In the modern enterprise landscape, business intelligence and reporting are often done by extracting data from data warehouse solutions. Data warehouse solutions involve creating ELT pipelines to consolidate data from disparate sources, defining fact and dimension tables, and connecting to a BI platform for real-time insights and analytics.
Increasing competition among businesses has meant that organizations require an agile data store that can assist them in strategic decision making. A data warehouse can be configured to convert indecipherable streams of raw data into accessible dashboards and reports that can guide managers and consultants.
In this blog, we will look at the way in which data warehouses can be used to drive BI and reporting in enterprises.
What is a Data Warehouse?
A data warehouse is a highly denormalized store of consolidated data from disparate structured and unstructured data sources. The purpose of maintaining a data warehouse is to keep a historical record of data, to track any changes, and to visualize it for easier understanding and deriving insights. Much of the data that comes into a data warehouse is imported from Online Analytical Processing (OLAP) and Online Transactional Processing (OLTP) databases. These transactional databases often contain information about clients, their purchases, and their account history. Data warehouses allow complex querying to be performed against multiple dimensions for easier access to the required information in a unified data store.
The design or architecture of a data warehouse and the features it comes with depends on an organization’s exact use case and needs. The most basic type of data warehouse is built with elements including metadata, raw data and summary data categorized into fact and dimension tables. The user may choose to arrange the tables in a star schema or snowflake schema according to the complexity of their use case. In some cases, the data warehouse may feature a staging table where clean and transformed data is temporarily stored for consolidation before it is passed on to the data warehouse.
More recently, data marts and data lakes have become more popular as they are easier to maintain and support simpler use-cases. Data marts are smaller compartmentalized data warehouses that act as central repositories for departments within an enterprise. In contrast, data lakes are large stores of structured and unstructured data from different areas in the business. Data lakes are used primarily to track data which is difficult to consolidate with other data sources in the enterprise.
Data Warehousing and Business Intelligence
Business intelligence software needs to be connected with a holistic repository of aggregated data to make relevant correlations between variables and to help managers make decisions.
In modern enterprises, data often comes from a variety of sources and is structured differently. In order to save time and effort analyzing each data source independently, data is passed on to the data warehouse after transforming it appropriately. This enables business intelligence software to conduct analysis and build dashboards for company-wide data. In addition, data warehouses allow users to improve data quality and fix any issues of relevance, reliability, or validity before it is used to guide decisions.
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CEOs, managers, consultants, and employees can each use the BI software according to their specific needs to design tailored dashboards which reflect data that is relevant to their function. Dashboards are shareable through local files which can be transferred on the business’ file sharing system or through a public URL that can be accessed remotely.
Connecting a Data Warehouse to a BI Platform for Visualization and Reporting
Once the data warehouse is configured with clean and transformed data from across the enterprise, it must be connected to the relevant BI platforms for visualization and to generate reports. This can be easily done through the OData service in the data warehouse solution which easily integrates with BI platforms for effective communication and transfer of data.
BI software usually features a highly intuitive GUI and drag-and-drop functionality to help users with even a basic understanding of the datasets to leverage information according to their needs. They make data wrangling and ad-hoc analysis easier by simplifying the querying process and using AI to suggest correlations between different tables in the data warehouse. These programs also have in-built capabilities that allow them to predict future trends based on current and historical data. Business users can customize their dashboards according to department data, charts, past vs. present etc.
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Furthermore, users can design and automate report generation through BI software to keep up to date with the latest changes in key metrics. These reports serve as a more detailed explanation of the summary depicted by dashboards and visualizations.
With widespread digitization and modernization in businesses, data warehousing is quickly becoming a norm rather than a choice. It allows businesses to streamline the querying process for users across the organization and frees up the time and effort of IT resources. Furthermore, it can easily integrate into BI platforms to make it simpler to understand unclean data from disparate sources.
Today, data warehouses are commonplace in sectors such as the financial industry, healthcare, the retail industry and in supply chain management. The existence of all historical and current data in a central repository makes organizations increasingly data-driven and strategic. Managers can leverage this data to anticipate shortages, invest in profitable ventures, and stock inventory according to the latest buying trends.
Business Intelligence and automated reporting are crucial mechanisms to identify patterns in existing data and predict changes in the future. A robust data warehouse at the backbone of BI software and competent managers to analyze the data can help any business venture take pragmatic steps to guarantee success in the future.
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