Foundations of Business Intelligence (2)

Data Redundancy
Data Inconsistency
Data Dependence
Limited Data Sharing
Security Risks
Maintenance Overhead

Managing data resources in a traditional file environment can be challenging and can lead to several problems, what are those?

Data Redundancy

In a traditional file environment, data is often stored in multiple files or locations, leading to this.

Data Inconsistency

This can arise when multiple users access the same data simultaneously, or when data is updated in one file but not in another. This can lead to inaccurate or conflicting information, making it difficult to make informed decisions.

Data Dependence

In a traditional file environment, applications are often developed using specific file structures and formats, making them dependent on the data and file structures.

Data Redundancy

This can result in inconsistencies, errors, and wasted storage space.

Limited Data Sharing

Traditional file environments are often limited in their ability to share data between different applications or users.

Limited Data Sharing

This can lead to siloed data, making it difficult to gain a holistic view of the organization's operations.

Security Risks

Traditional file environments often lack security features, making them vulnerable to unauthorized access or data breaches.

Security Risks

Additionally, the lack of backup and recovery mechanisms can put data at risk of loss or corruption.

Maintenance Overhead

Maintaining data in a traditional file environment can be time-consuming and resource-intensive, requiring manual updates, backups, and file maintenance.

Database management system

are software applications design to manage and manipulate data.

Data Storage and Retrieval

-        DBMS allows users to store, retrieve, and modify data efficiently.

DBMS Organization

-        DBMS organizes data into tables, records, and fields, making it easier to manage and query.

Data Security

-        DBMS offers security features such as access control, encryption, and backup and recovery mechanisms to protect data from unauthorized access or loss.

Data Integrity

-        DBMS enforces this by ensuring that data is accurate, complete, and consistent.

Data concurrency

-        DBMS allows multiple users to access and manipulate data simultaneously without conflicts or data inconsistencies.

Data scalability

-        DBMS can handle large volumes of data and can scale up or down to meet changing business needs.

Data analysis

-        DBMS supports this and reporting by providing tools such as queries, reports, and dashboards.

A relational DBMS

-        is a type of DBMS that uses a relational model to organize and manipulate data.

A relational DBMS

data is stored in tables that are related to each other through common fields.

A relational DBMS

-        This approach offers several advantages over other DBMS models

Flexibility
Standardization
Efficiency
Simplification
Ad hoc querying

A Relational DBMS Advantages

Flexibility

o   A relational DBMS can accommodate changes in data requirements or structures without the need to modify application code.

Flexibility

o   a key capability of modern DBMS, enabling organizations to stay agile and responsive in the face of changing data requirements and structures. Flexible features in DBMS include schema evolution, dynamic indexing, data virtualization, and cloud deployment, among others.

Schema evolution
Dynamic Indexing
Data Virtualization
Cloud Deployment

Examples of flexible features in DBMS

Flexibility - Schema Evolution

can evolve its schema over time to accommodate new data types or structures without requiring significant downtime or modifications.

Dynamic Indexing

A flexible DBMS can create and modify indexes dynamically, based on changing access patterns or query requirements.

Data Virtualization

A flexible DBMS can virtualize data from multiple sources, providing a unified view of the data without requiring physical data replication or movement.

Cloud Deployment

A flexible DBMS can be deployed in the cloud, providing elastic scalability and pay-as-you-go pricing models that can adapt to changing business needs.

Standardization

o   The relational model is a widely accepted standard for data management, making it easier to integrate data from different sources.

Standardization

is an important aspect of modern DBMS, providing several benefits such as improved data quality, data integration, data storage efficiency, faster application development, and easier maintenance and support. Standardization is often achieved through the use of common data models and standards, ensuring consistency and interoperability across an organization or industry.

Efficiency

The relational model is highly optimized for data access and retrieval, making it faster and more efficient than other models.

Efficiency

is an important consideration in DBMS, affecting system performance, scalability, and resource utilization.

Indexing
Query Optimization
Data Compression
Parallel Processing

examples of features that can improve DBMS efficiency

Indexing

can improve query performance and reduce the need for full-table scans.

Query Optimization

techniques such as cost-based optimization can help to generate more efficient query execution plans.

Data compression

techniques can help to reduce storage requirements and improve data retrieval performance.

Parallel Processing

techniques can help to improve query processing and reduce response times by distributing processing across multiple CPUs or nodes.

Simplification

o   The relational model simplifies data management by organizing data into tables and fields, making it easier to understand and use.

Simplification

o   organizations can improve efficiency, reduce errors, and make better use of data to inform business decisions.

Ad hoc querying

The relational model allows users to perform ad hoc queries to retrieve data from multiple tables, enabling complex data analysis.

Ad hoc querying

In a DBMS, it is usually achieved through a graphical user interface (GUI) or query language, such as SQL. Users can select fields and filter data based on specific criteria to generate custom reports or views of data.

Relational Database Management System (RDBMS)
Structured Query Language (SQL)
Business Intelligence (BI) Tools
Data Warehouses
Extract, Transform, Load (ETL) Tools
NoSQL Databases

Tools and Technologies

Relational Database Management Systems (RDBMS):

These are software tools that allow businesses to store, organize and manage large amounts of structured data in a database. Examples include Oracle, MySQL, SQL Server and PostgreSQL.

Structured Query Language (SQL):

This is a programming language used to manage and manipulate data in a relational database.

Structured Query Language (SQL)

-        allows businesses to retrieve, insert, update, and delete data from a database, and to generate reports and analysis based on that data.

Business Intelligence Tools

-        These are software applications that enable businesses to analyze data from various sources, including databases, to gain insights and inform decision-making. Examples include Tableau, Power BI and QlikView.

Data Warehouses

These are large databases that store historical data from various sources, such as transactional databases, for the purpose of analysis and reporting.

Data Warehouses

often use specialized software such as Snowflake, Amazon Redshift, and Microsoft Azure Synapse Analytics.

Extract, Transform, Load (ETL) Tools

These are software applications used to extract data from various sources, transform it into a format that can be used for analysis, and load it into a data warehouse or other database.

NoSQL Databases

These are non-relational databases that can store unstructured and semi-structured data, such as documents, images, and videos. Examples include MongoDB and Cassandra

Business Intelligence

-        is a process of analyzing data to make informed business decisions.

Business Intelligence

-        The foundation of BI lies in databases and information management.

Data
Analytics
Technology
Processes
People

Key Components of BI

Data

BI relies on this from various sources, including databases, data warehouses, and data lakes.

Data

This is needs to be accurate, complete, and relevant to the business questions being asked.

Analytics

Once the data is collected, BI uses to extract insights from it.

Data Mining

is the process of discovering patterns and insights from large datasets using statistical and machine learning techniques.

Data Mining

It involves extracting valuable information from data that is stored in databases, data warehouses, and other repositories.

Technology

BI relies on this such as software tools, data visualization platforms, and databases. This must be reliable, scalable, and secure.

Processes

BI requires well-defined processes for data collection, analysis, and reporting.

Processes

This includes data governance policies, data quality management, and data integration processes.

People

BI requires these with the right skills and knowledge to use the technology and analyze the data.

People

This includes data scientists, business analysts, and other stakeholders who can interpret and apply the insights gained from BI.

Database Management System

 is used to create, maintain, and manage databases.

Databases

can be used to store a wide range of data, including customer information, sales data, inventory levels, and financial information.

Business Intelligence Tools

are used to extract insights from data stored in databases.

Business Intelligence Tools

can help organizations make data-driven decisions by providing insights into:

·        business performance,

·       customer behavior, and

·       market trends.