Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning. In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others. Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long. With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.

1. How to Improve Your
Analytic Data
Architecture Maturity
with Machine Learning
Presented by: William McKnight
“#1 Global Influencer in Data Warehousing” OnAlytica
President, McKnight Consulting Group
An Inc. 5000 Company in 2018 and 2017
@williammcknight
www.mcknightcg.com
(214) 514-1444
Second Thursday of Every Month, at 2:00 ET

2. Proprietary + Confidential
Powering Data
Experiences
to Drive Growth

3. Proprietary + Confidential
1in 2
customers integrate
insights/experiences beyond
Looker
2000+
Customers
5000+
Developers
Empower People with the Smarter Use of Data

4. Proprietary + Confidential
*Source: https://emtemp.gcom.cloud/ngw/globalassets/en/information-technology/documents/trends/gartner-2019-cio-agenda-key-takeaways.pdf
Rebalance Your Technology Portfolio Toward
Digital Transformation
Gartner: Digital-fueled growth is the top investment priority for technology leaders.*
Percent of respondents
increasing investment
Percent of respondents
decreasing investment
Cyber/information security 40 %1%
Cloud services or solutions (Saas, Paa5, etc.) 33%2%
Core system improvements/transformation 31%10 %
How to implement product-centric delivery (by percentage of respondents)
Business Intelligence or data analytics solution 45%1%
DigitalTransformation

5. Proprietary + Confidential
1 https://www.forrester.com/report/InsightsDriven+Businesses+Set+The+Pace+For+Global+Growth/-/E-RES130848
“Insights-driven businesses
harness and implement digital
insights strategically and at scale
to drive growth and create
differentiating experiences,
products, and services.”
7x
Faster growth than
global GDP
30 %
Growth or more using
advanced analytics in a
transformational way
2.3x
More likely to succeed
during disruption

6. Proprietary + Confidential
Governed metrics | Best-in-class APIs | In-database | Git version-control | Security | Cloud
Integrated Insights
Sales reps enter discussions
equipped with more context
and usage data embedded
within Salesforce.
Data-driven Workflows
Reduce customer churn
with automated email
campaigns if customer
health drops
Custom Applications
Maintain optimal inventory
levels and pricing with
merchandising and supply chain
management application
Modern BI &Analytics
Self-service analytics for
install operations, sales
pipeline management,
and customer operations
SQLIn Results Back

7. Proprietary + Confidential
‘API-first’ extensibility
Technology Layers
Semantic modeling layer
In-database architecture
Built on the cloud
strategy of your choice

8. Proprietary + Confidential
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Thank You

9. How to Improve Your
Analytic Data
Architecture Maturity
with Machine Learning
Presented by: William McKnight
“#1 Global Influencer in Data Warehousing” OnAlytica
President, McKnight Consulting Group
An Inc. 5000 Company in 2018 and 2017
@williammcknight
www.mcknightcg.com
(214) 514-1444
Second Thursday of Every Month, at 2:00 ET

10. Data Strategy
Data layer
acknowledged; Most
development is within
architecture; All in on
AI
Architecture
EDW(s) with DQ above
standard; 3 & 5 year
architecture plans;
Data Lake; Streaming
used; ELT > ETL;
Leveraging IP in S/T;
Third party data; EDW
accesses data lake
(cloud storage)
Technology
Graph db for relationship
data; Specialized analytic
stores for workloads with
requirements not suited for
EDW; EDW columnar; minimal
cubes; MDM – all applicable
functions for major subject
areas; Cloud first; Data
catalog
Organization
Data Governance by subject
area across most major
subject areas; Organizational
Change Management is part
of most projects; Chief Data
Officer; Data Scientist;
Strong Devops
Maturity Level 3

