4. Modern Data Ecosystem Architecture
Alternative
Data
S3
RAD
Polaris
Report DM
Phase 1
Landing
(Raw Data)
Datalake
(Designed Data)
Data Science
(Ephemeral)
• Python / SQL /
Predictive / R
• Business Intelligence
Structured
Data Marts
• Data Catalog
SRM
Bloom berg
Salesforce
Phase 2
Middle Office
Treasury DB
Mortgage DB
Mongo DB
Phase 3
Black Mountain
C obra
IT2
Phase 4
Geneva
Investran
2021
Highlights: Ma naged data ecosystem on the cloud; Centra lized , sta ndard ized ETL,
Holistic Enterprise Data Warehouse fo r a ll repo rting . Integra ted Data La bora tory to
support Alternative Data initiatives. Unified a dvanced analytics platfo rm a nd too lset.
Phased approach to landscape simp lification.
Data
Pipelines
6. What’s Next? ….Data Spiders, Bots & NLP
Alternative
Data
S3
RAD
Polaris
Report DM
Phase 1
Landing
(Raw Data)
Datalake
(Designed Data)
Data Science
(Ephemeral)
• Python / SQL /
Predictive / R
• Business Intelligence
Structured
Data Marts
• Data Catalog
SRM
Bloom berg
Salesforce
Phase 2
Middle Office
Treasury DB
Mortgage DB
Mongo DB
Phase 3
Black Mountain
C obra
IT2
Phase 4
Geneva
Investran
2021
Highlights: Ma naged data ecosystem on the cloud; Centra lized , sta ndard ized ETL,
Holistic Enterprise Data Warehouse fo r a ll repo rting . Integra ted Data La bora tory to
support Alternative Data initiatives. Unified a dvanced analytics platfo rm a nd too lset.
Phased approach to landscape simp lification.
Data
Pipelines
DATA SPIDERS ML/ BOTS NLP
Success Story: Mark Ramsey, R&D Chief Data & Analytics Officer, GSK
7. 1. Data spiders are coming to fruition in a significant way
(auto discovery)
Data Spiders (detect databases, data sets and schemas)
Modak Analytics: Smart data discovery, Automated
Ingestion, Kosh Repository: Developed by Modak for
GSK
8. 2. Bots must be used to build data pipelines (auto ETL)
Machine Learning/Bots to scour data
Infer joins across data sets
Automatically build pipelines to transform and load them
9. 3. Use Artificial Intelligence to unify data
AI will reveal data quality defects, dynamically unify data
structures
Tamr: Machine Learning for Data Quality and Unification
Kinetica: High-volume streaming; simultaneous ingest
and analysis (used by Porche)
10. 4. Traditional BI will be replaced by NLP and chatbots
NLP (Google-like search and/or voice activated chatbot
for data user experience)
WolframAlpha: Google-like interface to query information
for graphical results
AnswerRocket: AI/ML/NLP BI Platform
Ask Data: Latest offering by Tableau
11. 5. Organizations must Offer Data Literacy Programs
Data literacy is the ability to read, work with, analyze,
and argue with data. Much like literacy as a general
concept, data literacy focuses on the competencies
involved in working with data
Data literacy programs must be established to educate
and certify all employees
12. 6. Organizations should measure their analytics maturity
Quality
Speed As it becomes mandatory for companies to become
analytics driven, analytics maturity will be officially
measured.
An analytics maturity model is a tool that helps
organizations assess the current effectiveness of a
person or group and supports figuring out what
capabilities they need to acquire next in order to improve
their performance
13. 7. Methods are being formalized to assess data valuation
14. Foundational Value of Data8. Convergence of digital and data transformations
As digital transformation becomes pervasive in every
organization, the importance of data will be at the
forefront
We will see a convergence of the Chief Digital Officer
and Chief Data Officer objectives
16. Foundational Value of Data10. 2020 - The year of the healthcare data revolution
Link to The Future in Healthcare
white paper:
http://bit.ly/healthcare-data-driven
17. Like any Evolution – Adapt or be Left Behind
Be the Change Agent – Be the Disruptor!
