Business Intelligence for Data Science
Database is a structured collection of data that helps manage transactions efficiently. DBMS is kind of
like an interface between the users, applications and database.
Modern Business and Data Warehouse Systems
Business face various challenges that require effective data management to stay competitive like:
Uncertainty
Risk
Globalization
Predicting customer needs
Concurrency staying ahead of the competition
The motivation for the creation of a Data Warehouse Systems
Central component in BI systems is the Data Warehouse (DW). The role of DW systems in business
support:
Providing quality business information
1. Reducing business costs
2. Monitoring and analyzing operations
3. Increasing profits
Support in management and decision-making
1. Management Information Systems (MIS)
2. Decision Support Systems (DSS)
The main components from both concepts of MIS and DSS systems:
OLTP (Online Transaction Processing) – refer to specialized databases and systems that
handle daily transactions. It involves various technologies, databases, and methodologies and
are an essential part of many business’ information systems.
OLAP (Online Analytical Processing) – set of tools and techniques used to perform complex
analysis on data stored in Data Warehouse.
Data Warehouse – central database that collects, integrates, and stores data from various
sources within organization. For data transformation and structure purposes ETL processes
are used.
OLAP and Data Warehouse is closely related to DSS systems, while OLTP is more related to MIS
systems.
, The concept of Data Warehouse Systems
Creators of Data Warehouse system concept:
- Bill Inmon (1992): “A Data Warehouse is a subject oriented, integrated, time variant, and
nonvolatile (unchanging) collection of data in support of management’s decision-making
process.”
Subject oriented
Data are categorized and organized by business topics, NOT by functional units as in OLTP systems.
Example of possible Data Warehouse themes:
- Sales (data about products, customers)
- Marketing (data about the market, products,
customers, competition)
Integrated
Data about a single entity is entered and stored in one place.
A Data Warehouse represents a centralized
database. It contains data from various sources in
a standardized format.
Time variant
Contains historical and future data for analysis.
Data is organized through sets of ‘snapshot
states’. Each snapshot pertains to a specific
time interval.
Data Warehouse includes time as a dimension, and contains aggregated business data over time:
Historical: last week, month, year
Future: data representing predictions based on conducted analyses
Data Warehouse is periodically updated by adding new, acquired data or time-dependent aggregated
data. Example: When adding the latest sales data for the recent period, it’s necessary to recalculate
summarized sales data for products, customers, and sales.
Nonvolatile (unchanging)
Data is rarely deleted or modified. Most often, data is only added to the DW, causing it to grow.
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