The document discusses the importance of data analytics in modern business, highlighting the vast amounts of unstructured data generated and the challenges organizations face in effectively utilizing it. It emphasizes the need for enterprise analytics to improve operational efficiency, customer experience, and governance. Additionally, it outlines various hurdles such as integration complexities, lack of automation, and skill shortages that organizations must overcome to leverage analytics successfully.

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Data-Driven Business
Santosh Tiwari – Head Technology
General Business and Channels

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The analytics economy

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Every day, protecting millions
of transactions from fraud.

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Every day, the world creates
2,500 petabytes
of data. In the past 2 years,
mankind has generated
more data than in the
preceding 5,000 years
combined.
Source: IFL Science
Analysts at Gartner
estimate that
80%
of all enterprise data today
is unstructured.
Source: Gartner
According to an IDG survey,
45%
of business leaders cite
“unstructured data”
as their single biggest hurdle
to overcome in analytics.
Source: IDG
Data Economy

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McKinsey April 2016
86% of executives say their organizations have been at
best ‘only somewhat effective’ at meeting the goals of
their data and analytics programs.

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SAS Data-Driven Business
Drive
Operational
Efficiency
Improve
Customer
Experience
Increase
Profitability
Improve
Governance,
Compliance
& Mitigate
Risk

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PRESERVE
HEAL
PREDICT
DELIGHT
FORECAST
INVENT
OPTIMIZE
INSPIRE
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Enterprise Analytics Enables Future Innovation
On the Edge of Tomorrow
Edge analytics Transparency Security
IoT AI ML

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Reduce cost and secure
Analytics service level
Accelerate analytics
insights delivery
Drive your business through
fact based strategy
and operations
VISION
Augment the value of
business processes
STRATEGY
Adapt to any business
problems with
Analytics diversity
Trust your Analytics
with proven data,
technologies and
processes
Scale Analytics
process to cope with
big data
Build an Enterprise
Analytics Platform
CHALLENGE
Lack of automation and
repeatability
Analytics silos with
different business views of
the same problem
Increased integration
complexity with open source
Stalled analytical
projects with no progress
and missing path to value
Information Technology
and supplier not reliable
Scarce skill sets
on the market
Difficult and costly
to develop and maintain
No proven results and
weak documentation
Not portable to
Legacy software
Do not scale up to
large data and processing
Not secured from
external attack
Not highly available
ENTERPRISE ANALYTICS: CHALLENGEVISIONSTRATEGY

17. Our digital transformation
to power the analytics economy

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Parallel&Serial,Pub/Sub,
WebServices,MQs
Source-based
Engines
Microservices
UAA
Query
Gen
Folders
CASMgmt
DataSource
Mgmt
Analytics
GUIs
etc…
BI
GUIs
Env
Mgr
Model
Mgmt
Log
Audit
UAAUAA
DataMgmt
GUIs
In-Memory
Runtime Engine
In-Database
In-Hadoop
In-Stream Solutions
APIs
Platform
Analytics
Data ManagementFraud and Security
Intelligence
Business Visualization
Risk Management
!
Customer Intelligence
Cloud Analytics
Services (CAS)
Data Enablement
IT Enablement Developer Enablement
Solutions Enablement
Cloud Enablement

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