Joe Caserta explores the world of analytics, tech, and AI to paint a picture of where business is headed. This presentation is from the CDAO Exchange in Miami 2018.

1. Data Intelligence
How the Amalgamation of Data, Science, and Technology is
Changing the Way We Do Business
January 22, 2017
Presented by:
Joe Caserta

2. Caserta Timeline
LaunchedBig Data practice
Co-author, with Ralph Kimball, The Data
Warehouse ETL Toolkit (Wiley)
Data Analysis, Data Warehousing andBusiness
Intelligence since 1996
Began consultingdatabase programing anddata
modeling 25+ years hands-on experience building database
solutions
Founded CasertaConcepts, LLC in NYC
Web log analytics solution published in Intelligent
Enterprise magazine
Launched Data Science, Data Interaction andCloud
practices
Laser focus on extending Data Analytics with Big Data
solutions
1986
2004
1996
2009
2001
2013
2012
2016
Dedicated to Data GovernanceTechniques onBig
Data (Innovation)
Awarded Top 20 Big Data Companies
Top 20 Most Powerful
Big Data consulting firms
Launched Big DataWarehousing (BDW) Meetup
NYC:4,000 Members
2017
Top 20 Most Admired Tech Leaders in Business
Established best practicesfor big dataecosystem
implementations
Caserta InnovationLab invents Blockchain, AI,AR
Solutions

3. About Caserta
Data Intelligence and Strategic Consulting
Data Lakes, Data Laboratories, Data Warehouses
Award-winning company for Data Innovation
Data Science, Machine Learning, Artificial Intelligence
Internationally recognized work force
Best Practices, Authors, Educators, Mentors
Strategy, Governance, Architecture, Implementation

4. Our Clients
Retail/eCommerce
& Manufacturing
Finance, Healthcare,
Energy & Insurance
Digital Media/AdTech
Education & Services

5. Evolution of Analytics
What
happened?
Why did it
happen?
What will
happen?
How can we
make It happen?
Data Analytics Sophistication
BusinessValue
Source: Gartner
How to interact with
the customer?
Reports  Correlations  Predictions  Recommendation s  Artificial Intelligence

6. Why is Data so Important?
1500s
Prin ng Press
1840s
Penny Post
1850s
Telegraph
1850s
Rural Free Post
1890s
Telephone
1900s
Radio
1950s
TV
1970s
PCs
1980s
Internet
1990s
Web
2000s
Social Media, Mobile, Big Data, Cloud
98,000+ Tweets
695,000 Status Updates
11 Million instant messages
698,445 Google Searches
168 million+ emails sent
1,829 TB of data created
217 new mobile web
users
Every 60 Seconds

7. Data Analytics is your Differentiator
Acquiring, analyzing and acting on data with a focus on speed to action

8. Artificial Intelligence
 “AI is one of the most important things that humanity is
working on. It’s more profound than electricity or fire”
- Sundar Pichai, CEO, Google

9. The Customer Journey
PR
Radio
TV
Print
Outdoor
Word of Mouth
Direct Mail
Customer Service
Physical Touchpoints
Digital Touchpoints
Search
Paid Content
email
Website/
Landing Pages
Social Media
Community
Chat
Social Media
Call Center
Offers
Mailings
Survey
Loyalty Programs
email
Agents
Partners
Ads
Website
Mobile
3rd Party Sites
Offers
Web self-service

10. Learning the Path-to-Purchase
Attribution
Type
Comments
Single Touch Rules-Based Statistically Driven
Assign the credit to the
first or last exposure
Assign the credit to each
interaction based on
business rules
Assign the credit to
interactions based on
data-driven model
Ad-Click Mailing MailingE-mail E-mailAd-Click Ad-Click
100% 33% 33% 33% 27% 49% 24%
- Last touch only
- Ignores bulk of
customer journey
- Undervalues other
interactions and
influencers
- Subjective
- Assigns arbitrary values
to each interaction
- Lacks analytics rigor to
determine weights
 Looks at full behavior
patterns
 Consider all touch points
 Can apply different models
for best results
 Use data to find
correlations between touch
points (winning
combinations)

