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Chapter 1 – Business Intelligence, Analytics, & Decision Support

Business Environment
this is becoming more complex, creating opportunities and problems
Business Environment Factors
– Markets
– Consumer Demand
– Technology
– Societal
Organizational Responses
Be reactive, anticipative, adaptive, & proactive
– take action
Strategy Gap
space between current performance & desired performance of a company
– trying to close this
BI (Business Intelligence)
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
Components of BI System
– data warehouse
– business analytics
– business performance management (BPM)
– user interface
Data Warehouse
provides storage for data that’ll be used in analysis
– architecture, holds source data
Business Analytics
tools for manipulating, mining, & analyzing data in a data warehouse
– Descriptive
– Predictive
– Prescriptive
BPM (Business Performance Management)
monitors & analyzes real-time business performance
User Interface
what costumers interact with
– ex. a dashboard, tools, Excel
Descriptive Analytics
“What is happening/what happened?”
– biz reporting, dashboards, scorecards, data warehousing
– creates well-defined biz problems & opportunities
Predictive Analytics
“What will/why will it happen?”
– data mining, text mining, media mining forecasting
– accurate projections of future state/conditions
Prescriptive Analytics
“What should I do, why should I do it?”
– optimization, stimulation, decision modeling, expert systems
– best possible biz decisions & transactions
Governance
prioritization/management of data
– poor job = poor BI
Intelligence
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)
OLTP (Online Transactional Processing)
handle updates to operational databases, handle routine ongoing business
– data source (Walmart, Amazon transactions)
– CRM, ERP
– main goal = high efficiency
OLAP (Online Analytical Processing)
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
CRM (Customer Relationship Management)
transactional system, stores interactions/info about customers
– ex. Salesforce
ERP (Enterprise Resource Planning)
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
Implementing BI
– 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
BI Issues
– developing vs. acquiring a system
– quantifying costs & benefits
– security & privacy
– integration
Successful BI
– aligns with/helps execute company’s biz strategy
– improves biz processes, makes decision making more data-driven
Big Data
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

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