Logical Data Fabric: Maturing Implementation from Small to Big (APAC)

DATA
VIRTUALIZATION
APAC WEBINAR
SERIES
Sessions Covering Key Data
Integration Challenges Solved
with Data Virtualization

Logical Data Fabric: Maturing Implementation
from Small to Big
Chris Day
Director, APAC Sales Engineering, Denodo
Deb Mukherji
Practice Head, Data Analytics & AI (ASEAN & North Asia), Tech Mahindra

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Copyright © 2020 Tech Mahindra. All rights reserved.
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Typical Challenges with Data
Lack of agility to react to ever changing business environment
to deliver business outcome
DQ issues – as data is intercepted and transformed in multiple
transient points, thus losing its integrity
Trust on data plummeting with its explosion
IT Alignment to business is increasingly a challenge
Business problem solving needing sharper focus from IT, with
greater cohesion from engineering
Business requirements captured in multiple cycles, thus lacking
robustness in insights
Stable but still flexible data architecture needed to aid cutting
edge pursuits on data
Need to circumvent conventional route to quickly take
customers into digital journey

Logical Data Fabric vs Physical Data Warehouse
Logical Data Fabric
 Capable of integrating disparate source data together, leaving data untouched while
providing business with a unified view.
 Eliminates cost and time constraints associated with data replication and storage in a
physical warehouse
 Logical Data Fabric provides real-time access to the source of data in a fast and
efficient manner
Physical Data Warehouse
Data Latency
Provisioning new data is a
challenge with traditional ETL
& data warehousing systems
Difficult to realize value from
data
Data Preparation
Time
With Traditional systems, on
average, time spent is 61% or
more compared to time spent
on analytics
ETL Challenges
 High Expense
 Scalability Issues
 Slow performance

Identify
Use Cases
Evaluate
benefits
Start with
One Use Case
Experience
Benefits
Advocate
Internally Expand
Replicate Other Use Cases
Regulatory
Compliance
Customer 360 Faster and Accurate
Data Delivery
Data
Governance
Plugging Revenue
Leakage
Logical Data Fabric : The Approach
Use Cases

Logical Data Fabric : Good for Projects
Fastrack analytics and
business intelligence
Single, logical views of data
from across fabric
Data governance and
security adherence
Flexibility in bridging data silos
without data movement
Virtual data integration,
management & harmonization
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Reduce costs associated with data movement and replication
Cost Reduction
Near Real & Real-Time access to data for improved BI &
analytics
Improved BI & Analytics
Flexible architecture, enhanced insights enable better
decisions
Customer Satisfaction
Access to data from source, reducing need for movement or
storage
Easy of Scalability
Efficient usage of h/w and s/w resources; Reusable
components
Optimized use of Resources
Capable of bridging data silos without need for data
movement
Flexibility compared to Physical setup
ROI Drivers

Start in small phases – it’s prudent to start small for defined use cases rather than taking an enterprise-wide
approach:
Example – for Supply Chain Management project, implementation of Data Fabric can be in phases to realize
benefits
 Implement it for an immediate problem in a particular department/LOB
 Demonstrate ROI to C-level execs – CTO, CDO, even CEO
 Expand to future projects in other departments/LOBs
 Make Logical Data Fabric the core part of your enterprise data architecture
Challenges
• Manual orders view & metrics
• Enablement of root cause
analysis of manual orders
• Any upcoming near production
stoppage scenarios
• Inaccurate forecast
• Demand plan and Production
plan not in sync
Challenges
• High inventory holding cost
• Inventory inaccuracy
• Inventory visibility
• Parts going obsolescent
tity/Phase
Phase 1 – Order Phase 2 – Inventory Phase 3 – Supplier Phase 4 – Sales & Service
Challenges
• Supplier performance issues
• Inaccurate subcontractor
data
• Delayed deliveries and
arrivals
• Limited visibility of supplier
supply chain data
Challenges
• Delay in processing warranty
claims
• Delay in delivery and payment
collection
• Challenge is tracking parts
return
• Challenge in sharing warranty
cost with supplier
Implementation Approach