11. Data Science in Practice
Source: https://www.collaberatact.com/data-science-stay/

12. Data Science for the Enterprise
CRISP-DM: Cross Industry Standard Process for Data Mining
1. Business Understanding
• Solve a single business problem
2. Data Understanding
• Discovery
• Data Munging
• Cleansing Requirements
3. Data Preparation
• ETL
4. Modeling
• Evaluate various models
• Iterative experimentation
5. Evaluation
• Does the model achieve business objectives?
6. Deployment
• PMML; application integration; data platform; Excel
Business
Understanding
Data
Understanding
Data
Preparation
Modeling
Evaluation
Deployment
Data

13. Governing Data Innovation

14. S3
Ingest Storage ETL Presentation VisualizationData Sources
• OPRA
• Equifax
• CDS
• Moody’s
• BlackBox
Relational Datasets
• Barclay
• Eureka
• Hedge Fund
Intelligence
• Hedge Fund
Research
• Lipper
• Morningstar
• MF Holdings
• BD/ ADV
Flat File Datasets
S/ FTP
Push
Kinesis
• CAT
Landing
Data Lake
(Tier 1)
Data Lake
(Tier 2)
Data Science
(Ephemeral)
Redshift
Spark
(Streaming*
/ Batch)
Lambda
Data Science
• Python
• SQL
• Scala
• Predic ve
Analy cs
• Text Analy cs
• Business
Intelligence
Structured
Data
Redshift
Metadata
Repository
• Data
Marketplace
• Clean
• Match
• Derive
• Aggregate
• Mllib
• CoreNLP
• Prepare
• Deliver
Streaming Data Sets
Data Analytics Innovation Ecosystem
SAP
Oracle
Financials
Marketing
Relational DBs
Salesforce
Workday
RESTful APIs
Cloud DBs
Bloomberg
Capital IQ
FactSet
Quandl
Alternative Data
Web logs
IoT
Streaming Data

15. Data Quality & Monitoring
• Build a robust data quality subsystem:
• Metadata and error event facts
• Orchestration
• Based on Data Warehouse ETL Toolkit
• Each error instance of each data
quality check is captured
• Implemented as sub-system after
ingestion
• Each fact stores unique identifier of the
defective source row

16. Change Management
Global economics
Intensity of competition
Reduce costs
Move to cross-functional teams
New executive leadership
Social trends and changes
Speed of technical change
Period of time in present role
Status & perks of office/dept under threat
No apparent reasons for proposed changes
Lack of understanding of proposed changes
Fear of inability to cope with new technology
Concern over job security
Forces for Change Forces ResistingChange
Status Quo
http://www.change-management-coach.com/force-field-analysis.html

17. Agile Data Organization

18. Cloud Platform Components
Cloud Component AWS Google Microsoft
Scalable distributed storage S3 GCS Azure Storage
Pluggable fit-for-purpose processing EMR DataProc HDInsight
Compute Services EC2 GCE VMs
Consistent extensible framework Spark Spark Spark
Dimensional MPP Data Warehouse
Redshift/
Snowflake
BigQuery
Azure SQL Data
Warehouse
Data Streaming Kenesis PubSub Azure Stream
Common Interface Jupyter DataLab Azure Notebook
Machine Learning SageMaker TensorFlow Azure ML

19. Customer Journey Dashboard

20. What the Future Holds
• DevOps for Analytics
• Search-Based BI (NLP)
• Artificial Intelligence (AI)
• Virtual Reality BI (VR)
• Virtual Assistant BI (Voice)
• Reporting/Predictions Converge
• Citizen Data Scientists Emerge

21. Thank You
joe@caserta.com
@Joe_Caserta
Joe Caserta
President, Caserta Concepts