1. Transforming Data Management
for the Cloud

2. Distributed Data Across Cloud and
On-Premises – Opportunities and
Challenges
Philip Russom, Ph.D.
Independent Industry Analyst for Data Management
and Analytics

3. Distributed Data
Across Cloud and
On-Premises:
Opportunities and
Challenges
PHILIP RUSSOM
INDEPENDENT INDUSTRY ANALYST

4. What you will learn
Business Opportunities for Today’s
Data
The Challenge of Distributed Data
Strategies for getting Business
Value from Distributed Data
◦Logical data architectures, Data
fabric, and Data virtualization

5. Business Opportunities
based on Leveraging Enterprise-wide Data
▪ Many organizations are positioned to get
greater business value from data
– Data management skills and infrastructure are
mature
– They have plenty of data
▪ If they could access the full wealth of their data,
they could leverage the data for:
– More impactful decisions, advanced analytics,
operational efficiency, customer service, cost
reductions

6. Challenges to
Leveraging and Reusing Enterprise Data
▪ Valuable enterprise data is distributed across many IT
systems
– Each with its own data models, semantics, and interfaces
▪ Makes data resources hard to find and slow to process
for multiple business use cases and user types
– Users need data mgt solutions and strategies
that address distributed data
▪ Data on cloud(s) exacerbates the problems of distributed
data
– Hybrid (on prem/cloud), Multi-Cloud, Silos migrated to

7. SOURCE: Philip Russom 2023
Leave Data
Silos, as is
Re-Enginee
r Most Data
Virtual
Views
Logical Data
Architecture
s
Business Value from
Distributed Data
Time
and
Cost
of
Effort
Minimal
Effort
Maximum
Effort
Little or No
Value
Added
Greatest
Value
Consolidate Data
into Fewer
Datasets
Collocate Data
onto Fewer
Platforms
Data Strategies
for Getting
Business Value
from Distributed
Data
YOUR GOAL – Look for data
strategies that provide the
most business value, but
from minimal technical
effort
That’s the upper right-hand
corner of this chart
Migrate
Data to
Cloud
Data
Fabric

8. Logical Data
Architecture,
with Fabric
&
Virtualizatio
n
Multiple Data
Strategies can Work
Together, to get
Business Value from
Distributed Data
Data Consumption = Rpts, Analytics, Data
Science
Distributed Data
Sources
• ERP, CRM, SFA, Mktg
• Financials,
Operations
• Procurement,
Shipping
• Many more sources
Aggregated Data
• Data
Warehouse
• Data Lake
• Data
Lakehouse
• ODSs, marts…
Logical
Data
Architecture
Metadata and Other Data Semantics
Data Integration, Quality, Streams, CDC,
APIs
Data Virtualization
= Logical Layer. Central Sec/Gov. RT Delivery.
Data
Fabric
SOURCE: Philip Russom 2023

9. Business Value gained from
Virtual and Logical Data Strategies
▪ Quick and cost-effective strategies
– As compared to data collocation, consolidation, and migration
▪ Agile development methods yield shorter time-to-use
▪ Logical methods can reduce operating costs
– And enable creative operating models for development
▪ Simplified, standardized, and controlled access to distributed
data
▪ Views of data specific to data domains, user types, and use
cases

10. High-Value Use Cases enabled by
Virtual and Logical Data Strategies
▪ Business-friendly virtual views enable self-service data
access
– For self-service data exploration, prep, visualization, and
analytics
▪ Single point of entry to distributed data
– For better security, governance, data standards, and
consistency
▪ Abstraction layer easily adapts to source system changes
– Simply adjust the layer, not the many tools that access the layer
▪ Virtual data often means fresher data for reports and
dashboards

11. Conclusion: Leveraging Distributed Data is
best done with Logical/Virtual Strategies
▪ Distributed data is a business opportunity to be
seized
– Enables more impactful decisions, agile analytics,
operational efficiency, faster time-to-insight
▪ Distributed data is a tech challenge to be addressed
– Makes more enterprise data accessible for leverage,
and embraces new technologies and data design
methods
▪ Logical/Virtual Strategies excel with distributed data
– Logical data architectures, data fabric, and

12. Distributed Data Across Cloud and
On-Premises – Opportunities and Challenges
SVP Data Architecture and Chief
Evangelist, Denodo
Paul Moxon
Independent Industry Analyst for
Data Management and Analytics
Philip Russom, Ph.D.
FIRESIDE CHAT

13. Thank you!