Smaller business cases are easier to achieve and
replicate
To showcase comparison of ROI between
physical and logical data warehouse
implementations
Easier to plan and execute smaller
engagements with new offerings/solutions
Availability of resources for new products is
a challenge and often costly affair
To keep initial investment low
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Our Recommendations
Recognizable benefits from smaller
projects make better business cases
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9
Pathway to success
in digital odyssey
June 2020
+

Combining industry best practice business
know how and KPIs with the fastest ROI
realisation
Business Insights to decision making points
reach faster than conventional methods
Save 25-40% of efforts, time and cost over
a grounds up approach to target KPI
definition for Phase 1 implementation
Ready for ongoing Improvement
Start small and enhance your KPIs and reporting
in the fastest possible turn around time to adjust
to business changes
Adjust your reporting and KPIs to ongoing and
unforseen business changes faster than
competition easily
Save up to 80% of time for ongoing
improvements compared to traditional methods
Reduce Operations Cost
A significant reducer of devops lifecycle for
any data/analytics and Data science needs
DataOps efficiency up by 2 to 4 times,
compared to conventional methods
Lower TCO - 45% reduction in integration
costs, in terms of resources and
technology
Reduce Project Risk with industry proven
solutions
Sizeable reduction in the risk of failed
implementations – assisted by business centric
approach to design & development
More than 5 0 implementations worldwide of
TechM iDecisions® across 10+ industries
Denodo deployed at over 800 companies world
wide
Better Decision Taking
Noticeable reduction of data quality
issues to the tune of 30-50%
Leverage real time data
Compliance
One single platform for Self-
Service, Security and Governance
Joint Value Proposition

Reference Architecture of Denodo + iDecisions®
Denodo Data Virtualization
iDecisions® Business KPIs Orchestration
CONNECT
iDecisions®
data model
COMBINE
Discover,
Transform, Prepare,
Improve Quality,
Integrate
CONSUME
Share, Deliver,
Publish, Govern,
Collaborate
iDecisions® iDecisions®

Connectivity Layer
Integration Layer
iDecisions®
Logical Data Model
iDecisions® KPIs
Reporting / Web Services Layer
Phase #1
• KPIs are fed with data from the data sources
• Denodo enables shorten time to market of data delivery
• Leads to faster turnaround for business
Phase #3
• The implementation of a KPI (interface view) can
be switched at any time (e.g. from the original
sources to the EDW)
• Changes in the implementation are transparent for
business users
• When it is not a problem, KPIs can still get data (or
part of the it) from the actual data sources
Phase #2
• In parallel, logical Data Model can be defined and
populated from the actual sources into a physical EDW
• Denodo + traditional ETLs can be combined here
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Solution components of Denodo + iDecisions®

• Eliminate DQ issues & Data
Latency
• Reads data directly from
source systems in real time
• Take data faster to decision
making point with higher
accuracy
• Faster to demonstrate results
to business users
• Can connect to any type of
data source – Structured
and Unstructured
• Can handle data in motion
as well as data at rest
• Both On-premise and Cloud
• Reporting layer can directly
connect to Logical data fabric
• High degree of permeation of
data driven insights
• Eliminate risk of failed
implementation
• Elimination of rework
• Maximizing Trust in Data
• Quicker ROI in less time
• Take customers deep into
digital journey in less time
• High degree of engineering
alignment to business
requirements
• Bring scale of economy in
time/cost
iDecisions + Denodo : Benefits

Product Demonstration
Director, APAC Sales Engineering, Denodo
Chris Day

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Demo Scenario
Distributed Data:
 Historical sales data offloaded to
Hadoop cluster for cheaper storage
 Marketing campaigns managed in an
external cloud app
 Customer details table, stored in the
DW
1) On-board and expose distributed data
through a single logical layer.
2) Publish a logical view calculating the
impact of a new marketing campaign
by country?
Sources
Combine,
Transform
&
Integrate
Consume
Base View
Source
Abstraction
Sales Campaign Customer
Sales Evolution

Logical Data Lakes: From Single Purpose to
Multipurpose Data Lakes
Thursday 15 April
1:00pm AEST | 11:00am SGT | 8:30am IST
https://bit.ly/2Q8qMKg

Thanks!
www.denodo.com info@denodo.com
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Logical Data Fabric: Maturing Implementation from Small to Big (APAC)

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