The document discusses the role of data virtualization in deploying enterprise machine learning programs, highlighting its benefits in improving data access and integration. It provides examples from companies like McCormick and Prologis, showcasing how data virtualization enhances analytics speed and simplifies data management. The document emphasizes the importance of a logical data architecture to overcome challenges in data pipeline processes and support advanced analytics initiatives.

1. How Data Virtualization Puts
Enterprise Machine Learning
Programs into Production
Chris Day
Director, APAC Sales Engineering
cday@denodo.com
2 9 S E P T E M B E R 2 0 2 0

2. Agenda1. What are Advanced Analytics?
2. The Data Challenge
3. The Rise of Logical Data Architectures
4. Tackling the Data Pipeline Problem
5. Customer Stories
6. Key Takeaways
7. Q&A
8. Next Steps

3. 4
Advanced Analytics & Machine Learning Exercises Need Data
Improving Patient
Outcomes
Data includes patient demographics,
family history, patient vitals, lab test
results, claims data etc.
Predictive Maintenance
Maintenance data logs, data coming in
from sensors – including temperature,
running time, power level duration etc.
Predicting Late Payment
Data includes company or individual
demographics, payment history,
customer support logs etc.
Preventing Frauds
Data includes the location where the
claim originated, time of the day,
claimant history and any recent adverse
events.
Reducing Customer Churn
Data includes customer demographics,
products purchased, products used, pat
transaction, company size, history,
revenue etc.

4. 5
VentureBeat AI, July 2019
87% of data science projects never make it
into production.

5. 7
McCormick Uses Denodo to Provide Data to Its AI Project
Background
§ McCormick’s AI and machine learning based project required data
that was stored in internal systems spread across 4 different
continents and in spreadsheets.
§ Portions of data in the internal systems and spreadsheets that
were shared with McCormick's research partner firms needed to be
masked and at the same time unmasked when shared internally.
§ McCormick wanted to create a data service that could simplify the
process of data access and data sharing across the organisation
and be used by the analytics teams for their machine learning
projects.

6. 8
• Data Quality
• Multiple Brands
• Which Data to Use?

7. 9
McCormick – Multi-purpose Platform
Solution Highlights
§ Agile Data Delivery
§ High Level of Reuse
§ Single Discovery & Consumption
Platform

8. 10
Data Virtualization Benefits for McCormick
§ Machine learning and applications were able to
access refreshed, validated and indexed data in
real time, without replication, from Denodo
enterprise data service.
§ The Denodo enterprise data service gave the
business users the capability to compare data in
multiple systems.
§ Spreadsheets now the exception.
§ Ensure the quality of proposed data and services.

9. Logical Data Warehouse

10. 12
Gartner, Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs, May 2018
“When designed properly, Data Virtualization can speed data integration, lower data
latency, offer flexibility and reuse, and reduce data sprawl across
dispersed data sources. Due to its many benefits, data virtualization is often the first
step for organizations evolving a traditional, repository-style data
warehouse into a Logical Architecture.”

11. 13
Logical Data Warehouse Reference Architecture
ETL
Data Warehouse
Kafka
Physical Data Lake
Machine
Learning
SQL
interface
Logical Data Warehouse
Streaming
Analytics
Distributed Storage
Files

12. 14
Why A Logical Architecture Is Needed
ü The analytical technology landscape has shifted over time.
ü You need a flexible architecture that will allow you to embrace those shifts rather
than tie you down to a monolithic approach.
ü Only a logical architecture will easily accommodate such changes, and not a
physical architecture.
ü IT should be able to adopt newer technologies without impacting business users.

13. Tackling the Data Pipeline Problem

14. 16
Typical Data Science Workflow
A typical workflow for a data scientist is:
1. Gather the requirements for the business problem
2. Identify useful data
§ Ingest data
3. Cleanse data into a useful format
4. Analyze data
5. Prepare input for your algorithms
6. Execute data science algorithms (ML, AI, etc.)
§ Iterate steps 2 to 6 until valuable insights are
produced
7. Visualize and share
Source:http://sudeep.co/data-science/Understanding-the-Data-Science-Lifecycle/

15. 17
Where Does Your Time Go?
• 80% of time – Finding and
preparing the data
• 10% of time – Analysis
• 10% of time – Visualizing data
Source:http://sudeep.co/data-science/Understanding-the-Data-Science-Lifecycle/