11. Maturity Level 4
Data Strategy
Data as asset in
financial statements /
executives; All
development is within
architecture;
predictive analytics;
Measure analytic
maturity
Architecture
Dynamic 3 & 5
year architecture
plans
Technology
Minimal cubes; MDM – all
functions for all major
subject areas; Looking at
GPU DBMS; Data catalog
populated; (Almost) all cloud
Organization
Data Governance by subject
area across all major subject
areas; Organizational Change
Management program is part
of all projects; Chief
Information Architect; Full
data lineage; Strong MLOps

12. Data Strategy
Data fully discoverable; AI
organization; hyper-
personalization;
prescriptive analytics;
Information Products
Architecture
Data Infrastructure as
platform with domain
mastery; microservices
and containerization
analytical architecture;
ETL automated
Technology
GPUs; complete enterprise MDM;
self-describing data; Operlytical
database; Databases at edge in
IoT; Embedded database in
applications
Organization
Data Governance=all,
pervasive
Maturity Level 5

13. ML Pioneers Are Locking In
• ML Pioneers
– Let the Data Speak
– Use Statistical Models
– Use Machine Learning
– Generate Deep Business Implications to Work
– Deal in Algorithm Management
– Acknowledge Human Scale
• First wave of ML Leaders are emerging
– And reaping exponential benefits
5

14. 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 reduce
cost associated with delays
ML in Action in the Enterprise

15. How to Improve Your Analytic Data
Architecture Maturity with Machine Learning
• Improve your applications with ML
• Shift them from only data warehouses, lakes,
and ETL (egregious toil and labor) to data
fabrics, AI, and pipelines.
7

16. Machine
Learning
Data Warehouse
Categorical Model
(e.g. Decision Tree)
Categorical Data Quantitative Data
Split
Quantitative Model
(e.g. Regression)
Train Train
Score Score
Evaluate
Data Engineering
Customer Data
Customer 360 Projects

17. Machine
Learning
Data Engineering
Training
Dataset
Predictive Model
(e.g. Logistic Regression)
Train
Evaluate
Deploy
Sensor Data
Up
Machines
Failing/ed
Machines
n
Machines
Scores Actions
IOT/Predictive Maintenance Projects

18. Data Engineering
Data Warehouse Data Engineering
Machine
Learning
Categorical Model
(e.g. Decision Tree)
Categorical Data Quantitative Data
Split
Quantitative Model
(e.g. Regression)
Train Train
Score Score
Evaluate
Historical
Transaction Data
Deploy
ScoresReal Time
Transactions
Actions
Fraud Detection Projects

19. Data Engineering
Machine
Learning
Data WarehouseData Engineering
Historical
Order Data
Train Train
Score Score
Evaluate
Deploy
Scores
Real Time
Orders
Actions
Sensor Data
Status
OK
Problems
n
Sensors
Supply Chain Optimization Projects

20. Foundations for ML Contributions to
Analytic Maturity
12

21. Data Scientists
• Part business analyst, part high-skilled
programmer, high-level statistician, and industry &
company domain expert
• Difficult to find
• Lengthy non-linear recruitment process
• Difficult to retain
• Top Jobs
– High-skill data analysis and interpreting
– Data Architecture
– Data modeling
– AI/ML – Top Job
13

22. Most ML will be done on data in the Data Lake
Data Scientist Workbench and Data Warehouse
Staging
OLTP
Systems
Data Lake
Data Scientists
ERP
CRM
Supply
Chain
MDM
…
Data
Warehouse
Data Mart
Stream or
Batch
Updates
DI
Real-Time,
Event-Driven
Apps
14

23. Balance of Analytics
Analytic Applications
DW
Data Lake
Analytic Applications
DW
Data Lake
Analytic Applications
DW Data Lake
DW

24. You’ll Need Many Data Domains
• 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
• 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
• Customer
• Employee
• Partner
• Patient
• Supplier
• Product
• Bill of Materials
• Assets
• Equipment
• Media
• Agencies
• Branches
• Facilities
• Franchises
• Stores
• Account
• Certifications
• Contracts
• Financials
• Policies
Typical Data Domains

