused on subsequent engagement Resolvian’s explainable intelligence support system for AML 38 Source: Resolvian, 2019
  39. 39. PwC | Data-Centric business and the knowledge graph Construction management: Agent-based linking and contextualizing siloed design drawings, spreadsheets, etc. 39 graphMetrix, 2020
  40. 40. PwC | Data-Centric business and the knowledge graph 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 40 Events Neighborhoods Tags Restaurants Users Homes Experiences Places Airbnb Engineering, 2018 Markets
  41. 41. PwC | Data-Centric business and the knowledge graph 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 41 Jennifer Zaino, “Cognonto Takes On Knowledge-Based Artificial Intelligence,” Dataversity, 23 November 2016
  42. 42. PwC | Data-Centric business and the knowledge graph Montefiore’s semantic data lake 42 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
  43. 43. PwC | Data-Centric business and the knowledge graph Siemens’ industrial knowledge graph 43 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
  44. 44. PwC | Data-Centric business and the knowledge graph Pharma knowledge graphs for patient safety • Challenges 44 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
  45. 45. PwC | Data-Centric business and the knowledge graph Thomson Reuters’ financial knowledge graph as a service 45 Thomson Reuters, 2018
  46. 46. PwC | Data-Centric business and the knowledge graph State of the art knowledge graph – Blue Brain Nexus (1 of 2) 46 How do scientists record the provenance, curate, share in open source and collaborate on what they’re documented using 3D imaging techniques generated with the help of a supercomputer, such as the slices of a rat’s brain? From the EPFL Blue Brain Portal Gallery,
  47. 47. PwC | Data-Centric business and the knowledge graph State of the art knowledge graph – Blue Brain Nexus (2 of 2) 47 Bogdan Roman, “Blue Brain Nexus Technical Introduction,” March 2018,
  48. 48. PwC | Data-Centric business and the knowledge graph A semantic knowledge graph could enable the model-driven organization (a digital twin) at the data layer 48 Step One: Model the relevant elements of the organization, how they relate to one another and interoperate Step Two: Embed the model where it lives as machine-readable data Step Three: Integrate the source datasets as a target knowledge graph with model-driven mappings Step Four: Browse, query, disambiguate, detect and discover via the resulting knowledge graph Capability enables process Process uses information Clearvision, 2019. Used with permission. Prog/proj creates information Prog/proj Supports process Prog/proj Has person Prog/proj creates technology Person uses process Person uses information Person creates information Person uses technology Person uses capability Capability uses technology Information uses technology Technology Supports process Prog/proj has risk Portfolio has person Risk owned by personPerson Identified risk Company employs person Portfolio Has prog/proj Prog/proj outputs Work package Prog/proj Has role Prog/proj Has parente prog/pro Company Has prog/proj Prog/proj Delivers strategy Prog/proj Has milestone Company has portfolio Strategy has milestone Company Has role Role needs competenceWork package Needs competence Work package Process Information Person Risk Portfolio Milestone Strategy Company Role Competence Technology Capability Capability uses information Prog/proj Uses information Prog/proj Uses technology Prog/proj delivers capability Prog/proj Work Package has person Person has competence
  49. 49. Conclusion: Long- term benefits of a new data foundation
  50. 50. PwC | Data-Centric business and the knowledge graph Bigger picture transformation – Moving to where the new business will be, with a new data foundation 50 • 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.
  51. 51. Q&A © 2019 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 for further details. Alan Morrison PwC | Emerging Tech | Sr. Research Fellow +1 (408) 205 5109