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Introduction to BI (BI & Analytical Processing vs Online transaction…
Introduction to BI
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Descriptive Analytics
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Refers to knowing what is happening in the org. and understanding underlying trends and causes of such occurences
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Enablers are reports & dashboards, scorecards, DW
Eg Customer analytics – past buying patterns, customer profile
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Components of BI system
a data warehouse (DW), with its source data
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Originally, the data warehouse included only historical data that was organized and summarized, so end users could easily view or manipulate it
Today, some data warehouses include access to current data as well, so they can provide real‐time decision support (for details
see Chapter 2).
business analytics, a collection of tools of manipulating, mining, and analyzing the data in the DW
are the tools that help users transform data into knowledge (e.g., queries, data/text mining tools, etc.)
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Prescriptive Analytics
Goal is to know what is going on so as to provide a decision or a recommendation for a specific action to achieve the best performance possible
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What is BI?
is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies.
a content‐free expression, so it means different things to different people
major objective is to enable interactive access to data, enable manipulation of these data, provide business managers with the ability to conduct appropriate analysis
Objectives of BI System
To facilitate closing the gap between the current performance of an organization and its desired performance, as expressed in its mission, objectives, and goals, and the strategy to achieve them
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To help transform data, to information (and knowledge), to decisions and finally to action
Analytics
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Business Analytics is a broad category of applications and techniques for gathering, storing, analyzing and providing access to data to help enterprise users make better business and strategic decisions
3 levels: Descriptive, Predictive & Prescriptive
Business Analytics
Descriptive Qns: What happened?, What is happening?
Enablers: Biz reporting, dashboards, scorecards, DW
Outcomes: Well defined biz problems and opportunities
Predictive Qns: What will happen? Why will it happen? Enablers: Data, Text and Web/media mining & Forecasting Outcomes: Accurate projections of the future & conditions
Prescriptive Qns: What should I do? Why should I do it? Enablers: Optimization, Simulation, Decision Modelling & Expert Systems Outcomes: Best possible biz decisions & transactions
Predictive Analytics
Aims to determine what is likely to happen in the future based on statistical techniques and more recently developed techniques used in data/text, web mining & forecasting
Customer analytics ‐ predict if a customer is likely to churn, or what a customer is likely to buy next and how much, or what promotion a customer would respond to, etc