This webinar will focus on the promise AI holds for organizations in every industry and every size, and how to overcome some of the challenges today of how to prepare for AI in the organization and how to plan AI applications. The foundation for AI is data. You must have enough data to analyze to build models. Your data determines the depth of AI you can achieve – for example, statistical modeling, machine learning, or deep learning – and its accuracy. The increased availability of data is the single biggest contributor to the uptake in AI where it is thriving. Indeed, data’s highest use in the organization soon will be training algorithms. AI is providing a powerful foundation for impending competitive advantage and business disruption.

1. The Data Needed to Evolve
an Enterprise Artificial
Intelligence Strategy
Presented by: William McKnight
President, McKnight Consulting Group
@williammcknight
www.mcknightcg.com
(214) 514-1444

2. AI Whiskey, AI Music, AI Paintings
2
https://techcrunch.com/2019/05/15/oh-no-theres-a-i-whiskey-now/

3. Deepfakes, Sophia and Analyzing Emotions

4. 4

5. Enhance in-car navigation
using computer vision
Reduce cost of handling
misplaced items
improve call center
experiences with chatbots
Improve financial fraud
detection and reduce costly
false positives
Automate paper-based,
human-intensive process
and reduce Document
Verification
Predict flight delays based
on maintenance records
and past flights, in order to
reduce cost associated with
delays
AI in Action in the Enterprise

6. https://www.theguardian.com/sustainable-business/2017/feb/21/urban-heat-islands-cooling-things-down-with-trees-green-roads-and-fewer-cars
City Management

7. https://arxiv.org/pdf/1712.01432.pdf
Large-scale Video Management

8. Satellite or Aerial Data Management
https://medium.com/the-downlinq/car-localization-and-counting-with-overhead-imagery-an-interactive-exploration-9d5a029a596b

9. More AI Business Use Case Examples
• Marketing – segmentation analysis, campaign effectiveness
• Cybersecurity – proactive data collection and analysis of threats
• Smart Cities – track vehicle movements, traffic data,
environmental factors to optimize traffic lights, ensure smooth
flow and manage tolling
• Retail, Manufacturing – Supply flow, Customer flow
• Oil and Gas - determine drilling patterns, ensure maximum
utilization of assets, manage operational expenses, ensure safety,
predictive maintenance
• Life Sciences – study human genome (100s MB/person) for
improving health

10. What’s New is Deep Learning
• AI: 1950s
• Machine Learning: 2000s
– supervised learning, unsupervised learning,
reinforcement learning
• Deep Learning: 2010s
– Higher Predictive Accuracy
– Can Analyze All Data Sets
Deep Learning allows more complex problems to be
tackled, and others to be solved with higher accuracy,
with less cumbersome manual fine-tuning

11. AI Affects the Entire Organization
• Strategic
• Technical
• Operational
• Talent
• Data
11

12. Where to Look for AI Opportunities
• The products you make and the services you offer
• The supply chain for those products and services
• Business operations (hiring, procurement, after-sale
service, etc.)
• The intelligence used in designing your product and
service set
• The intelligence used in the marketing/approval funnel
for your products and services
12

13. AI is on the Data Maturity Spectrum
Maturity Level 3 (of 5):
Data Strategy
Data as asset in
financial statements
/ executives; All
development is
within architecture;
All in on AI
Architecture
EDW with DQ above
standard; 3 & 5 year
architecture plans
Technology
DI=streaming; Graph db for
relationship data; Specialized
analytic stores for workloads
with requirements not suited for
the EDW; EDW columnar; No
ODS; minimal cubes; MDM – all
functions for all major subject
areas
Organization
Data Governance by subject
area across all major subject
areas; Organizational Change
Management program is part of
all projects; True Self-Service
Business Intelligence; Chief
Information Officer

14. Get Data Under Management
14
• In a leveragable platform
• In an appropriate platform
– For the data
– For the usage
• Used effectively by multiple business
groups
• High NFRs
– Availability, performance, scalability,
stability, durability, secure
• Granular capture
• Data at data quality standard
– As defined by Data Governance

15. Data to Collect
• This is wide ranging, spanning all current data
– eCommerce
– ERP / CRM
– IoT (e.g., Heavy Industry, Factory, Consumer, Health,
Aircraft)
• Equipment performance
• Forecast breakdowns
• Health risk
– Publicly available (e.g., governmental)
– Third party
15

16. AI Data Examples
• Call center recordings and chat logs
• Streaming sensor data, historical maintenance records and search logs
• Customer account data and purchase history
• Email response metrics
• Product catalogs and data sheets
• Public references
• YouTube video content audio tracks
• User website behaviors
• Sentiment analysis, user-generated content, social graph data, and other
external data sources
16

17. Data is integral to AI Success
• Fraud Detection
• Call Center Chatbot
• Self-Driving/Transportation
• Predict Flight Delays
• Marketing – segmentation analysis,
campaign effectiveness
• Smart Cities
• Retail, Manufacturing – Supply flow,
Customer flow
• Oil and Gas Exploration
• Customer
• Employee
• Partner
• Patient
• Supplier
• Product
• Bill of
Materials
• Assets
• Equipment
• Media
• Geography
• Citizen
• Agencies
• Branches
• Facilities
• Franchises
• Stores
• Account
• Certifications
• Contracts
• Financials
• Policies
• Weather
Enterprise Data Domains
Robust Data is half of the effort for AI success

