From a keynote presentation delivered at SEMANTiCS 2018 on 12 September 2018. See also the video of this talk at https://lnkd.in/gXxhaMD (starts at 15 minutes + in) Technology innovations aren't all created equal. Most are incremental improvements (Type I), layers of a new stack, for example, that accumulate over time. Stacks become towers of Babel, triggering struggles with complexity. Migrating to the cloud simply hands the complexity problem over to service providers. Then there are new abstraction paradigms (Type II) that make the stack more broadly accessible and easily used. Abstractions don't necessarily reduce complexity, but they can help manage it. Data virtualization and agent management of data and logic, for example can help address configuration shortfalls, and if broadly adopted, could reduce the number of data breaches by closing configuration gaps. And then there are occasionally reduction paradigms (Type III) that can simplify the whole nature of how services are created and delivered. These suggest we could dispense with layers of the stack and enable broad simplification through a model-driven approach. Semantic graph model-driven platforms seem to be on that path. We've been through Type I technology evolution, and Type II provides a way to sustain the gains of Type I. But complexity still looms in the background and remains an inhibitor. Where we've ended up on the business model front is thousands of companies now trying to replicate what the much touted unicorns such as AirBnB, Netflix, Snapchat and Uber of the late 2000s and 2010s have done, in broad range of different business niches. Type III innovation could be the enterprise key to tackling IT complexity and becoming fit for growth at long last. A byproduct would be an AI-ready data layer and a cross-enterprise analytics capability that hasn’t been feasible for most.

1. Collapsing the IT stack
Clearing a path for AI adoption
Alan Morrison
Senior Research Fellow
Integrated Content | Emerging Tech

2. PwC | Collapsing the IT stack
Outline for today’s talk
2
Early adopters--winning the data war
Collapsing the IT stack
Diagnosing the problem
Progress on solutions
Conclusion

3. PwC | Collapsing the IT Stack
Early adopters--winning
the data war
3PwC | Collapsing the IT stack

4. PwC | Collapsing the IT stack
Largest change in market cap by company (2009 to 31 March 2018)
4
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 383(1) 464
7 Berkshire Hathaway United States Financial 358 492
8 Alibaba China Consumer Services 302(2) 470
9 JPMorgan Chase United States Financials 275 375
10 Bank of America United States Financials 263 307
(1)Change in market cap from IPO date
(2)Market cap at IPO date
Source: Bloomberg and PwC analysis

5. PwC | Collapsing the IT stack
Widening corporate inequality – Top versus bottom of Top 100
5
337 329
417
559
416 469
725
604
754
851
40 61 69 64 70 81 85 76 88 97
0
200
400
600
800
1000
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Market caps of top and bottom companies
Number 1 Number 100
8,402
17,438
20,035
0
3,000
6,000
9,000
12,000
15,000
18,000
21,000
24,000
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Total market cap of Top 100 companies as at 31 March
Apple
Apple
Apple
Apple
AppleApple
AppleExxon
Mobil
PetroChina
Exxon
Mobil
MarketCap($bn)MarketCap($bn)

6. PwC | Collapsing the IT stack
Widening corporate inequality in the Top 100—by country/region to 31 March
6
3,805
5,170 5,538
6,202
6,739
8,052
9,322 9,636
10,928
12,187
1,061 1,260 1,459 1,226 1,145 1,131 2,012 1,517
1,801
2,8222,272
3,311 3,370 2,980 3,347
3,997
3,424 2,996 3,031 3,362
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
US China Europe
42
39 38
41
43
47
53 54 55 54
9 9 9 8 7 7
10 10 10
12
31
33
31
27
28
30
26
24
22 23
0
10
20
30
40
50
60
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
US China Europe
Market cap change 2017 to 2018
• US up $1,259bn
• Europe up $331bn
• China up $1,021bn
MarketCap($bn)MarketCap($bn)
Source : Bloomberg and PwC analysis

7. PwC | Collapsing the IT stack
Widening corporate inequality – by country to from 2009 to 31 March 2018
7
3
5
7
9
9
42
3
4
4
5
10
55
3
4
4
5
12
54
0 10 20 30 40 50 60
Switzerland
Germany
France
United Kingdom
China
United States
2018
2017
2009*
*2009 figures do not add to 100 due to seven companies in the 2009 Top 100 being in locations of domicile that are no longer in the Global Top 100
Source: Bloomberg and PwC analysis
Number of companies in the Top 100

8. PwC | Collapsing the IT stack
San Francisco Bay Area now in Top 20 economies worldwide…. but for how long?
8
“The Bay Area has the 19th-largest economy in the world, ranking above Switzerland and Saudi Arabia….
Startups, particularly those in the consumer-internet business, increasingly struggle to attract capital in the
shadow of Alphabet, Apple, Facebook et al.”
--The Economist, “Why startups are leaving Silicon Valley,” 30 Aug 2018

