The document outlines key trends in data strategy, emphasizing the role of AI and machine learning in simplifying data management and enhancing insights for business decisions. It discusses the adoption of hybrid cloud architectures and data fabric technologies for effective data integration and analytics. Furthermore, it highlights the growing importance of APIs in data monetization and digital transformation initiatives.

1. ASEAN WEBINARS
The Future of Data Strategy
Five trends that will shape what’s coming next

2. Speaker
Paul Moxon
SVP Data Architecture & Chief Evangelist
Denodo

3. 3
…It’s Difficult to Make Predictions, Especially
About the Future.”
Attributed to Niels Bohr
(Bulletin of the Atomic Scientist, 1971)

4. 4
Analysts: “predict” the future by looking at the present

5. 5
But The Future Can Hold Surprises…
Motorola Razr 2007 Apple iPhone 2007

6. 6
ML and AI as to Simplify
Data Management
Challenges

7. 7
ML and AI to Simplify Data Management Challenges
§ Data science practices are already
common in many companies to
produce better insights that enable
business decisions
§ Data Scientists have been one of the
most popular jobs in recent years
§ Currently common practice for
resource allocation, supply chain
management, fraud detection,
predictive analytics, etc.
§ Denodo is already frequently used in this
scenarios as a way to simplify and
accelerate data exploration and analysis
https://www.denodo.com/en/webinar/customer-keynote-data-virtualization-modernize-and-
accelerate-analytics-prologis

8. 8
Artificial Intelligence in Data Management
§ Software vendors have started to incorporate similar
techniques to analyze their data and automate all kind
of tedious tasks
§ These techniques can provide actions and expertise that
otherwise required manual intervention of a human
expert
• Scales to process large data volumes
• Reduces the workload of repetitive tasks on skilled
profiles
§ In the data management space, one of the first
successful applications of these techniques is helping to
identify data quality issues and potentially sensitive data
§ Many vendors now incorporate some form of AI
tagging, automatic classification, ML security
assessment, etc.
https://www.wsj.com/articles/how-data-management-helps-companies-deploy-ai-11556530200

9. 9
Artificial Intelligence in Data Management

10. 10
Application in Data Virtualization
§ Enhance data discovery
§ Dataset recommendations based on your profile
§ Simplify data modeling
§ Relationship discovery based on usage analysis
§ Suggestions for filters
§ Improve performance
§ Tuning recommendations
§ Self healing bottlenecks

11. 11
Welcome to a Hybrid World

12. 12
Denodo Global Cloud Survey 2020
• More than 75% of companies already have projects in cloud
• Over 15% are Cloud-First and/or are in “advanced” state
• Only 3.97% do not have plans for Cloud in the short term
• More than 53% have hybrid integration needs
• Key Use Cases include: Analytics (50%), Data Lake (31%), AI/ML (28%)
• Less than 9% of on-prem systems are decommissioned (Forrester estimates 8%)
• Key Technologies in Cloud Journey: Cloud Platform Tools (56%), Data Virtualization (49.5%),
Data Lake Technology (48%)
Source: Denodo Global Cloud Survey 2020

13. 13
Avoid Hybrid/Multi-Cloud Point-to-Point Connections
Source: By Unknown author - Tekniska museet, Public Domain, https://commons.wikimedia.org/w/index.php?curid=3877011

14. 14
Logical Multi-Cloud Architecture

15. 15
Data Fabrics Will Be
Pervasive

16. 16
Data fabric is a hot, emerging market that delivers a unified, intelligent, and
integrated end-to-end platform to support new and emerging use cases.
The sweet spot is its ability to deliver use cases quickly by leveraging
innovation in dynamic integration, distributed and multicloud architectures,
graph engines, and distributed in-memory and persistent memory platforms.
Data fabric focuses on automating the process integration, transformation,
preparation, curation, security, governance, and orchestration to enable
analytics and insights quickly for business success.
The Forrester Wave: Enterprise Data Fabric, Q2 2020
Noel Yuhana

17. 17
Can we just have a repository for all data?
• Loss of capabilities: data lake capabilities may differ from those of original
sources, e.g. quick access by ID in operational RDBMS
• Huge up-front investment: creating ingestion pipelines for all company datasets
into the lake is costly
• Questionable ROI as a lot of that data may never be used
• Replicate the EDW? Replace it entirely?
• Large recurrent maintenance costs: those pipelines need to be constantly
modified as data structures change in the sources
• Risk of inconsistencies: data needs to be frequently synchronized to avoid stale
datasets
COST
GOVERNANCE
Can’t we put all company data in a single super repository? Would that be possible?
Is that realistic?

