ADV Slides: How to Improve Your Analytic Data Architecture Maturity

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DATAVERSITYExecutive Editor at DATAVERSITY en DATAVERSITY
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
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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
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
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
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
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
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
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
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
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
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
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
Foundations for ML Contributions to
Analytic Maturity
12
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
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
Balance of Analytics
Analytic Applications
DW
Data Lake
Analytic Applications
DW
Data Lake
Analytic Applications
DW Data Lake
DW
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
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
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
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
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.
…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
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
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
1 de 31

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ADV Slides: How to Improve Your Analytic Data Architecture Maturity

  • 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 Resources: https://info.looker.com/ Blog: https://looker.com/blog Upcoming Events: https://looker.com/events Request a personal demo: https://looker.com/demo Email us: hello@looker.com 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