Business Intelligence - Concepts
Business intelligence is a concept that brings together ideas from different fields. Whether to use a certain idea in business intelligence depends on the specific needs and available resources.
Some of the commonly used concepts in business intelligence are −
Data Warehouse
A data warehouse is a type of data repository where data from various sources is brought together, organized, and versioned. This organization makes it easier to use for reporting and analysis.
Data Mart
A data mart can either be a building block within a data warehouse or a smaller section taken from it. For example, while a data warehouse holds all the company's data, a data mart might focus on specific areas like sales or finance. This setup allows different departments to quickly access the information they need making data analysis easier and more efficient.
Data Lake
A data lake is a large storage system designed to hold a wide range of data types often in their raw original form. It allows authorized users to access and analyze this data for different purposes. Unlike traditional databases a data lake can store both structured data like tables and unstructured data that are text or images, making it useful for managing different information. Organizations can collect and use data from multiple sources without having to first process it because of this flexibility.
Data Vault
A data vault is a way to design and manage data warehouses that makes it easy to store and access data from different sources. It helps create a flexible and scalable system so you can handle changes and updates over time.
ODS
The ODS stands for Operational Data Store. The term ODS refers to an Operational Data Store, a system used for operational reporting and managing day-to-day business activities. Like a data warehouse it holds integrated subject-focused data but unlike a data warehouse and ODS only stores current data without keeping historical records. This makes it perfect for real-time reporting offering quick access to the latest information. For instance, a company might use an ODS to track daily sales figures or monitor current inventory levels.
ETL
ETL stands for extract, transform, and load. ETL is the process of extracting data from sources like files, databases, or websites, transforming it based on business and technical rules, and then loading it into a target data store. Though often linked with data warehousing and business intelligence, ETL is also frequently used for data migration and integration. This process ensures data is well-prepared and organized for use in various systems or applications.
Data Integration
Transactional systems store data specific to each application, like separate databases for in-store purchases, online sales, and employee information. To answer complex questions that require data from multiple sources, this data needs to be integrated in a data warehouse. Integration involves combining and aligning the data, resolving inconsistencies and making it compatible for accurate analysis and a unified view of the business.
Machine Learning
Machine learning is a subset of artificial intelligence that enables systems to learn from data, identify patterns, and make predictions on their own. These models, built using existing data, can adjust and improve their predictions as new data is introduced.
Many Business Intelligence (BI) tools now use ML to help users discover insights and analyze data more easily. You dont need to be a data scientist to use these features, as they often come with simple drag-and-drop or point-and-click options. ML in BI helps automate insight generation and makes advanced data analysis more accessible.
Data Mining
Data mining means looking through large amounts of data without having a specific question. The idea is to find interesting patterns, trends, and connections. Analysts then show what they've found to business leaders, who figure out how to use these insights. This is useful because it can bring to light important details that might not come up with straightforward questions.
Sometimes, the process doesnt reveal new insights, but when it does, it can help with things like improving marketing strategies for reducing customer churn. If the results are too costly or dont offer enough return, the idea might be set aside for further analysis. Data mining, along with reporting and analytics, helps businesses gain a better understanding and make informed decisions.