18. 18
Gartner – The Evolution of Analytical Environments
This is a Second Major Cycle of Analytical Consolidation
Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
Operational
Application
Operational
Application
Cube
Operational
Application
Cube
? Operational Application
Operational Application
Operational Application
IoT Data
Other NewData
1980s
Pre EDW
1990s
EDW
2010s
2000s
Post EDW
Time
LDW
Operational
Application
Operational
Application
Operational
Application
Data
Warehouse
Data
Warehouse
Data
Lake
?
LDW
Data Warehouse
Data Lake
Marts
ODS
Staging/Ingest
Unified analysis
› Consolidated data
› "Collect the data"
› Single server, multiple nodes
› More analysis than any
one server can provide
©2018 Gartner, Inc.
Unified analysis
› Logically consolidated view of all data
› "Connect and collect"
› Multiple servers, of multiple nodes
› More analysis than any one system can provide
ID: 342254
Fragmented/
nonexistent analysis
› Multiple sources
› Multiple structured sources
Fragmented analysis
› "Collect the data" (Into
› different repositories)
› New data types,
› processing, requirements
› Uncoordinated views
“Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs”. Henry Cook, Gartner April 2018

19. 19
Gartner – Logical Architectures
“Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs”. Henry Cook, Gartner April 2018
DATA VIRTUALIZATION

20. 20
Gartner: Five Key Pillars of a Modern Data Fabric Design
Data
Consumers
Data
Sources
Final Data Integration and Orchestration Layer
Insights and Automation Layer
Active Metadata
Knowledge Graph Enriched With Semantics
Augmented Data Catalog
Data
Consumers
Data
Sources
Data Fabric

21. 21
What is a Data Fabric?
Data Fabric
Location
Customer
Products
RDBMS/OLTP Traditional Analytics/BI Data Lakes Cloud Data Stores Apps and Document
Repositories
Flat Files
Third Party
Legacy
Mart
Data Warehouse
Mart
ETL ETL
XML • JSON • PDF
DOC • WEB
Applications/APIs
REST OData
SOAP/XML GraphQL
Supplier
Data Integration Services
Data Fabric Services Data Compute Services
Data Marketplace Data Access Services
Management
Services

22. 22
What is a Data Fabric?
RDBMS/OLTP Traditional Analytics/BI Data Lakes Cloud Data Stores Apps and Document
Repositories
Flat Files
Third Party
Legacy
Mart
Data Warehouse
Mart
ETL ETL
XML • JSON • PDF
DOC • WEB
Applications/APIs
REST OData
SOAP/XML GraphQL
Data Integration Services
Data Fabric Services Data Compute Services
Data Marketplace Data Access Services
Management
Services
Data Steward
Sys Admin
Data Fabric
Admin

23. 23
Voice Control and NLP

24. 24
Voice Control and NLP
§ Voice control has already taken over our homes
§ Siri, Alexa, Google Home can give you the weather,
read the daily news, control lights and thermostats,
etc.
§ In BI and Analytics, systems are starting to adopt
natural language as a way to query the system by
non technical users
§ As this technologies progress, business users and
sales reps in the field will be able to ask for
complex information from their phones and tablets

25. 25
Voice Computing: Humanizing Data Insights
Natural Language Processing enabled business users to post a question to a chatbot and receive an
answer with data insights that are completely humanized
“The total Q3 sales for Product A in
Mexico totaled $200.4 M, a 15%
increase from Q2”
“What are the
Q3 sales
trends for
Product A in
Mexico?”

26. 26
Data Monetization
and the API Economy

27. 27
Data Monetization and the API Economy
§ The market for data applications is predicted to
have the largest growth by segment in coming
years
§ Application to application communication is
done via APIs, and therefore APIs have become
the cornerstone of many digital transformation
initiatives
§ API access (vs direct access through their
website) already accounts for a significant
portion of the revenue of Internet giants
§ There is also a significant market of companies
that use data as their main asset, and their
business model is to “sell APIs”
§ In addition, traditional companies have started to
use their data as an additional asset
https://www.statista.com/statistics/255970/global-big-data-market-forecast-by-segment/

28. 28
DrillingInfo APIs Enable Data Monetization

29. 29
Using APIs to Add A Competitive Edge

30. 30
Denodo Data Services
§ Data virtualization enables API access to any data
connected to the virtual layer, with zero coding
§ It includes security controls to show different data
depending on the user/role
§ You can add complex workload management policies,
including quotas (e.g. 100 queries/hour)
§ Denodo supports a wide range of protocols and options
§ GraphQL
§ GeoJSON (geospatial APIs)
§ OData 4
§ OAuth 2.0, SAML and SPNEGO authentication
§ OpenAPI (pka Swagger) documentation

31. Q&A

32. Next Steps

33. 33
denodo.link/drive2108

34. Modernizing Data Architecture
Using Data Virtualization
REGISTER NOW
denodo.link/apacwb2109
APAC Webinar | 16 Sep | 11am SGT
Chris Day
Director, APAC Sales Engineering
Denodo

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