1.
What the Aspiring or
New Data Scientist
Needs to Know About
the Enterprise
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.
Data Science Pioneers Are Locking In
• Data Science Pioneers
– Let the Data Speak
– Use Statistical Models
– Use Machine Learning
– Generate Deep Business Implications to Work
– Deal in Algorithm Management
• Some fake-it-till-you-make-it Data Scientists
make it
• First wave of Data Science leaders are
emerging
– And reap the exponential benefits
2

3.
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
AI in Action in the Enterprise

4.
Grayscale of Analytics and Data Science
• Reports
• Correlations
• Predictions
• Recommendations
• Interactions
4

5.
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
5

6.
Work on Your Specific Challenge(s)
Organization not ready
for Data Scientist
Data
Scientist
trained
in data
science
Data
Scientist
from
analyst
position
Organization ready for
Data Scientist
Organizational realities: Shield or
Deal
Organizational
realities: Shield or
Deal and Grow
Organizational
Readiness
Grow Organizational
Readiness and Grow
Data Science Skills
Grow Data Science
Skills

7.
Data Scientists Backgrounds
• Physical Sciences
• Computer Science
• Finance & Economics
• Math & Statistics
7

8.
Skill Requirements for AI/Data Scientist
• The split of the necessary AI/ML between the 'edge' of
corporate users and the software itself is still to be determined
• Math and Statistics
– floating point arithmetic, deep statistics, and linear algebra
• 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
• Soft Skills
8

9.
Soft Skills of a Data Scientist
• Business Goal Focus
• Communication
• Detail-Orientation
• Creativity
• Curiosity
These are harder to learn than the hard skills.
9

10.
Ideas for Growing Data Science
• Shadow Existing Data Scientist
• University Data Science Training
• Online Education
• Hackathons
10

11.
AI Pattern
1. Hire/Grow Data Science
2. Uncouple AI from Organizational Constraints
– While Conforming the Organization
3. Ideation
4. Compile Data! (Data Lake!)
– Internal and External
5. Label Data
6. Build Model
7. Prototype
8. Iterate
9. Productionalize
10. Scale
11

12.
Advice to the Data Scientist
12

13.
There are Lots of Data Platforms
• Operational Database
• Operational Real-Time
• Operational Big Data
• Operational Data Hub
• Master Data Management
• A Data Warehouse
• A Data Mart
– Dependent
– Independent
• A Data Lake
• Analytic Big Data Application
• Archive Storage
• A Staging Area
13

14.
Inefficient Information Architecture Exists
• Mostly exist for historical reasons
• Enterprise data can be a casualty
• Swelling budgets
• Several paths forward

15.
RDBMS
LEGACY
SOURCES
DATA INTEGRATION
USERS/REPORTS
MASTER DATA
OPERATIONAL
ANALYTICAL
COLUMNAR
DATABASES
DATA LAKE
Data Architecture 2019
DATA WAREHOUSE
DATA MARTS
IN THE CLOUD
SYNDICATED DATA
DATA STREAM
PROCESSING
NOSQL
15
OPERATIONAL
APPLICATIONS AND USERS
DATA SCIENTISTS

16.
Data Warehouses Have Flavors
● The Customer Experience Transformation Data Warehouse
focuses on customer attributes and touchpoints to improve the
value of customers.
● The Asset Maximization with IoT data warehouse deals with the
high volume of edge data tracking the physical assets of the
organization.
● The Operational Extension Data Warehouse supports company
operations directly with real- time analytics.
● The Risk Management Data Warehouse supports the ever-
growing compliance and reporting requirements and corporate
risk.
● The Finance Modernization Data Warehouse handles the
voluminous financial reporting and ensures the bottom line is
considered in every aspect of the business.
● The Product Innovation Data Warehouse delivers all product-
related information into the decisions of the product life cycle.

17.
You’ll Spend Most of Your time 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
17

18.
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

19.
You need an architecture that delivers
• Management of data sets both in learning and
inference
• Manage identities and access controls over
data sets, models and insights
• Ability to deploy easily to custom CPU/GPU
infrastructure and libraries
• Operational monitoring of ML jobs, for
efficiency and quality of insights
• Transparency and trust (e.g. through audit
trails) to build confidence in the results
19

20.
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)
20

21.
The Data May be of Suspect
Quality
Referential integrity
Uniqueness
Cardinality
Subtype/supertype constructs
Value reasonability
Consistent Value Sets
Formatting
Data derivation
Completeness
Correctness
Conformance to a clean set of values

22.
Somebody will focus on the Non-Functional
Requirements
• Availability, Performance, Reliability, Scalability,
Maintenance, Capacity, Security, Usability, Connectivity,
Systems management, Disaster recovery
• Used to describe the quality attributes of the system and
the constraints which the design options will be required
to satisfy in order to deliver the business goals, objectives
or capabilities.
• These requirements assist in determining sizing, cost and
viability of the proposed system as well service level
requirements for operational management of the solution.

23.
Find an Application to Ride

24.
Organizations are trying to run as agile
• Agile roles
• Sprints
• Commitments
• You may be on or lead an agile team(s)
24

25.
Organizations will need to do Organizational
Change Management to get your work accepted
• Some OCM tasks may be done at release or
product levels, i.e.,
– Stakeholder Management
– Training
– Future State Job Roles
• Others done for epics/features, i.e.,
– Deployment Readiness
– Deployment Communications
– Future State Job Roles
Product
Release
Sprint

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
26

27.
You May Feel/Be Alone
• Allow Ample Time For Skill Building
• Stay Close To Business Strategy
– Eventually you will set business strategy
• Look to Digital Leaders
• Pair With Data Scientists Across Different
Domains To Collaborate
27

28.
Machine Learning Ethics
• Elon Musk: “AI is our biggest threat”
• Weapons
• Bias
• Generating Training Data
• Transparency
• Fake News
• Jobs
• Surveillance
• Birth traits
• AI Rights
28

29.
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.

30.
…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 (and perhaps the
same person).
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.
30
Analytics Strategy Analytics Architecture Analytics Modeling Analytics Processes Analytics Ethics

31.
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.
31

32.
Focuses
• Templates and automation for model
development
• Model Monitoring
• Deployment Management
• Quality Assurance/SDLC
• Human Alerting
32

33.
Challenges
• These are still very early days for Data Science,
and practices are still being ironed out
• Many Data Science initiatives work in isolation
from each other and the broader business
• Data Science can require massive volumes of
data, which needs to be accessed scalably
• It is difficult to measure and manage the value
of Data Science projects
• Senior management does not yet see Data
Science as strategic
33

34.
34

35.
What the Aspiring or
New Data Scientist
Needs to Know About
the Enterprise
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