The document discusses data-centric architecture and knowledge graphs. It defines key terms like data, content, and knowledge graphs. It discusses how knowledge graphs are evolving to be multi-model and can combine different data structures. The document argues that a data-centric approach is needed to reduce data and application silos and enable greater data reuse. It provides examples of how knowledge graphs can help industries like banking, pharmaceuticals, and oil and gas better manage their data assets and digital twins. The market potential for knowledge graph technologies is large but there is still low awareness of how they can help organizations.

1. Alan Morrison
Data-Centric Architecture Forum
Fort Collins, Colorado
February 2020
Data-centric business
and the knowledge graph

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. Defining “data”,
“content,” etc.

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. 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. 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. 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. PwC | Data-Centric business and the knowledge graph
As a
hypergraph
modeling
medium,
Grakn.ai
powers the
OpenCTI
(Cyber Threat
Intelligence)
knowledge
sharing
platform.
Source: opencti.io and
Grakn.ai, 2019 and
2020
8

9. Defining data
centricity

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. 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. 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. 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. PwC | Data-Centric business and the knowledge graphPwC
The mobile data flood level keeps rising….
14

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. 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 http://datacentricmanifesto.org/

17. Why the market will
need data-centric
approaches

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. 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. 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. 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. 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. PwC | Data-Centric business and the knowledge graph
Graph databases can help to encourage a smart data integration, enrichment, and
reuse mentality
23

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. 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. 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. 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. 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 (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.

29. Transformative case
studies that point the
way forward

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. 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. 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. 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. 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. 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. 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. 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. 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. PwC | Data-Centric business and the knowledge graph
Construction management: Agent-based linking and
contextualizing siloed design drawings, spreadsheets, etc.
39
graphMetrix, 2020

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. 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. 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. 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. 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. PwC | Data-Centric business and the knowledge graph
Thomson Reuters’ financial knowledge graph as a service
45
Thomson Reuters, 2018

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, https://portal.bluebrain.epfl.ch/gallery-2/

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, https://www.slideshare.net/BogdanRoman1/bluebrain-nexus-technical-introduction-91266871

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
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

49. Conclusion: Long-
term benefits of a
new data foundation

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. pwc.com
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 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/
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