25. Data is ready when it is…
• In a leveragable platform
• In an appropriate platform for its profile and
usage
• With high non-functionals (Availability,
performance, scalability, stability, durability,
secure)
• Data is captured at the most granular level
• Data is at a data quality standard (as defined by
Data Governance)
17

26. Data Science Modeling
• Evaluate various models and algorithms
– Classification
– Clustering
– Regression
– Others
• Tune parameters
• Iterative experimentation
• Data preparation
• May discover additional data needs or DQ
issues
18

27. Benefits of MLOps
• MLOps draws on DevOps principles and practices. Built upon notions
of continuous integration, delivery and deployment, DevOps responds
to the needs of the agile business – in summary, to be able to deliver
innovation at scale. Principles include:
• Continuous integration and delivery (CI/CD): initiatives follow
iterative models that can create value quickly, while building
understanding and experience.
• Collaborative development: solutions are defined, created and
optimised based on input from multiple stakeholder groups
• Business value focus: measurement and management look at both
the efficiency and effectiveness of solutions
• Governance by design: Quality, security, compliance and other factors
are to be considered at the outset and across the project.
19

28. Be a Leader. Shoot for this…
Analytics Strategy Analytics
Architecture
Analytics
Modeling
Analytics
Processes
Analytics Ethics
Multiple data
scientists on staff.
New team
members brought
up to speed in
weeks, not
quarters.
Analytics
contributions to all
major projects is
considered.
Central catalog
to track all
models along
their lifecycle.
Enterprise
data is
cataloged,
accessible,
well-
performing
and managed.
Hard to make
manual errors.
Logic within
analytics is
transparent.
Model expansion
in the enterprise.
Output from
analytics is
predictable and
consistent, with
auditable
outcomes.
Models are
reproducible.
Unused and
redundant
settings are
detectable.
Access restrictions
applied to models.
Data is tested for
model applicability.
Easy to specify a
configuration as a
small change from
a previous
configuration.
Analytic
applications
monitored for
operational issues.
Production analytic
flow includes
packaging,
deployment,
serving and
monitoring.
Scoring runs on a
periodic basis.
Good faith
attempts to remove
biased variables
from models.
Potential for
malicious use of
analytics
considered in
analytics lifecycle.

29. …and beyond.
Business is
fundamentally
different than 2
years ago due to
ML.
ML is driving
company
initiatives.
Engineers &
researchers are
embedded on
same teams.
Full ML code
reviews.
ML can be
deployed
from
anywhere.
Automated end
to end ML
lifecycle support
frequent model
updates, model
testing.
Dozens to
hundreds of
models running
simultaneously.
Impact of small
changes to ML
can be measured.
New algorithmic
approaches
tested at full
scale.
Visual model
configuration
changes.
Cybersecurity
experts engaged
in ML operations.
ML systems
protected from
manipulation
and corruption;
incorruptibility
highly
considered in all
models.
Model
transparency,
actions can be
explained.
End to end audit
trail for ML –
who, why, when.
Only fully vetted
models are used.
21
Analytics Strategy Analytics
Architecture
Analytics
Modeling
Analytics
Processes
Analytics Ethics

30. Work on Your Specific Challenge(s)
Organization not ready
for ML
Trained
ML
No
Trained
ML
Organization ready for
ML
DevOps and MLOpsGrow Organizational
Readiness
Grow Organizational
Readiness and Grow
ML Skills
Grow ML Skills

31. How to Improve Your
Analytic Data
Architecture Maturity
with Machine Learning
Presented by: William McKnight
“#1 Global Influencer in Data Warehousing” OnAlytica
President, McKnight Consulting Group
An Inc. 5000 Company in 2018 and 2017
@williammcknight
www.mcknightcg.com
(214) 514-1444
Second Thursday of Every Month, at 2:00 ET