The document discusses the necessity of digital transformation through knowledge graph technology, emphasizing that it is more about addressing people and skill gaps than merely adopting new technologies. It highlights the importance of overcoming legacy systems and mindsets to foster innovation and create interactive, contextualized business intelligence. Key solutions include developing transformation engines, leveraging diverse talent, and committing to foundational changes for scalable intelligence sharing in organizations.

1. Prepared by Alan Morrison Version 1.0
Kickstarting “Digital”
Transformation with
Knowledge Graph
Technology
Enterprise Data Transformation &
Knowledge Graph Adoption
Semantic Arts DCAF Event Series
February 28, 2022

2. A bit about SA and me, the Estes Park Group
and the PKG working group
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Where we met first, years ago: Where we meet now:
(1) Semantic Arts virtual Estes Park Group:
Every first Thursday of the month
10:30am Mountain time
(2) Personal Knowledge Graph Working Group
(also virtual and global):
Twice a month on alternate Fridays at 8:00am
Pacific time
If you’d like to be on our mailing list, just ask!

3. Version 1.0
Prepared by Alan Morrison
Outline
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Transformation Related Trends
Where have we been? Compute, networking and storage
advances–but perennial AI winters
Where are we going? Digital twins first, then interoperability,
interactivity and scaling
What’s getting in the way? Installed base, legacy mindset, inertia and
tech myopia
How do we kickstart real transformation? A sound plan, leadership commitment,
guerrilla teams and tribal alliances
What’s the real opportunity? Interactive, dynamic twinned supply chains

4. Where have we been?
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5. 5
“Contrary to popular belief, digital transformation is less about technology and more
about people. You can pretty much buy any technology [emphasis mine], but your
ability to adapt to an even more digital future depends on developing the next
generation of skills, closing the gap between talent supply and demand, and
future-proofing your own and others’ potential.”
–Becky Frankiewicz, President of ManpowerGroup North America &
–Tomas Chamorro-Premuzic, Chief Innovation Officer at ManpowerGroup
“Digital Transformation Is About Talent, Not Technology.” HBR, May 6, 2020
Q: Typical digital transformation buzz
True or false?

6. A: Partly false. Passively buying tech for
business innovation makes you part of the
problem, not the solution.
● Tech, particularly mainstream business tech, is pervasive and parasitic.
● Just passively buying more business tech will guarantee you’ll fail.
● When it comes to transformative tech, build more and buy less.
● Don’t add to the Tower of Babel; get serious and build what will fix root
problems.
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7. A2: Partly true. Tribal biases and resistance
often prevent change–especially the most
needed change.
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8. IT’s Tower of Babel embodies the root problem:
50+ years of application-centric sprawl
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9. Solution: Commit to using a knowledge graph
to kickstart for other kinds of innovation
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Ontotext, 2022

10. What does a knowledge graph do?
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Abstraction
Synthesis
Disambiguation
Large-scale integration and interoperation,
including:
Facilitates a contextual web, through:
identification

11. Where are we now?
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12. A: Data oligarchy
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BTW, all these
companies use
knowledge graphs

13. A few stats on the (data) oligarchs
● Google could be storing 10 exabytes total at this point
● Apple uses Google’s cloud for user data=six exabytes
● Amazon has 1.4M+ servers
● ⅓ of internet users daily will hit a website built on AWS
infrastructure
● Facebook has been storing a new petabyte of data every two
days
● Microsoft has 1M+ servers
● Tesla has 2.5M+ cars on the road–a massive data farming
operation
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14. And yet, in the late 2010s, some declared a
new, decentralized, independent “web” that
will give users more control… (?)
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Predictably,
intermediaries
have for several
years already
staked out
territory for this
new “web”
Omers Ventures, 2018
“International funds have
invested a total of USD 500
million this year in Indian
blockchain ventures.”
–Poulomi Chatterjee in
Analytics India, Feb. 13, 2022

15. ….including web3 “interoperability” intermediaries
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Axelar, 2022

16. Where are we going?
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17. A2: Digital twins for an interactive online world
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18. But interactive digital twins need an
interactive, contextualized data foundation
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19. What’s getting in the way?
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20. A: Pretending we’re solving problems
Surprise–Transformation requires transformative methods:
● Diagnosing the root cause
● Openness to new approaches
● Building a new foundation, step by step
● Focusing on key, but manageable pain points first
● Picking the right teams to lead innovation projects
● Proving the value of the solution you’re building
● Infiltrating the organizational tribes that are at firstresistant
● Then long-term commitment by leadership, with a bit of faith
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21. A2: New ways of working
take a long time
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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?
● How do we collaborate at scale?

22. How did we get here? By selling the old as new
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Nutanix, 2013
CompTIA, 2018
From a white paper
on desiloing the
datacenter. Note
there’s no mention
of data silos.
HP 2116 minicomputer, 1974 (Wikimedia Commons)

23. The mentality of provincial IT is still prevalent today
● We have the compute, networking and storage today to build an intelligent web
● But we have the siloed mentality of the 1970s:
○ Business units subscribe to their own SaaSes
○ IT departments defend their own turf
○ Only tabular, structured data is catalogued
○ Data, content and knowledge are all managed separately
○ Data is treated as inorganic and static, rather than organically
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24. How do we kickstart real
transformation?
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25. A: Build a transformation engine that actually runs
● Understand the root problem
● Find or build organizations who care about
solving the real problem
● Find passionate, informed people to tackle the
problem
○ Abstract thinkers
○ Practical problem solvers who are open to
abstract, non-linear thinking
● Use a proven method to work the problem
● Create a diverse network of talent to help
● Expand the informal network to build alliances
● Develop a vision and use it to inspire
● Solve small, annoying problems first to
demonstrate value
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26. What’s the real opportunity?
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27. A1: Connected,
scaled out and
contextualized
business
intelligence
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Alleviates the “drunk under the lamppost looking for his money” problem

28. A2: Scaled out, purpose-specific intelligence
platforms and communities
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Blue Brain
Nexus–Reverse
engineering the brain
Diffbot–Crawling the whole
web for ecommerce
intelligence
Strise.ai–Bringing together
160,000 sources for
Anti-money laundering and
fraud detection
Montefiore/Einstein–A
Improve hospital outcomes and
efficiencies at the same time
with a KG foundation

29. A3: A means of end-to-end, scalable
intelligence sharing for supply chains
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Graphmetrix: Smart document sharing for
large-scale construction projects using SOLID pods
OriginTrail.io: Decentralized
supply chain tracking and tracing
using knowledge graphs +
blockchains

30. To succeed, organizations will have to become
more like intelligence agencies–bona fide
data-centric organizations
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31. Prepared by Alan Morrison Version 1.0
Look forward to chatting with you.
Alan Morrison
Data Science Central
LinkedIn | Twitter | Quora | Slideshare
+1 408 205 5109
a.s.morrison@gmail.com
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