Modern businesses now rely on accurate insights and data-driven decision-making for their strategic planning and growth. The growing importance of data warehousing and business intelligence and data analytics aptly shows how businesses use robust data management tools and analytics platforms to support their decision-making.
Moreover, BI relies on technologies like data warehousing to provide timely, reliable, and accurate intelligence. To comprehend how a BI architecture delivers value, it is first pertinent to understand the complementary relationship between a data warehouse and business intelligence.
What is BI?
Business Intelligence (BI) refers to the processes and technologies which help derive meaningful insights and actionable intelligence from data. Business Intelligence tools access an organization’s data to present analytics and insights in the form of reports, dashboards, graphs, summaries, and charts.
Additionally, such tools empower a vast range of decision-makers within an organization. For example, marketers track campaign metrics or customer behavior in real-time dashboards. Finance teams collate data from all departments to see what factors affect profit and loss. Sales personnel use business intelligence dashboards to track KPIs, whereas operation departments utilize BI to optimize business operations.
A fundamental BI architecture consists of the following components:
- Disparate source systems or databases, which collect the data in its original, raw format.
- An integration layer in the DWH extracts data from the databases, cleanses it and loads it into a DWH.
- A data warehouse that prepares and stores data for analysis.
- Business Intelligence tools to draw and present data-based insights in the form of visualizations, reports, dashboards, summaries, and charts.
What is The Role of a Data Warehouse (DWH) in Business Intelligence?
Behind every successful BI system, there’s a powerful DWH. Now, what is a data warehouse? A Data warehouse (DWH) is a central platform for consolidating and storing data from different sources and preparing this data for downstream business intelligence and analytics. Think of it as a single repository that organizes and stores all the data for BI analytics.
A data analytics data warehouse stores historical and current data in a structured format optimized for complex querying. It is then connected to Business Intelligence tools to generate reports, including forecasts, trends, and other visualizations that fuel actionable insights.
Components of the data warehouse in business analytics consist of ETL (extract, transform, and load) tools, a DWH database, DWH access tools, and reporting layers. These tools exist to streamline the data science process and to reduce or eliminate the need of writing code to manipulate data pipelines.
The ETL tools help extract data from source systems, convert it into the desired format, and load the transformed data into the DWH. The database component stocks and manages structured data for reporting. The access tools allow businesss intelligence and data analytics users to interact with the data lying in the DWH. The reporting layer provides a BI interface for analyzing and visualizing the data stored in the data warehouse.
What Is The Difference Between Data Warehousing and Business Intelligence?
There are specific key differences between data warehousing and business intelligence.. However, before we dive into the differences, it is essential to note that they operate in the same space and are equally important for an overarching business intelligence strategy.
Below are some of the inherent differences between the two.
The primary purpose of BI is to analyze data and present actionable insights to decision-makers. Here, a data warehouse is a centralized repository for gathering, processing, and storing data from various disparate sources.
The goal of BI is to facilitate business users in making intelligent and data-backed business decisions through forecasting and predictive analytics. On the other hand, the purpose of a data warehouse is to store structured data in a central location so that BI users can have access to a holistic view of the organization’s data.
BI output consists of dashboards, reports, data visuals, charts, and graphs containing insights and trends. Such results allow business users to make sense out of complex data. Output for a DWH consists of data records held in fact and dimension tables of data models.
BI users are usually C-level executives, managers, or data analysts looking to carry out timely data analysis for better decision-making. Conversely, DWHs are generally handled and maintained by data architects and engineers who provide business users with analysis-ready data.
Some commonly used BI tools are SAP, Power BI, Tableau, and Qlik. On the other hand, popular data warehouse providers include Amazon Redshift, Google BigQuery, and Azure Synapse.
How Is Data Analyzed Using a Data Warehouse?
DWHs use Online Analytical Processing (OLAP) to process large swaths of data. It consolidates all the data on a centralized platform. It is a data processing approach employed by DWHs for streamlining complex queries. In simpler terms, it is a computing method that helps users extract and query the required data for analysis.
For example, if someone asks about the relationship between two different datasets in a DWH, OLAP processing would be used to move through the stored data to find, identify, and summarize the desired information quickly. Using OLAP, a data warehouse provides BI with the data it needs to analyze.
Data Warehousing and Business Intelligence: Solutions for Enterprises
Business intelligence architecture without a data warehouse is like a car without an engine. One cannot simply drive accurate BI without a robust data warehouse powering it up. Therefore, despite their differences, data warehouse and business intelligence complement each other to provide a reliable BI architecture for businesses.
Following the best practices in business intelligence and data warehousing, organizations often integrate enterprise data warehouses in business analytics architecture to deploy business intelligence and data warehousing (BIDW). BIDW refers to the entire BI architecture where accurate and reliable data is seamlessly drawn from data warehouses to generate actionable insights for quick and intelligent decision-making.
If you need to build an agile data warehouse for your organization, try out our automated, meta-data-driven tool Astera DW Builder. It is an end-to-end data warehouse automation solution that allows you to quickly design, develop, and deploy analytics-ready data warehouses. Moreover, here you can watch how it is used to deploy both on-prem and cloud data warehouses in just a few days.