9. PwC | Collapsing the IT stack
Largest change in market cap by company (2009 to 31 March 2018)
9
Known
knowledge
graph builders
Known KG
builders
Operator of Taobao
and KG builder
(1)Change in market cap from IPO date
(2)Market cap at IPO date
Source: Bloomberg and PwC analysis
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 383(1) 464
7 Berkshire Hathaway United States Financial 358 492
8 Alibaba China Consumer Services 302(1) 470
9 JPMorgan Chase United States Financials 275 375
10 Bank of America United States Financials 263 307
v

10. PwC | Collapsing the IT Stack
Collapsing the IT stack
10PwC | Collapsing the IT Stack
Wikimedia Commons, 2007
PwC | Collapsing the IT stack

11. PwC | Collapsing the IT stack
Most innovations are incremental, adding to the stack, with data as
an afterthought (Type I)
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
Component -ized
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 | Collapsing the IT stack
Most of the IT workforce just adds to or keeps track of the sprawl
12
0.01
0.9
2
1.6
3.2
US IT workforce in 2016 (in mIllions)
Semantic data
Data related (less semantics)
General
Network/hardware
Software
Sources: US Bureau of Labor Statistics and PwC estimates, 2018
Total IT workforce = 7.7 million
(= 5 percent of the US overall workforce in 2016)

13. PwC | Collapsing the IT stack
The US as a whole has more opioid abusers than it does IT workers
13
US Census Bureau, Bureau of Labor Statistics, and Health and Department of Human Services, 2018
0 50 100 150 200 250 300 350
IT workers
All opioid abusers
Disabled and over 65 years old
Entire US workforce
Entire US population
Number of IT workers in the US in 2016, in context (in millions)

14. PwC | Collapsing the IT stack
Object virtualization (Type II) manages complexity, just so IT can get
its arms around the sprawl
14
EnterpriseWeb and PwC, 2015

15. PwC | Collapsing the IT stack 15
Type III: data-centric architecture reduces both application and
database sprawl
Applications for execution only, models exposed with the dataApp code trapped in Database orphans and models
Data lake or hub
Semantic model/rules
  
Applets

16. PwC | Collapsing the IT stack
Identify and declare the few hundred business rules you need as a model
16
“In every company I’ve ever studied, there are only a few hundred key concepts and relationships that the
entire business runs on. Once you understand that, you realize all of these millions of distinctions are just slight
variations of those few hundred important things.”
--Dave McComb, author of Software Wasteland, quoted in Strategy + Business
See “Are you Spending Way too Much on Software at
https://www.strategy-business.com/article/Are-You-Spending-Way-Too-
Much-on-Software?

17. PwC | Collapsing the IT stack
Call the model to reuse those rules whenever necessary
17
“You discover that many of the slight variations aren’t variations at all. They’re really the same things with
different names, different structures, or different labels. So it’s desirable to describe those few hundred concepts and
relationships in the form of a declarative model that small amounts of code refer to again and again.”
--Dave McComb (as previously cited)
See “Are you Spending Way too Much on Software at
https://www.strategy-business.com/article/Are-You-Spending-Way-Too-
Much-on-Software?

18. PwC | Collapsing the IT Stack
Diagnosing the bigger
problem
18PwC | Collapsing the IT Stack
PwC | Collapsing the IT Stack
PwC | Collapsing the IT stack

19. PwC | Collapsing the IT stack
What AI needs versus what it has
19
What it needs: Contextualized, disambiguated, highly relevant
and specific integrated data, flowing to the point of need
What it has: Single batch datasets cleaned up to be good enough
by data scientists, who spend 80% of their time on cleanup
What it needs: Knowledge engineers, and many bold Data
Visionaries in addition to big D Data Scientists, data-centric
architects, pipeline engineers, specialists in many new data niches
What it has: A growing group of tool users versed only in
probability theory, neural networks, python and R, including small
D data scientists, engineers and architects, plus scads of
entrenched application-centric developers
Finance
Operations
Marketing
Input Output
Input
layer
Hidden
layer 1
Hidden
layer 2
Output
layer

20. PwC | Collapsing the IT stack
The real inhibitors to adoption aren’t technological – they’re rooted
in tribal biases and resistance to change
20
Tribalism CollectivismIndividualism
Anarchy TotalitarianismLocus of inertia
Daniel Quinn, Beyond Civilization and Alice Linsley, “Daniel Quinn: A Return to Tribalism?”, college-ethics.blogspot.com, 2018

