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Lecture09-BusinessIntelligence-1.pptxInformation Technology for Managers Business Intelligence
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What is Business Intelligence?
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Business Intelligence
Various applications, practices, and technologies to support improved decision making
Employed by organizations to make and act on predictions about future conditions
Tools operate on data stored in a data warehouse or data mart
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Data Warehouse
Stores large amounts of historical data in a form that readily supports analysis and management decision making
Employed by organizations to hold data required to make key business decisions
Data comes from numerous operational systems and external data sources
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Example of Data Warehouse
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Uses of Data Warehousing
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ETL Process and Data Marts
Extract-transform-load (ETL) process
Used to pull data from disparate data sources to populate and maintain the data warehouse
Involves extract, transform, and load steps
Run as frequently as necessary
Data mart: Smaller version of a data warehouse
Designed from scratch as a complete, individual, miniature data warehouse
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Figure 9.1 - Extract, Transform, and Load Process
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What is Big Data?
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Big Data
Describes data collections that are large and complex
Cannot be dealt by traditional data management software, hardware, and analysis processes
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The 4Vs of Big Data
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Structured Data
Structured data: Format of the data is known in advance
Data fits into traditional databases
Rationale database model: Organizes structured data into collections of two dimensional tables called relations
ACID properties (atomicity, consistency, isolation, and durability)
Adapted by SQL databases
Guarantee database transactions are processed reliably and ensure the integrity of data
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Unstructured Data
Data that is not organized in any predefined manner
Does not fit into relational databases
Exists in large quantities and comes from various sources
Adds a depth to data analysis
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NoSQL Database, Hadoop, and In-Memory Database
Stores and retrieves data in a way that does not rigidly enforce the ACID conditions
NoSQL database
Open-source software framework designed for processing large volumes of data
Divides the work into a set of independent tasks that are executed in parallel on a large number of servers
Hadoop
Stores an entire database in random access memory (RAM)
In-memory database
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Figure 9.3 - Hadoop as a Staging Area for Loading Data into a Data Warehouse or Data Mart
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Business Intelligence (BI) Tools
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Business Intelligence Tools
Spreadsheets
Perform operations on the data based on formulas created by the end user
Create useful reports and graphs based on that data
Reporting and querying
Present data in an easy to understand fashion
Enable end users to make their own data requests and format the results without the need for additional help from the IT organization
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Business Intelligence Tools (continued 1)
Online analytical processing (OLAP)
Analyzes multidimensional data from different perspectives
Enables users to identify issues and opportunities and perform trend analysis
Data cubes
Contain numeric facts called measures
Categorized by dimensions such as time and geography
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Figure 9.5 - Three-Dimensional Data Cube
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Business Intelligence Tools (continued 2)
Drill-down analysis: Enables decision makers to gain insight into the details of business data to understand why something happened
Interactive examination of high-level summary data in increasing detail to understand certain elements
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Business Intelligence Tools (continued 3)
Data mining: Used to explore large amounts of data for hidden patterns
Predicts future trends and behaviors for use in decision making
Commonly used techniques
Association analysis
Neural computing
Case-based reasoning
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Business Intelligence Tools (continued 4)
Steps involved in data mining process
Selection
Preprocessing
Transformation
Actual data mining process
Evaluation of results
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Figure 9.6 - Data Mining Leads to Informed Action Based on New Knowledge
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Dashboards
Present a set of key performance indicators about the state of a process at a specific point in time
Key performance indicators (KPIs)
Track progress in executing chosen strategies
Consist of a direction, measure, target, and time frame
Options for displaying results
Maps, gauges, bar charts, trend lines, scatter diagrams, and other representations
Provide users with information at every level to make informed decisions
Guide to Microsoft Virtual PC 2005 and Virtual Server 2007
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Figure 9.7 - Sample Summary Dashboard
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Source: www.microstrategy.com/us/analytics/technology.
Data Governance
Helps ensure that a firm has reliable and actionable data to make informed business decisions
Involves management of availability, usability, integrity, and security of data in an organization
Requires establishing a data governance body
Ensures meeting regulatory and compliance requirements
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Table 9.4 - Data Governance Components
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Challenges of Big Data
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Big Data: Challenges
Choice of data, place, and the method of storage
Privacy concerns associated with data mining
Corporations harvesting and mining huge amounts of personal data that can be shared with other organizations
Security concerns
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Summary
Business intelligence
Extraction, transformation, integration, analysis, and presentation of data
Supports improved decision making
Extract-transform-load (ETL) process is employed to gather data from multiple sources
Big data describes data collections that are large and complex that traditional data management systems are incapable of handling
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Summary (continued)
Dashboard presents a set of key performance indicators about the state of a process at a specific point in time
Data governance involves the management of the availability, usability, integrity, and security of the data used in an organization
Challenges of big data include choice of data, place and the method of storage, privacy and security concerns
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