Scaling the mirrorworld with knowledge graphs
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The webinar discusses the vision of the Mirrorworld and the role of knowledge graphs in enhancing data management and efficiency throughout its lifecycle. It highlights the potential of digital twins, the challenges of traditional data management, and the importance of treating smart data as a strategic asset. The presentation also emphasizes the opportunities presented by knowledge graphs in building scalable, integrated systems to support AI and emerging technologies.
Scaling the mirrorworld with knowledge graphs
- 1. Alan Morrison SWC Webinar 23 October 2019 Scaling the mirrorworld with knowledge graphs
- 2. PwC |Scaling the mirrorworld with the knowledge graph Agenda 2 The mirrorworld vision How graphs will begin to underlay the mirrorworld Why semantic graphs are more efficient Use cases that point the way forward Conclusion: Efficiencies throughout the data lifecycle
- 3. Definitions and the mirrorworld vision
- 4. PwC |Scaling the mirrorworld with the knowledge graph Definitions 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 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.
- 5. PwC |Scaling the mirrorworld with the knowledge graph In the mirrorworld, everything will have a paired twin. Kevin Kelly in Wired Feb 12, 2019 June 2019 5
- 6. PwC |Scaling the mirrorworld with the knowledge graph What’s a digital twin? Depends on who you ask 6 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
- 7. PwC |Scaling the mirrorworld with 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 7
- 8. PwC |Scaling the mirrorworld with the knowledge graph Why treating smart data as a strategic asset is so critical right now 8 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
- 9. How graphs will begin to underlay the mirrorworld
- 10. PwC |Scaling the mirrorworld with 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 10
- 11. PwC |Scaling the mirrorworld with the knowledge graph Emerging markets — related to most relevant hype cycle techs 11 Total projected revenue: $58.2 billion (2021) Source: Tractica, Grandview Research and PwC analysis, 2019
- 12. PwC |Scaling the mirrorworld with the knowledge graph Summary: A very large available market, but of course there’s a catch…. 12 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
- 13. Why semantic graphs are more efficient
- 14. PwC |Scaling the mirrorworld with the knowledge graph Why traditional data management doesn’t scale 14 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
- 15. PwC |Scaling the mirrorworld with the knowledge graph The consequence of logic and data siloing – App-centric system-level complexity and disconnectedness spinning out of control (Result – Table and code sprawl) 15 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
- 16. PwC |Scaling the mirrorworld with the knowledge graph Implications of semantic knowledge graphs 16 • Data modeling in graph form can become dynamic, reusable, and scalable. • The same data model can be readily used conceptually, logically and physically, in a write-once, use-anywhere fashion, and can be reused as semantic metadata. • Semantic metadata is both machine- and human-readable, can be encoded as data and can live with the rest of the data at the data layer. • With the help of knowledge graphs, techniques born in the world of web content can be applied to other data + logic, across boundaries. • Logic does not have to be trapped in applications, but can remain universally accessible and callable via the data layer as part of reusable data models. • Knowledge graph-driven development in this world can become a highly efficient and scalable means of development, eliminating application and data silo sprawl.
- 17. PwC |Scaling the mirrorworld with 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 17 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
- 18. PwC |Scaling the mirrorworld with the knowledge graph The key opportunity – Large-scale integration and model-driven intelligence in a de-siloed and de-duplicated way 18 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 (https://www.youtube.com/watch?v=N2L8AqkEDLs), 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.
- 19. PwC |Scaling the mirrorworld with the knowledge graph The solution – Data-centric architecture reduces both application and database sprawl 19 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
- 20. Use cases that point the way forward
- 21. PwC |Scaling the mirrorworld with the knowledge graph Largest changes in market cap by global company, cross industry, 2018 21 1. Change in market cap from IPO date 2. Market cap at IPO date Source: Bloomberg and PwC analysis • Other major tech, FS and pharma cos. are also working on cross-enterprise knowledge graphs • Many have cross-enterprise knowledge graph ambitions, but most are focused on a single use case • S&P does cross-enterprise data management using relational tech 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 3831 464 7 Berkshire Hathaway United States Financial 358 492 8 Alibaba China Consumer Services 3021 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 Known KG builders The most value-creating companies in the world are using knowledge graphs
- 22. PwC |Scaling the mirrorworld with the knowledge graph State of the art knowledge graph – Blue Brain Nexus (1 of 2) 22 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, https://portal.bluebrain.epfl.ch/gallery-2/
- 23. PwC |Scaling the mirrorworld with the knowledge graph Morgan Stanley’s operational risk model (simplified) 23 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 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
- 24. PwC |Scaling the mirrorworld with the knowledge graph A semantic knowledge graph could enable the model-driven organization (a digital twin) at the data layer 24 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 https://virtualdutchman.com/2018/10/14/moving-to-a-model-based-enterprise-the-business-model/ 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
- 26. PwC |Scaling the mirrorworld with the knowledge graph Knowledge graphs complete the picture of your transformed data lifecycle and how it’s managed 26
- 27. pwc.com Thanks for attending! © 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 www.pwc.com/structure for further details. Alan Morrison PwC | Emerging Tech | Sr. Research Fellow +1 (408) 205 5109 alan.s.morrison@pwc.com https://www.linkedin.com/in/alanmorrison/ https://twitter.com/AlanMorrison https://www.quora.com/profile/Alan-Morrison