This document discusses trends in big data and analytics. It shows that the volume of business data worldwide doubles every 1.2 years. It also highlights that movement to cloud-based offerings and adoption of machine learning are adding more value to big data. The convergence of IoT, cloud, and big data is creating new opportunities for customized, self-service analytics architectures.

1. 70
60
50
40
30
20
10
Poor data can
cost businesses
20%-35%of
their operating
revenue
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-
.....
0 •.
Big Data Market
2016 2021
Organizations
leverage Data
Lakes to drive
Streaming Analytics Market
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2021
◄i+i=5
2016
·---------
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Volume of
business data
worldwide,
doubles every
1.2 years
Movement to
the cloud
based
offerings
value Varietydriving
big data
investments
more than
volume or
velocity
Big Data
becomes fast and
approachable
--- ----- - - - --
Adoption of
Spark and
Machine
Learning adds
more value to
Big Data
- -------
Convergence of
loT, cloud and Big
Data create new
opportunities for
self-service
Analytics
Architectures
customized to
reject one-size-
fits all
frameworks
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Better
insights into
business and
customers
Strategy &
Roadmap
Prototyping
&Tool
'- Evaluation
Frameworks for Real­
Time Data processing
and Analytical needs
Create a
data driven
culture
ELT Offload
Architecture
Datastore,
Governance
& Security
Management
Polyglot
Persistence
Ability to
act faster in
decision
making
Analytics,
Dash­
boarding &
Alerting
Scalable Data
Platforms
Scalable Data
Processing &
Storage
Self-Service
capabilities
------------------► Scalable solution to support
100,000 messages I sec for 9
millions users
----------► Real Time Data Collection, Ingestion
and Analytics on Stream data from
various sources
►Creation of Data Lakes which pulls in data from
- - - - - - - the different Silos and provides a common
analytics platform and capabilities
-----------------------►
Access data through common
APls & direct data mart access
provided users to perform in­
depth custom analysis
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