The document discusses the future of data strategy with a focus on the role of machine learning and AI in simplifying data management challenges, enhancing data virtualization, and improving business analytics. It highlights trends towards hybrid cloud integration, the adoption of APIs for data monetization, and the necessity for adaptive data architectures to accommodate emerging technologies. Furthermore, it emphasizes the importance of using data virtualization to streamline processes and facilitate analysis in various contexts, including predictive analytics and supply chain management.

1. WEBINAR
Future of Data Strategy

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

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

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
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

10. 10
Welcome to a Hybrid
World

11. 11
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

12. 12
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

13. 13
Logical Multi-Cloud Architecture

14. 14
Voice Control and NLP

15. 15
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

16. 16
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?”

17. 17
Data Monetization
and the API Economy

18. 18
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/

19. 19
DrillingInfo APIs Enable Data Monetization

20. 20
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

21. 21
Data Fabrics and Adaptive
Data Architectures

22. 22
Data fabric focuses on automating the process integration,
transformation, preparation, curation, security, governance, and
orchestration to enable analytics and insights quickly for business
success. It minimizes complexity by automating processes,
workflows, and pipelines, generating code and streamlining data
to accelerate various use cases such as customer 360, data
science, fraud detection, internet-of-things (IoT) analytics, risk
analytics, and healthcare insights.”
The Forrester Wave™: Enterprise Data Fabric, Q2 2020

23. 23
Gartner – The Evolution of Analytical Environments
Logical Architectures are a Second Major Cycle of Analytical Consolidation
Operational ApplicationOperational Application
Operational ApplicationOperational Application
Operational ApplicationOperational Application
IoT DataIoT Data
Other NewDataOther NewData
Operational
Application
Operational
Application
Operational
Application
Operational
Application
CubeCube
Operational
Application
Operational
Application
CubeCube
?? Operational ApplicationOperational Application
Operational ApplicationOperational Application
Operational ApplicationOperational Application
IoT DataIoT Data
Other NewDataOther NewData
1980s
Pre EDW
1990s
EDW
2010s2000s
Post EDW
Time
LDW
Operational
Application
Operational
Application
Operational
Application
Operational
Application
Operational
Application
Operational
Application
Data
Warehouse
Data
Warehouse
Data
Warehouse
Data
Warehouse
Data
Lake
Data
Lake
??
LDWLDW
Data WarehouseData Warehouse
Data LakeData Lake
MartsMarts
ODSODS
Staging/IngestStaging/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

24. 24
Adaptive Data Architectures
• Organizations need an adaptive data architecture
• An architecture that can flex and adapt to new technologies, new data sources, new
formats, new protocols, etc. while minimizing the impact on the consumers
• Future-proofs the architecture
• Despite the preceding slides…we can’t predict what technologies will emerge in next 3-5
years (or 5-10 years), but we can build architectures that will accommodate them
• Allows users to access new data, new technologies using existing, familiar tools
• e.g. read data from a Parquet file using Excel (via the Data Virtualization Platform)
• A Data Fabric – built on Data Virtualization – provides this adaptability and protects your
existing technology investments and de-risks the adoption of new, emerging technologies

25. 25
Adaptive Data Architecture
Reporting
Analytics
Data Science
Data Market Place
Data Monetization
AI/ML
iPaaS
Kafka
ETL
CDC
Sqoop
Flume
RawDataZoneStagingArea
CuratedDataZoneCoreDWHmodel
Data Warehouse
Data Lake
Data Virtualization Platform
Analytical Views
Data Science Views
λ Views
Real-Time Views
DWH Views
Hybrid Views
Cloud Views
UniversalCatalogofDataServices
CentralizedAccessControl
Enterprise Data Fabric

27. 27
Next Steps
Access Denodo Platform in the Cloud!
Take a Test Drive today!
www.denodo.com/TestDrive
GET STARTED TODAY

28. www.denodo.com info@denodo.com
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