Knowledge Connections

Executive Roundtable on Knowledge Graph Adoption in the Enterprise

November 30, 2020 | Connected Data London 

Moderator: Michael Atkin,

Principal, agnos.ai and Director, Enterprise Knowledge Graph Foundation

 

Business Adoption Panel: how does EKG get embedded into an organization 

  • Teresa Tung, Managing Director, Accenture Labs

  • Steven Gustafson, CTO, Noonum

  • Tom Plasterer, Director of Bioinformatics, Data Science & AI, AstraZeneca 

  • Bethany Sehon, Director, Metadata & Semantics, Enterprise Data Management, Capital One

 

Implementation Panel: how EKG gets implemented (orchestrated) in complex operational environments

  • Ben Gardner, Solution Architect - Knowledge Management at AstraZeneca

  • Laurent Alquier, Manager, Emerging Technologies Partnerships, Johnson & Johnson

  • Amy Heineike, VP Engineering, EMEA at Primer.ai

  • Katariina Kari, Knowledge Engineer at Zalando SE

  • Natasa Varytimou, Director, Data Architect, UBS

 

Hypothesis

 

The EKG equation starts with the recognition that data incongruence is a liability (a real problem).  We know the problem is solvable and that it is easier to solve with semantic technology than it is with conventional technology.  We also know that orchestrating transitions of this magnitude is difficult.  Making the leap from “columns” to “concepts” is achievable, but not automatic.  Unraveling existing infrastructures that serve mission-critical applications is expensive and not realistic.  And we know that leadership is required to overcome organizational inertia and facilitate change.

 

We (the community of data practitioners) are enamored with the potential of knowledge graphs because of the capabilities provided by semantic standards.  We know that precision and SME knowledge can be modeled.  We know that these models can be expressed in standards to achieve “unambiguous shared meaning.”  We know that quality is based on mathematical axioms (structural validation).  We know that access can be controlled at a datapoint level (lineage, provenance and entitlement) - and that we can unshackle ourselves from the rigid schemas that characterize our conventional environment.  

 

I conclude that we are approaching a new dawn of content interoperability.   This is one of the most important developments in terms of productivity.  The data incongruence problem is solvable and will be a revolution for the management of knowledge in an interconnected world.   

 

 

Business Adoption Panel Topics

 

  • Cognition & Mandate: To facilitate broad adoption (i.e. to get EKG embedded as central to the organization) we need to engage executive and business management in language that makes sense to them. 
     

    • Do executive stakeholders really understand and buy into the need to solve problems of data incongruence in fragmented technology environments? 

    • How do you sell the organizational value proposition (i.e. leverage and innovation agility) for EKG?

    • How important is the mandate to get EKG out of the “innovation lab” (i.e. what are the obstacles to overcome to make full migration over time)?
       

  • Eliminating Technical Debt: Can we be bold and conclude that silo-based (or data warehouse/data lake) environments are not sufficient to meet the goal of trust and flexibility of data.  We must make the transition to open standards and FAIR principles.  There is no other choice in an interconnected world.
     

    • How should we be thinking about the goal of unshackling ourselves from the technical debt that exists in organizations (and that run mission-critical applications)?

    • Is an enterprise knowledge graph the only pathway to enable companies to make the transition from “brown field” (technical silos) to “green field” (flexible infrastructure)?

    • Does the structure of the knowledge graph (i.e. foundational building blocks, concept reuse, abstraction layer) mean organizations can modernize their content nfrastructure slowly over time?
       

  • Business Proposition & Culture: Overcoming organizational obstacles (i.e. the four horsemen of the EKG apocalypse - ignorance, arrogance, obsolescence & power) is the most important success factor for the adoption of semantic technology.  How fragile is the EKG initiative within your organization.
     

    • Who are the “champions” of EKG and how did you get the “people of influence” to support the journey (how were they cultivated, what are they funding, what is their expectation)?

    • What are the organizational obstacles (i.e. lack of business vision/inertia, lack of skills, undefined use case, fear of new technology) and how do you overcome them?

    • Have you formalized the business proposition (what was the business justification, what are the criteria for evaluating success and why did the EKG story resonate)?
       

 

Implementation Panel Topics

 

  • Essential Capabilities: after organizational commitment, use case definition, budget and a clear view of the pathway what are the essential capabilities that are required to implement the EKG
     

    • What are the organizational pre-requisites and is a traditional data management program required?

    • What are the technology prerequisites and how do you achieve synchronization of strategy with IT?

    • What are the data prerequisites for managing integration/mapping, data pipeline management and DevOps/DataOps for systems development and testing
       

  • Use Cases: When a company is thinking about the knowledge graph value proposition - where is the best place to start? 
     

    • What are the criteria for defining and prioritizing the use case deliverables, understanding dependencies and coordinating the strategy for EKG rollout? 

    • Is data integration the true killer application for EKG?  Is it just as hard/expensive to do “EKG integration” as it is to do “canonical data model-to-physical repository integration? 

    • How much investment is required to implement EKG as the data fabric for the organization?  How important is collaboration across the organizational ecosystem?
       

  • Center of Excellence: new skill sets and a learning curve are required to effectively implement a knowledge graph and what should executives be thinking about as it relates to managing the implementation pathway
     

    • What was your experience in finding the people with the skill sets required to implement the knowledge graph?  What skills (e.g. ontology modeling, data transformation, content integration) are required?  Did you find them in house, recruit or use consultants?

    • How is governance managed in an EKG environment (i.e. reining in rogue operations, defining policies and incentivizing reusability) and how is this different from traditional data governance? 

    • What should the organization understand as it relates to project management (and organizational buy-in) to manage the transition strategy to EKG?