Understanding Business Intelligence and Data Warehouse
7 September 2017, by Sree Tejaswi
Business Intelligence and Data Warehouse (BI/DW) are two separate but closely linked technologies that are crucial to the success of any large or mid-size business. The insights derived from these systems are vital for an organization as it helps in revenue enhancement, cost reduction, and adroit decision making.
Let’s understand what is Business Intelligence, Data Warehouse (DW), and how they are related.
Business Intelligence (BI)
Interpreting large volume of data often throws up challenges to the enterprises. However, BI can help companies with the easy interpretation of this big data, renders actionable information to end users and supports them in making more informed business decisions.
BI encompasses the set of strategies, technologies, applications, data, and processes used by an organization and supports in data analysis, demonstration, and propagation of business information. BI persuades enterprises to make effective business operational decisions such as product positioning and pricing and strategic decisions like goals, priorities, and directions at the broadest level. BI combines the external data derived from the market with the internal data obtained from the company sources (financial and operational data) and creates an “intelligence” that cannot be reaped from any singular set of data.
Data Warehouse (DW)
Data storage and management is an important managerial activity in any organization today and have become significant for rational decision making. A DW acts as a central repository system where an enterprise stores all its data (from one or more sources) in one place. DW helps industries in reporting and data analysis from the current and historical data stored, and hence it is considered as a core component of Business Intelligence.
Usually, data streams from online transaction processing database into a data warehouse on a daily, weekly, or monthly basis.
The process flow in data warehouse includes
- Extract data from source systems and upload to DW
- Data Cleansing and Transformation
- Archiving the data
- Steering the data to appropriate data sources
This whole process of extracting data and loading it to DW is generally called ETL (extraction, transformation, and loading).
Benefits of a Data Warehouse
- Enhance business intelligence with effective strategic, tactical, and operational insights
- DW contains a copy of analytical data that expedite decision making
- The data cleansing promises the data quality before it is used for reporting
- Integrate data from multiple data sources and make it accessible from one place.
- Easier and more efficient decision-support query writing
- Enable decision-makers and business users to have timely access to the data from different sources.
- Stores large volumes of historical data which helps in analyzing different time periods and trends that aid in making future predictions
- Restructures the data and deliver excellent query performance even for complex analytic queries without impacting the day-to-day transactions
Most business intelligence applications use data collected from a data warehouse, and the concepts of BI and DW together known as BI/DW. Data warehousing helps in achieving a successful BI program by facilitating several key aspects of reporting and data analytics.