Data Mining for Business Decisions Part-1

Data Mining for Business Decisions

Data Mining

Data mining refers to the process of searching and analyzing a large batch of raw data to discover patterns and extract useful information and transform it into an understandable structure for further use.
With the rise of technologies, the volume of data available for mining is growing.

Business Decisions

Business Decision explains making choices that influence the direction, growth and success of a company and determine short-term and long-term organizational activities.

Why is data mining required for business decisions?

In today’s data-driven world, data mining is required for business decisions because it helps organizations extract valuable insights from large datasets and enable them to make more informed, accurate and strategic choices.

Strategic information         

Strategic information is the kind of data and insights that are important for making long-term decisions which shape the overall direction of a business.

It supports strategic planning and helps businesses align their goals, resources and actions with their mission and vision.

Need for strategic information

Strategic information drives effective decision-making, competitive positioning  and planning.

Strategic information is important and required for making informed decisions that guide an organization towards achieving its long-term goals.

 Why is strategic information needed ?

·        Market Positioning – To understand customer needs, market trends and competitor activities and it allows organizations to position themselves effectively.

·        Informed Decision-Making –  Insights and data to make informed choices that align with the organization’s long-term goal are provided by strategic information.

·        Competitive Advantage- Strategic information helps organizations develop unique strategies which differentiate them from competitors.

·        Long –Term Planning – Strategic information is required by organization to develop effective long-term plans.

·        Resource Allocation – Strategic information is required for allocating resources efficiently.

·        Risk management- Strategic information helps in identifying risks and uncertainties.

·        Performance Monitoring- To track performance against objectives and goals, strategic information is required.

·        Innovation and Growth- Strategic information is needed to identify opportunities for growth and innovation.

·        Stakeholder Communication – Strategic information is required for effective communication with stakeholders.

Operational and Informational Data Stores

Data Stores refer to repositories used to store, manage, and distribute data sets.

Operational Data Store

An operational data store is a central database that aggregates data from multiple systems and used for operational reporting and as a source of data for the enterprise data warehouse (EDW).

Informational  Data Store

An informational data store refers to data storage system designed to support business intelligence, analytics and reporting by storing and managing large volumes of data over extended periods.

Difference between operational and informational data stores

Data Warehouse

A data warehouse is an enterprise system and a centralized repository used for the analysis and reporting of structured and semi-structured data from multiple sources such as customer relationship management, marketing automation etc.

      Characteristics

·        Historical Data Storage

·        Subject-Oriented

·        Integrated Data

·        Non-Volatile

·        Data Cleansing and Transformation

·        Optimized for Querying and Reporting

·        Scalability

·        Metadata Management

·        High Availability

·        Data Modeling

       Role and Structure

       Role

·        Optimized  for  Complex Queries

·        Data Analysis

·        Decision Support

·        Performance Optimization

·        Data integration

·        Data Consistency and Quality

·        Historical Data Storage

·       Centralized Data Repository

 Structure

A data warehouse has a multi-layered structure which supports efficient data processing  and analysis.

Key Components

  • Data Sources
  • ETL Process
  • Staging Area
  • Data Marts
  • Metadata
  • Data Governance and Security
  • Access Layer


·  




Introduction to Business Intelligence

Business Intelligence or BI is the technology-driven process of analyzing data and presenting actionable information to help managers, executives and other corporate end users make informed business decisions.    

It  combines business analytics, data mining, data tools and infrastructure, data visualization, and best practices to help organizations make more data-driven decisions.

Key Components

  • Data Sources
  • Data Warehousing
  • Data Analysis Tools
  • Decision-Making Support
  • Reporting and Visualization

Benefits

  • Competitive Advantage
  • Increased Operational Efficiency
  • Improved Decision-Making
  •  Enhanced Customer Understanding

Some BI Tools and Platforms

  • Tableau
  •   Microsoft Power BI
  • QlikView/Qlik Sense
  • IBM Cognos Analytics
  • SAP BusinessObjects
  • Sisense
  •  Looker
  • Zoho Analytics
  • Oracle Analytics Cloud
  • Domo

Introduction to OLAP and its Operations

Online analytical processing  or OLAP refers to the kind of  software technology which can be used  to analyze business data from different points of view.

Organizations collect and store data from multiple data sources, such as websites, applications etc. OLAP combines and groups this data into categories to provide actionable insights for strategic planning.

