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Data And Analytics

Data and Analytics refers to ways data is managed to support all uses of data, and the analysis of data to drive improved decisions, business processes and outcomes, such as discovering new business risks, challenges and opportunities.

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Tap The Data Ecosystem.

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Amplify The Data Advantage With Cloud.

Overview

Benefits

Capabilities

Features

Shreya Pandey
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Finemate Consultants Pvt. Ltd
(4.5 Ratings)
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Data And Analytics Overview

Data and analytics is especially important to modern businesses as it can improve decision outcomes for all types of decisions (macro, micro, real-time, cyclical, strategic, tactical and operational). At the same time, D&A can unearth new questions and innovative solutions to questions — and opportunities — that business leaders had not even considered.Data and analytics is also a catalyst for digital strategy and transformation as it enables faster, more accurate and more relevant decisions in complex and fast–changing business contexts.

What Are The Benefits Of Data And Analytics?

(1) Data analytics helps an organization make better decision.

(2) Increase the efficiency of the work.

(3) The analytics keeps you updated of your customer behavioural changes.

(4) Personalization of products and services.

(5) Improving quality of products and services.

What Are The Capabilities Of Data And Analytics?

Business Functions

(1) Finance: Powering startegic financial leadership.

(2) Supply Chain: Building agile and resilient businesses.

(3) Customer experiences: Crafting experience to delight customers.

(4) Human Resources: Redefining employee experiences.

(5) Marketing: Reimagining experience to enhance engagement.

Technology

(1 Networks: Building responsive networks to drive agility.

(2) Data center : Building automated , secure , and green solutions.

(3) Applications: Decoding operations to accelerate speed to market.

(4) Workspace: Creative inituitive and intelligent workspaces.

What Are The Limitation Of Data And Analytics?

(1) Lack of alignment within teams.

(2) Lack of commitment and patience.

(3) Low quality of data.

(4) Privacy concerns.

(5) Complexity And Bias.

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