The decision process in an organization can be modeled into three main phases:
This model helps decision-makers break down complex problems into logical steps and improve decision quality.
Example:
A retail chain notices declining sales (Intelligence) → Considers discounts, rebranding, or relocating stores (Design) → Chooses to offer combo discounts during weekends (Choice).
Decision Support Systems (DSS) are computer-based tools that help managers in making informed decisions using data, analytical models, and interactive software.
Key features include:
Example:
A bank uses DSS to decide loan approvals. It analyzes customer income, credit history, and repayment trends to suggest approval or rejection.
Group Decision Support Systems (GDSS) extend DSS to support collaborative decision-making among multiple stakeholders.
Features include:
Example:
A multinational company planning a new product launch uses GDSS to gather input from marketing, finance, and R&D teams across different countries.
Groupware refers to software applications designed to help teams collaborate efficiently, even from different locations.
Common Groupware Tools:
Groupware technologies support tasks like document sharing, messaging, scheduling, and video conferencing.
Example:
A project team uses Microsoft Teams to chat, share design files in SharePoint, and hold weekly meetings—all in one platform.
Business Expert Systems are knowledge-based systems that mimic human expert decision-making in a specific domain using rules and logic.
Artificial Intelligence (AI) involves simulating human intelligence in machines—such as reasoning, learning, and self-correction.
Example:
A business expert system can suggest tax-saving options based on income profile.
AI in banking can detect fraud by learning user transaction patterns.
OLTP systems manage day-to-day transactional data (insert, update, delete) like sales, orders, or payments in real time.
OLAP systems support complex queries and data analysis by summarizing large datasets across dimensions like time, location, and product.
Example:
OLTP: ATM withdrawal system records debit transactions instantly.
OLAP: A retail manager analyzes weekly sales across regions and product categories.
A Data Warehouse is a central repository where data from multiple sources is collected, cleaned, and stored for reporting and analysis purposes.
It helps decision-makers access historical data in a consistent format.
Example:
A university maintains a data warehouse to track student performance trends over years, combining results, attendance, and placement data.
Data Marts are smaller, subject-specific versions of data warehouses. They focus on a single department or function like sales, HR, or finance.
Example:
While a company’s data warehouse stores company-wide data, the Sales Data Mart contains only sales reports, targets, and customer trends.
The architecture of a data warehouse typically includes:
Example:
Salesforce CRM → ETL process → Warehouse (e.g., AWS Redshift) → Power BI Dashboard
Several tools are used to build, manage, and analyze data in a warehouse:
Example:
A retail business uses Apache Nifi to move data → stores it in Snowflake → visualizes sales trends in Tableau.
Multi-dimensional analysis involves examining data across multiple dimensions (like time, product, location) to uncover trends and relationships.
This is often done using OLAP (Online Analytical Processing) tools which allow users to:
Example:
A company analyzes sales data:
- Dimensions: Time, Region, Product
- Question: What were the total mobile phone sales in South India in Q1 2024?
Data Mining is the process of automatically discovering patterns, trends, and relationships in large datasets using statistical, mathematical, or AI techniques.
Knowledge Discovery is a broader process that includes data selection, preprocessing, data mining, and interpretation of results.
Example:
An e-commerce platform discovers that users who buy headphones often buy phone covers too.
This knowledge helps in bundling or cross-selling products.
Common techniques used in data mining include:
Example:
- Classification: Email → spam or not spam using machine learning
- Association Rule: "80% of users who bought shoes also bought socks"
Advanced databases include structured, semi-structured, and unstructured formats like:
Special techniques and tools are used to mine these non-traditional data types.
Example:
- Text Mining: Extracting frequent keywords from customer reviews
- Image Mining: Detecting objects in CCTV footage
- Web Mining: Analyzing browsing behavior on Amazon to improve recommendations
Knowledge Management Systems (KMS) are tools and processes that help organizations capture, store, share, and apply knowledge to enhance performance and decision-making.
KMS supports both explicit knowledge (documents, databases) and tacit knowledge (experience, insights).
Example:
A company’s internal portal with training manuals, FAQs, best practices, and expert advice is a Knowledge Management System.
The structure of a typical Knowledge Management System includes:
Example:
Google Drive (storage) + Google Meet (sharing) + Google Docs (collaboration) form a basic KMS structure in many companies.
Organizations use various techniques to manage knowledge effectively:
Example:
A software company documents its troubleshooting steps (codification) and also encourages team meetings where senior developers share experiences (personalization).
Appreciation:
Limitations:
Example:
A well-maintained KMS helps a new employee learn faster.
However, if the knowledge is outdated or scattered, it may confuse more than help.