Chapter 1 – Business Intelligence, Analytics, & Decision Support – Flashcards
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Business Environment
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this is becoming more complex, creating opportunities and problems
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Business Environment Factors
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- Markets - Consumer Demand - Technology - Societal
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Organizational Responses
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Be reactive, anticipative, adaptive, & proactive - take action
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Strategy Gap
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space between current performance & desired performance of a company - trying to close this
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BI (Business Intelligence)
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umbrella term, combines tools, databases, analytics, applications, etc - objective: give easy access/availability to data to help biz managers conduct analysis: actionable intelligence - transforms data to info/knowledge, decisions, & action - term coined by Gartner Group, been doing since 1970s
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Components of BI System
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- data warehouse - business analytics - business performance management (BPM) - user interface
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Data Warehouse
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provides storage for data that'll be used in analysis - architecture, holds source data
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Business Analytics
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tools for manipulating, mining, & analyzing data in a data warehouse - Descriptive - Predictive - Prescriptive
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BPM (Business Performance Management)
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monitors & analyzes real-time business performance
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User Interface
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what costumers interact with - ex. a dashboard, tools, Excel
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Descriptive Analytics
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"What is happening/what happened?" - biz reporting, dashboards, scorecards, data warehousing - creates well-defined biz problems & opportunities
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Predictive Analytics
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"What will/why will it happen?" - data mining, text mining, media mining forecasting - accurate projections of future state/conditions
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Prescriptive Analytics
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"What should I do, why should I do it?" - optimization, stimulation, decision modeling, expert systems - best possible biz decisions & transactions
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Governance
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prioritization/management of data - poor job = poor BI
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Intelligence
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how modern companies ethically & legally organize to get as much info as possible from customers, biz environment, stakeholders, & other sources of valuable info - use data ethically (not espionage) - problem: too much data w/ very little value (info more valuable than data)
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OLTP (Online Transactional Processing)
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handle updates to operational databases, handle routine ongoing business - data source (Walmart, Amazon transactions) - CRM, ERP - main goal = high efficiency
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OLAP (Online Analytical Processing)
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extract information from data stored by transactional systems, often built on top of a data warehouse where data isn't transactional - ex. regular sales reports - main goal = effectiveness, correct info in timely manner
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CRM (Customer Relationship Management)
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transactional system, stores interactions/info about customers - ex. Salesforce
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ERP (Enterprise Resource Planning)
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all info on one platform, uses modules that connect to one main resource - challenge: move the data over to this system/put it in new format - ex. SAP
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Implementing BI
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- lengthy, risky, expensive - success measured by widespread usage for better decision making, provide what's needed to who needs it - must benefit enterprise as a whole
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BI Issues
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- developing vs. acquiring a system - quantifying costs & benefits - security & privacy - integration
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Successful BI
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- aligns with/helps execute company's biz strategy - improves biz processes, makes decision making more data-driven
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Big Data
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data that can't be stored in a single storage unit, arriving in many different forms (Google, Facebook data). Results in BIA & data mining/understanding - Volume - Variety: all types of data - Velocity: speed at which we're creating data is very fast
