1. Out of the swamp Suggestions to bring your analytics back on track
ANALYTICSINSTITUTE
problems worth solving
Suggestions to bring your analytics back on track
MAX COTTICA (Executive M.A.I.I.)

2. Computer Shop Clerk
(5 years)
IT Development Manager
(10 years)
SQL DBA/Developer
Data Warehouse Developer/Junior Manager
(6 years)
Data Warehouse Manager
About me
Global Data Integration Senior Manager
(4 years)
Head of Data Science
And BIG Data Solutions
(1 year)
1980s
1990s
2000s
2010s
2017

3. 1980 ‘80s ‘90s ‘00s ‘10s Max vs. Analytics in the years: TIER 0 TIER 1
What happened
1980
‘80s
‘90s
‘00s
‘10s
“My goal was to produce reports.”
TIER 0
Descriptive Analytics
Roll-ups and how, when and where
“My goal was to produce ad-hoc reports for a large variety of
departments using a centralized media for distribution.”
TIER 1
Identify problems and fire alerts
Diagnostic Analytics
“My goal was to aggregate and integrate data and offer analytics
based on different segmentations like time or geography .”
TIER 2
Why and forecasting
Predictive Analytics
“My goal was to use aggregated reports to produce KPI reports and
to use predictive models to estimate future growth.”
TIER 3
Prescriptive Analytics
“My goal is to use predictive analytics in conjunction
with scenario based algorithms to produce prescriptive analytics
and actionable events.”
What will happen if…
Next best action

4. Data fit to analytical purpose
Data assurance
Data availability

5. We overloaded the lake with raw and meaningless information
That added overhead to the process of discovering, integrating and transforming/aggregating data
That also fragmented the tools required to do the job
Times for POCs, demonstrations, quick wins, tactical and low hanging fruits went up exponentially due to heavy data wrangling
We lost faith in data modelling, data architecture, metadata and data assurance
Because of that there has been a huge proliferation of data outside of the lake
WHAT happened ?

6. Agile methodologies applied at delivery level and not from the top down
Poor understanding of data assets, no idea where data is
Poor or no ODS strategy
Lack of product owners, data stewards and data governance
Poor master data and reference data management
Adoption of a raw data layer with no system refined data
Application of security and regulations on top of existing landscape
Poor data definitions and integration
Lack of data modelling and poor data design and architecture
Lack of a metadata and data quality strategy
Complex security models
Why did it happen ?

7. How do I get back on track ?
Adopt SAFe as a scaled agile methodology, funnel projects don’t sieve them
Know your data from source and make sure it is fit for analytical purposes
Start a data governance and stewardship program
Adoption of data modelling as an enterprise tool, again
Adoption of a data assurance strategy
Adoption of a raw layer and a refined layer in your logical data warehouse or data lake
Adoption of data lineage and metadata capturing
Improve data availability
Standardize your platforms and development tools
Provide better or ODS functionality
Consider a dedicated Chief Data Office
In a large enterprise consider a dedicated Chief Analytics Office
Security and regulations must be part of the data fabric
How do I get back on track ?

8. Building your chief data office
Head of Data
Regulatory
Head of Data Governance and Architecture
Head of Data Management and Design
Head of Data Security and Integration
Head of Analytics and Visualization
Risk
Data Architects
Governance
Metadata
Security
BI & Monitoring
Infrastructure
Architecture
Design
EQLT
Data Science
Stewardship
Master Data
Big Data MI
Data Modellers

9. Building a CHIEF ANALYTICS OFFICE
Machine Learning, Deep Learning, AI, Natural Language Processing, Insights… BI
BI on BI
Tooling
Licensing
Monitoring
Alerts
Functional
Data Model
Data Science
Centralized Code
End User Security
Scientific
Data Model MI
Advanced Analytics
Standard Reports
Predictive, prescriptive, forecast and propensity models

10. Building your execution layer
Data, Master Data and Metadata Management
Procurement
Integration (EQLT)
Consumption
Quality
Presentation
Monitoring
Modelling
Connection
Ingestion
Engagement
Acquisition
Visualization
Analytics
DATA
DEFINITION
DOCUMENT
Physical Model
Functional Model
Scientific Model
SECURITY
MANIFESTO
STTM
Logical Model
Data Architecture and Design
Conceptual Model
Data Governance and Stewardship

11. BUILDING an analytics or data science team
Feature Catchers
SQL Gurus
Data Miners
Data Wranglers
Data Druids
Data Scientists
Data Analysts
Data Engineers
Lighthouse
Agile leaders
Tech Leaders
Senior Analysts
Senior Scientists
Last Guardians
Test Engineers
QA Engineers
CICD

12. Transformed data store
BUILDING YOUR ON PREM HADOOP ecosystem
Atomic data store
Transformed data store
Real-time
events
CLOUD
Star Schemas
1:1 Schema
Application
transactions
EDW
Once off
EQLT
EQLT
EQLT
ODS 2
MapR-FS
VIZ
Third party
Manual files
VIZ
MORE VIZ

13. DL NLP ML Building a real time solution
(to disrupt the recruitment industry) CV
Mobile ML
NLP DL
Cover Letter AI
BOT
Matches
Employers
Employees
LinkedIn
Profile
WebServices
APIs
WEB UI
Job Specs

14. The data river for analytics: C2A-A2C
NBA
NBA
Customers
NBA
NBA
NBA
NBA
NBA
NBA
NBA
Batch
NBA
Reference
Real Time
NBA
NBA
Digital Channel
NBA
NBA
NBA
NBA

15. ANALYTICSINSTITUTE
problems worth solving