Business Intelligence Flashcards
Business Intelligence (BI)
A term used to describe a comprehensive, cohesive, and integrated set of tools and processes used to capture, collect, integrate, store and analyze data with the purpose of generating and presenting information to support business decision making.
It involves the capturing of internal-, external- and metadata (data about data).
It is a framework that allows a business to transform data into information and the information into knowledge.
What does BI provide a framework for?
- Collection and storing of operational data.
- Aggregation of operational data to obtain decision support data.
- Analyzing of decision support data to produce information.
- Prediction of future behaviors and outcomes (also produces information)
- Presentation of information/results to support business decisions.
- Making of business decisions, which generate data that are collected, stored, and so forth.
- Evaluation of business decision outcomes, which again generate data to be collected, stored and so forth.
Extraction, transformation and loading (ETL) tools
Used to COLLECT, FILTER, INTEGRATE and AGGREGATE data to be stored in a data store.
Data Store
- Optimized for decision support
- Represented by a data warehouse or data mart
- Stored in structures that are optimized for data analysis and query speed.
Query and reporting tools
- Employed to perform data selection and retrieval
2. Used by data analysts to create queries that access the data store to develop reports.
Data visualization tools
- Optimized for decision support
- Present data to the end user in a variety of meaningful and innovative ways, which include:
- Summary Reports
- Maps
- Bar and Pie Graphs
- Mixed Graphs
- Dashboards
Data monitoring and alerting
Allows real-time monitoring of business activities
Data analytics
- Performs data analysis and data mining on data in the data store.
- Can be either descriptive or predictive.
- Descriptive analysis discovers relationships, trends and patters among data elements in the data store.
- Predictive analysis describes the process of creating statistical models to predict future events and metrics.
Basic Architectural Components
- Data store
- Query and reporting tools
- Data visualization tools
- Data monitoring and alerting tools
- Data analytics
Dashboards
Web-based technologies to present key performance indicators (KPIs) or information of the business in a SINGLE INTEGRATED VIEW.
Portals
A unified, single point of entry for information delivery.
Use a web browser to gain access to web pages containing integrated data from multiple sources.
Data analysis and reporting tools
Advanced tools used to query multiple and diverse data sources to create integrated reports.
Data mining tools
These tool provide advanced statistical analysis to uncover problems and opportunities hidden within business data.
Data warehouses (DW)
The foundation of a BI infrastructure. Data are captured from the production system and placed in the DW on a near real-time basis.
BI provides company-wide integration of data and the capability to respond to business issues in a timely manner.
OLAP tools
Online analytical processing provides multidimensional data analysis.
Data visualization tools
Provide advanced visual analysis and techniques to enhance understanding and create additional insight of business data and its true meaning.
Master Data Management (MDM)
An arrangement of best managerial practices to manage data as a corporate asset.
Main objective of MDM
To provide a consistent and comprehensive definition of data within an organization to enable a uniform understanding of the organization’s data.
Types of BI Architecture Tools
- Dashboards and business activity monitoring
- Portals
- Data analysis and reporting tools
- Data mining tools
- Data warehouses (DW)
- OLAP tools
- Data visualization tools
Key Performance Indicators
KPI’s are scale-based measurements used to evaluate the company’s effectiveness in reaching its strategic and operational goals.
Benefits of BI (4)
- Integrating architecture
- Common user interface for data reporting and analysis
- Common data repository fosters single version of company data
- Improved organizational performance
4 Technology Trends for BI
- Data storage improvements
- BI as a service
- Big data analytics
- Personal analytics / Self service BI
Operational Data vs Decision Support Data
- Serves different purposes.
- Operational data storage is optimized to support transactions that represent daily operations.
- Operational data are stored in highly normalized (many tables, each with a minimum number of fields) relational databases to provide effective update performance.
- Decision support data give tactical and strategic business meaning to the operational data.
- Decision support data is optimized for query processing.
- Decision support data is stored in denormalized (few tables, each with many fields) dimensional data marts.
In what three ways do operational data and decisions support data differ from the data analyst’s point of view.
- Time span
- Operational data covers a short time period, whereas decision support data tend to cover a longer time frame. - Granularity (level of aggregation)
- Decision support data are presented at different levels of aggregation - from highly summarized to atomic. - Dimensionality
- Operational data focus on representing individual transactions rather than the effect of the transactions over time.
- In contrast, data analysts tend to include many data dimensions and are interested in how the data relate over those dimensions.