21. PwC | Collapsing the IT stack
Tribalism – Machine learning edition
21
Source: Pedro Domingos, The Master Algorithm, 2015
More at “Machine learning evolution”: http://usblogs.pwc.com/emerging-technology/machine-learning-evolution-infographic/, PwC, 2017
Symbolists Bayesians Connectionists Evolutionaries Analogizers
Use symbols, rules, and
logic to represent
knowledge and draw
logical inference
Assess the likelihood of
occurrence for
probabilistic inference
Recognize and
generalize patterns
dynamically with
matrices of probabilistic,
weighted neurons
Generate variations and
then assess the fitness of
each for a given purpose
Optimize a function in
light of constraints
(“going as high as you
can while staying on
the road”)
Favored algorithm
Rules and decision trees
Favored algorithm
Naïve Bayes or Markov
Favored algorithm
Neural network
Favored algorithm
Genetic programs
Favored algorithm
Support vectors

22. PwC | Collapsing the IT stack
Tribalism – Data integration edition
22
Trend toward more data centricity this way
Application-centric
RESTful developers
Relational database
linkers
Data-centric
knowledge graphers
Application-centric
ESB advocates
Semantic Web Company, 2018
Computerscience
wiki.org, 2018
TIBCO, 2014
Oracle DBA’s Guide, 2018
User Scott
Select FROM emp
Local
database
PUBLIC SYNONYM
Emp - >
emp@HQ.ACME>COM
Database link (undirected)
Remote
database
EMP table
Portals
Net
Application
B2B
Interactions
Enterprise
data
Business process
management
Web Services
Mobile
Applications
DEE
Application
ERP
CRM SFA
Legacy
System
ESBCustom environment Common environment
API
Unstructured
Data
Semi-
structured Data
Structured
Data
Schema mapping based
on ontologies
Entity Extractor informs all
incoming data streams about
its semantics and links them
Unified Views
RDF Graph
Database
PoolParty
Graph Search

23. PwC | Collapsing the IT stack
Crossing the chasm between the tribes
23
Reducing the amount of unfamiliarity developers confront--familiar document means to achieve
comparable ends to graph:
• Semantic suites that use the JSON format and familiar hierarchies: SWC’s PoolParty is an example
• GraphQL: A popular document shape language that talks to APIs using SELECT-like statements and tree shapes;
backend-agnostic; just uses a mental model for graph; addresses the API endpoint proliferation problem
• Accessible web as database methods: JSON-LD and Schema.org, etc. vocabularies
• Document “schemas” via data objects: JavaScript objects to developers = documents to NoSQL DB types; Object
Data Modeling instead of database semantics
• Mongoose or MongoDB JSON schema features + GraphQL: MongoDB object modeling and querying that can
be used for subdocument filtering within a GraphQL context
• HyperGraphQL: A GraphQL UI for Linked Data, restricted to certain tree-shaped queries
• Universal Schema Language: Mike Bowers’ document/graph query and modeling language still in development
• COMN: Ted Hills’ well-defined NoSQL + SQL data modeling notation

24. Progress on solutions
PwC | Collapsing the IT stack 24

25. PwC | Collapsing the IT stack
Types of logic most used in AI-enabled systems
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
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
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: Consumer tax software
Perceiving
Learning
Abstracting
Reasoning
Perceiving
Learning
Abstracting
Reasoning
Perceiving
Learning
Abstracting
Reasoning
Example: Facial recognition
using deep learning/neural nets
Example: Explains first how handwritten
letters are formed so machines can decide
based on these individual models—less data
needed, more transparency.
John Launchbury of DARPA (https://www.youtube.com/watch?v=N2L8AqkEDLs), Estes Park Group and PwC research, 2017
Previously dominant On the rise and rapidly improving Nascent, just beginning
1

26. PwC | Collapsing the IT stack
Most automated knowledge graph – Diffbot?
26
“Diffbot’s crawler regularly refreshes the DKG with new information and its machine learning algorithms are smart enough
to pass over sites with histories of producing ‘logically inconsistent’ facts.
“‘That’s one of the reasons why we fuse information together from different sources,’ Tung said. ‘Our scale is such that
there’s minimal potential for errors. We’d bet the business on it.’
“Diffbot launched in 2008 and counts 28 employees among its core staff of engineers and data scientists.”
--Mike Tung of Diffbot, quoted in VentureBeat
Diffbot claims an automated knowledge graph of 1 trillion + facts, designed to grow without humans in the loop.
That compares with 1.6 billion crowdsourced facts in Google’s knowledge graph, according to VentureBeat.
Kyle Wiggers, “Diffbot launches AI-powered knowledge graph of 1 trillion facts about people, places, and things,” VentureBeat,
30 August 2018