It helps organizations process and benefit from a growing amount of digital information.

OLAP solves complex analytical programs.

It processes large amounts of data from a data mart, data warehouse or other data storage unit.

Benefits

  • Non-technical user support
  •   Faster decision making
  • Integrated data view
  • Multidimensional Analysis

Types

  • Multidimensional OLAP
  •  Relational OLAP
  • Hybrid OLAP 

OLAP Operations

  • Roll-Up- Roll-up involves aggregating data by climbing up the hierarchy of dimensions.
  • Drill-Down- It is the opposite of roll-up and involves breaking down data into more detailed levels.
  • Dice- Dicing is similar to slicing but it involves selecting two or more dimensions to create a smaller subcube.
  •  Slice- Slicing involves selecting a single dimension from the OLAP cube and fixing it at a particular value, creating a subcube.
  • Pivot- It involves rotating the data cube to view the data from different perspectives.
  • Drill-Through- It allows users to access detailed data from the underlying transactional database.
  • Drill-Across- It involves accessing related data from different fact tables within the same schema.

Data Mart

A data mart refers to a data storage system that contains information specific to an organization's business unit. It contains a small and selected part of the data that the company stores in a larger storage system. 

To analyze department-specific information more efficiently, data mart is used by companies.

It is a subset of a data warehouse which is focused on a specific business line or team.

Features

  • Smaller in Scope
  •  Faster  Access
  • Subject-Oriented
  • Simplified Data Structure

Types

  •  Dependent Data Mart
  • Independent Data mart
  •  Hybrid Data Mart

Building a Data Warehouse

To build a data warehouse is a complex process which involves designing and implementing a system to consolidate, store and manage large volumes of data from various sources.

Steps to build data warehouse

  • Define Business Requirements
  • Data Modeling and Design
  • Choosing the Right Technology
  •  ETL Process
  • Data Integration and Quality Management
  •  Build and Populate the Data Warehouse
  • Create Reports, Dashboards and Analytics
  • Performance Tuning and Optimization
  • Security and Data Governance
  • Maintenance and Support
  • Evaluate and Iterate

Introduction to Dimensional Modeling and ETL Process

Dimensional Modeling

It is a design technique used in BI systems and data warehouses to structure data in a way that is optimized for querying and reporting.

It is focused on ease of use and performance in analytical tasks.

Concepts in Dimensional Modeling

  • Fact Table- It contains quantitative data that users want to analyze.
  • Dimension Table- It stores descriptive information about the business entities related to the facts such as time, products etc.
  • Star Schema- It is the simplest type of dimensional model where a central fact table is directly linked to dimension tables. It resembles  star with the fact table at the center and dimensions as  points radiating out.
  • Factless Fact Table- It captures events or conditions that don’t have associated numerical measures but are important to track.
  • Snowflake Schema- It is a more normalized version of the star schema. In this, dimension tables are further broken down into related tables, resulting in a “snowflake”  structure.
  • Grain of a Fact Table- It describes the level of detail represented by each record.

·      Benefits

·        Flexibility

·        Supports Complex Analysis

·        Efficient Query Performance

·        User-Friendly

ETL Process

ETL or extract, transform, load refers to a data integration process that combines, cleans and organizes data from multiple sources into a single, consistent data set for storage in a data warehouse, data lake or other target system.

Extract, transform, and load or ETL improves business intelligence and analytics by making the process more reliable, efficient, detailed, and accurate.

It involves extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse.

Key Stages of the ETL Process

·        Extract – This phase involves collecting data from various source systems which may include databases, flat files, APIs and cloud services.

·       Transform- This phase converts the raw data into a clean and usable format that aligns with the schema of the data warehouse.

Common Transformations - Data Cleaning, Data Integration, Data                          Aggregation, Data Normalization/ Denormalization, Data Formatting

·       Load- This phase involves transferring the transformed data into the data warehouse.

Types of Loading:

1.     Initial Load- Loading all historical data into the warehouse for the first time.

2.     Incremental Load- Periodically loading new or updated data, typically on a daily, weekly, or monthly basis.

3.     Full Refresh- Replacing existing data with new data(less common).



·       Benefits

  •  Improved Data Quality
  •  BI Support
  • Scalability
  •  Data Consolidation
Note : Please wait for its next part.


·                                                    Thank You

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Comments

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