16. 18
Where Does Your Time Go?
A large amount of time and effort goes into tasks not intrinsically related to data science:
• Finding where the right data may be
• Getting access to the data
§ Bureaucracy
§ Understand access methods and technology (noSQL, REST APIs, etc.)
• Transforming data into a format easy to work with
• Combining data originally available in different sources and formats
• Profile and cleanse data to eliminate incomplete or inconsistent data points

17. 19
Data Scientist Workflow
Identify useful
data
Modify datainto
auseful format
Analyzedata Executedata
science algorithms
(ML,AI, etc.)
Prepare for
MLalgorithm

18. 20
Identify Useful Data
If the company has a virtual layer with a good coverage of
data sources, this task is greatly simplified.
§ A data virtualization tool like Denodo can offer
unified access to all data available in the company.
§ It abstracts the technologies underneath, offering a
standard SQL interface to query and manipulate.
To further simplify the challenge, Denodo offers a Data
Catalog to search, find and explore your data assets.

19. 21
Data Scientist Workflow
Identify useful
data
Modify datainto
auseful format
Analyzedata Executedata
science algorithms
(ML,AI, etc.)
Prepare for
MLalgorithm

20. 22
Data Virtualization offers the unique opportunity of
using standard SQL (joins, aggregations,
transformations, etc.) to access, manipulate and
analyze any data.
Cleansing and transformation steps can be easily
accomplished in SQL.
Its modeling capabilities enable the definition of views
that embed this logic to foster reusability.
Ingestion And Data Manipulation Tasks

21. 23
Prologis Launches Data Analytics Program for Cost Optimization
Background
§ Create a single governed data access layer to create
reusable and consistent analytical assets that could be used
by the rest of the business teams to run their own analytics.
§ Save time for data scientists in finding , transforming and
analysing data sets without having to learn new skills and
create data models that could be refreshed on demand.
§ Efficiently maintain its new data architecture with minimum
downtime and configuration management.
Prologis is the largest industrial real estate
company in the world, serving 5000 customers
in over 20 countries and USD 87 billion in
assets under management.

22. 24
Prologis Architecture Diagram
wc_monthly_occupancy_rpt_f wc_lease_amendment_d w_day_d wc_property_d
MARKET_AVAILABILITY_CURRENT MARKET_AVAILABILITY_FUTURE
Prologis
SnowFlake
API
Access
Informatica
Cloud
ShareHouse
ODBC JDBC
peoplesoft_gl_actuals yardi_unit_leasing p360_property
WAF
AWS Lambda APIs

23. 25
Data Virtualization Benefits Experienced by Prologis
§ The analytics team was able to create business focussed subject areas with
consistent data sets that were 30% faster in speed to analytics.
§ Denodo made it possible for Prologis to quick start advanced analytics projects.
§ The Denodo platform’s deployment was as easy as a click of a button with
centralized configuration management. This simplified Prologis’s data architecture
and also helped bring down the overall maintenance cost.

24. 26
ü Denodo can play key role in the data science ecosystem to reduce data
exploration and analysis timeframes.
ü Extends and integrates with the capabilities of notebooks, Python, R, etc.
to improve the toolset of the data scientist.
ü Provides a modern “SQL-on-Anything” engine.
ü Can leverage Big Data technologies like Spark (as a data source, an
ingestion tool and for external processing) to efficiently work with large
data volumes.
ü New and expanded tools for data scientists and citizen analysts: “Apache
Zeppelin for Denodo” Notebook.
Data Virtualization Benefits for AI and Machine Learning Projects

25. More Information?

26. 28
Next Steps
Access Denodo Platform in the Cloud
Take a Test Drive today!
bit.ly/32rJ8JQ
GET STARTED TODAY

27. 29
D E N O D O V I R T U A L L U N C H & L E A R N A S E A N :
Respond Quickly in a Crisis
with a Logical Data Layer
Thursday, 15 October 2020
12.00pm - 1.30pm SGT
REGISTER YOUR INTEREST
bit.ly/2Ro9PZF
Elaine Chan
Regional Vice President,
Sales, ASEAN & Korea
Chris Day
Director,
APAC Sales Engineering

28. Bridging the Last Mile: Getting Data to the People
Who Need It
Thursday, 22 October 2020 | 10.00am SGT | 1.00pm AEDT REGISTER HERE
bit.ly/3hn6eWs
Chris Day
Director, APAC Sales Engineering, Denodo
Sushant Kumar
Product Marketing Manager, Denodo

29. Thanks!
www.denodo.com info@denodo.com
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