27. PwC | Collapsing the IT stack
Versus more explicit, precise, contextualized meaning with a triadic, Peircean
knowledge graph and less than 1M concepts?
27
“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
Jennifer Zaino, “Cognonto Takes On Knowledge-Based Artificial Intelligence,” Dataversity, 23 November 2016

28. PwC | Collapsing the IT stack
Contextual AI via a large knowledge graph at Fairhair.ai
28
Meltwater, 2018
Media Intelligence Apps New Apps Enterprise
Solutions
3rd party Apps 100M
documents ingested
daily
150 NLP/IR pipelines
100’s Billions of
Searches
Global Monitoring Analyze & Report
Distribute Influence & Engage Employee
App
Freemium
At-Powered
Reporting
Outside
Insight Custom
Solutions
PaaS
Service Layer
Context Building
Enriching & Analysis
Outside Data
Streaming, Search, Analytics, APIs
Building block to leverage the platform
Knowledge Graph
Enable cognitive applications on top of our data by connecting the dots
Data Enrichment Platform
Enrich, analyze & build by interoperating with all major players
AI-driven data Acquisition
Bring high quality outside to our repository with minimal human effort

29. PwC | Collapsing the IT stack
Montefiore’s semantic data lake
29
Montefiore Health, Franz, Intel and PwC research, 2017
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
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
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.

30. PwC | Collapsing the IT stack
Siemens’ 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
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

31. PwC | Collapsing the IT stack
Pharma knowledge graphs for patient safety
31
Graph integration Natural language processing
Data cleaning during analysis In-memory query engine
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
PwC and Cambridge Semantics, 2018
SolutionsChallenges

32. PwC | Collapsing the IT stack
NuMedii’s precision therapeutics knowledge graph
32
Ontotext and NuMedii, 2018
goTerm
Calcium ion
binding
2201
Protein binding
Extracellular
region
ENSG
00000138829
Extracellular
matrix
disassembly
Extracellular
matrix
Organization
proteinaceous
extracellular
matrix
positive
regulation
of bone
mineralization
Fibrillin - 2
Extracellular
matrix
Structural
constituent
Extracellular
matrix
micro fibril
Camera-type
eye
development
CHEMBL_TC_
10038
Go Function
Reference_
gosubset_prok
100001650
100001532
100001739
100000687
100002060

33. PwC | Collapsing the IT stack
Thomson Reuters’ financial knowledge graph as a service
Thomson Reuters, 2018

34. PwC | Collapsing the IT stack
Colryut Group’s graph master data federation (Type II
transformation)
34
This graph visualization + data
editing/filtering environment allows
scalable and articulated governance
at the data layer, as well as
communications between groups in
different parts of the organization,
including IT and executive
management.
DINTTA_GOEDKODE TA_GOEDKODE
DINT
GC21GL5Q
DINT
Product service center
New Project
DINT
Product service center
Source: Colruyt Group and Tom Sawyer
Software, 2018
In order to minimize dependencies
between transformation projects,
Belgian supermarket chain Colruyt
Group used a master data structuring,
editing and visualization environment
created by Tom Sawyer Software.

35. Conclusion and some suggestions
PwC | Collapsing the IT stack 35

36. PwC | Collapsing the IT stack
Tell your C-suite: The Third Wave of AI is missing half the data it
needs
36
• Relationship data has long been overlooked, but specifying relationships is how you build context
• Connected, relationship-rich data will be seen as the most important asset for companies
• Can’t have governance without connected data
• Can’t have connected, meaningful data without a semantic model
• Can’t compete in the digital ecosystem and cross boundaries without meaningful data connectivity
• When it comes to enabling the AI your company needs, think semantic graph:

37. PwC | Collapsing the IT stack
The innovation graph must be semantic to scale
37
Once digitized (with the help of AI
+ blockchain, etc.), organizations
play different roles than they've
been accustomed to in the business
ecosystem. Some because of their
data collection heritage can become
data providers.
Others take up roles in the data
supply chain, or position
themselves as industry platforms or
marketplaces.

38. PwC | Collapsing the IT stack
Document models can be a stepping stone to graphs
38

39. PwC | Collapsing the IT stack
Contextual graphs + statistics methods = innovation at scale
39

40. Questions or comments?
pwc.com
© 2018 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
Sr. Research Fellow
PwC | Integrated Content | Emerging Tech
Mobile: +1(408) 205 5109
Email: alan.s.morrison@pwc.com
PricewaterhouseCoopers LLP
488 Almaden Blvd., Suite 1800
San Jose, CA 95110 USA
http://usblogs.pwc.com/emerging-
technology/author/amorrison009/
https://www.linkedin.com/in/alanmorrison
@AlanMorrison quora.com/profile/Alan-Morrison