18. Data is integral to AI Success
• Fraud Detection • Customer
• Employee
• Partner
• Patient
• Supplier
• Product
• Bill of
Materials
• Assets
• Equipment
• Media
• Geography
• Citizen
• Agencies
• Branches
• Facilities
• Franchises
• Stores
• Account
• Certifications
• Contracts
• Financials
• Policies
• Weather
Enterprise Data Domains

19. Data is integral to AI Success
• Call Center Chatbot • Customer
• Employee
• Partner
• Patient
• Supplier
• Product
• Bill of
Materials
• Assets
• Equipment
• Media
• Geography
• Citizen
• Agencies
• Branches
• Facilities
• Franchises
• Stores
• Account
• Certifications
• Contracts
• Financials
• Policies
• Weather
Enterprise Data Domains

20. Data is integral to AI Success
• Self-Driving/Transportation
• Customer
• Employee
• Partner
• Patient
• Supplier
• Product
• Bill of
Materials
• Assets
• Equipment
• Media
• Geography
• Citizen
• Agencies
• Branches
• Facilities
• Franchises
• Stores
• Account
• Certifications
• Contracts
• Financials
• Policies
• Weather
Enterprise Data Domains

21. Data is integral to AI Success
• Predict Flight Delays • Customer
• Employee
• Partner
• Patient
• Supplier
• Product
• Bill of
Materials
• Assets
• Equipment
• Media
• Geography
• Citizen
• Agencies
• Branches
• Facilities
• Franchises
• Stores
• Account
• Certifications
• Contracts
• Financials
• Policies
• Weather
Enterprise Data Domains

22. Data is integral to AI Success
• Marketing – segmentation analysis,
campaign effectiveness • Customer
• Employee
• Partner
• Patient
• Supplier
• Product
• Bill of
Materials
• Assets
• Equipment
• Media
• Geography
• Citizen
• Agencies
• Branches
• Facilities
• Franchises
• Stores
• Account
• Certifications
• Contracts
• Financials
• Policies
• Weather
Enterprise Data Domains

23. Data is integral to AI Success
• Smart Cities • Customer
• Employee
• Partner
• Patient
• Supplier
• Product
• Bill of
Materials
• Assets
• Equipment
• Media
• Geography
• Citizen
• Agencies
• Branches
• Facilities
• Franchises
• Stores
• Account
• Certification
s
• Contracts
• Financials
• Policies
• Weather
Enterprise Data Domains

24. Data is integral to AI Success
• Retail, Manufacturing – Supply
Flow, Customer Flow • Customer
• Employee
• Partner
• Patient
• Supplier
• Product
• Bill of
Materials
• Assets
• Equipment
• Media
• Geography
• Citizen
• Agencies
• Branches
• Facilities
• Franchises
• Stores
• Account
• Certifications
• Contracts
• Financials
• Policies
• Weather
Enterprise Data Domains

25. Data is integral to AI Success
• Oil and Gas Exploration
• Customer
• Employee
• Partner
• Patient
• Supplier
• Product
• Bill of
Materials
• Assets
• Equipment
• Media
• Geography
• Citizen
• Agencies
• Branches
• Facilities
• Franchises
• Stores
• Account
• Certifications
• Contracts
• Financials
• Policies
• Weather
Enterprise Data Domains

26. Where to put data for Machine Learning
• Cloud Storage/Data Lake
• DBMS
• Master Data Management
• HDFS
– optimized for sequential read/writes
• Unstructured Data Stores
• Text-based serializations
26

27. Analytics Reference Architecture
Logs
(Apps, Web,
Devices)
User tracking
Operational
Metrics
Offload
data
Raw Data Topics
JSON, AVRO
Processed
Data Topics
Sensors
and
/ or
Transactiona
l/ Context
Data
OLTP/ODS
ETL
Or
ELT
Batch
Low
Latency
Applications
Files
In-
database
analytics
Reach
through
or ETL/ELT
or
Stream
Processing
or
Stream
Processing
Q
Q
Data
Warehouse

28. AI Pattern
1. Hire/Grow Data Science
2. Uncouple AI from Organizational Constraints
– While Conforming the Organization
3. Ideation
4. Compile Data!
– Internal and External
5. Label Data
6. Build Model
7. Prototype
8. Iterate
9. Productionalize
10. Scale
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29. Algorithm & Data Matching
• Naive Bayes Classification
• Decision Tree
• Regression
Try Multiple; Run Contests
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30. Corporate Requirements > Data
• The split of the necessary AI/ML between the 'edge' of corporate
users and the software itself is still to be determined
• Math
– floating point arithmetic, deep statistics, and linear algebra
• GPUs
• Python
– easy to program and it good enough
– NumPy and pandas libraries are available
• TensorFlow
– adds a computational/symbolic graph to Python
• R and MATLAB
– optimized for math with features such as direct slice and dice of matrices
and rich libraries to draw from
• Java and Scala
– work well with Hadoop and Spark respectively
30

31. The Data Needed to Evolve
an Enterprise Artificial
Intelligence Strategy
Presented by: William McKnight
President, McKnight Consulting Group
@williammcknight
www.mcknightcg.com
(214) 514-1444