Although they function in the same area, business intelligence and data warehouses are fundamentally different concepts. Data warehouses and business intelligence both include data storage. However, data collection, technique, and analysis are the main focuses of business intelligence. On the other hand, a data warehouse primarily organizes and stores such data to support BI activities. The upkeep and deployment of a data warehouse are so essential to business intelligence (BI) that they are frequently referred to as BIDW.
The relationship between a data warehouse and business intelligence solutions (BIDW)
Modern companies now rely on comprehensive insights and data-driven decision-making for their strategic planning and expansion. Data warehousing, business intelligence, and data analytics are becoming increasingly important, demonstrating how organizations employ reliable data management tools and analytics platforms to support decision-making. Additionally, BI depends on tools like data warehousing to deliver accurate, timely, and reliable intelligence. Understanding the close correlation between a DWH and business intelligence is important to appreciate how a BI architecture fully creates value.
What is business intelligence (BI)?
Business intelligence (BI) describes the procedures and tools that assist in getting valuable information and intelligence that can be used from data. A company’s data is accessed by BI tools, which then display analytics and insights as reports, dashboards, graphs, summaries, and charts.
These tools also provide a broad range of decision-makers in an organization with more power. For instance, real-time dashboards are used by marketers to monitor campaign KPIs or consumer behavior. Finance teams gather data from all departments to determine what influences profit and loss. Operations departments use BI to optimize daily business operations, while sales professionals use BI dashboards to track KPIs.
Business intelligence architecture and its components
Data warehouses (DWHs), business analytics, business performance management, and user interfaces comprise the bulk of business intelligence.
Data from both internal and external sources are stored in DWHs. Various operational systems are among the internal sources.
Through queries and rules, business analytics generates a report as and when needed. Another crucial component of business analytics is data mining.
Data and company objectives are linked through business performance management to enable effective tracking. The executive decision-making body is then informed of this company’s performance via dashboards and Sharepoint.
Even if we highlight the importance of each step of this procedure, it’s equally critical to highlight some of its most significant advantages. When it comes to gathering, arranging, and managing business data effectively so that it can be transformed into insights for better decision-making, a solid BI architecture serves as a guide. Let’s take a closer look at a few things.
Even though many businesses want to benefit from data-driven operations, not all are successful. This is mostly because the data collection uses a variety of applications and formats that could be more challenging to manage and organize. A startling 95% of firms identify managing unstructured data as a challenge. A well-developed BI framework ends all these problems by offering a structured management mechanism for the data.
More time for the IT department
Analytical responsibilities have been assigned to the IT department for many years. One of these duties is creating performance reports with data to assist managers and workers in making strategic decisions. Because of the daily analysis required as markets become more competitive, IT staff members are overworked and unable to keep up with demand. By implementing a clever BI architecture system, the IT staff will be greatly relieved of the tiresome chore of producing reports. Giving them ample time to concentrate on other crucial issues like cybersecurity and the proper operation of the business’ system.
In addition to relieving the IT department of time-consuming reporting responsibilities, incorporating the appropriate BI architecture into your company will boost productiveness. With a BI system, staff members can quickly automate their reports and access real-time data wherever they are.
Instead of waiting hours or days for data to be supplied to them in a static report, this will enable them to incorporate data into their strategy process. This is especially true because, in 2019, 64% of users said BI data and analytics increased their productivity and efficiency.
Most likely, data spread across numerous systems maintained individually by each department constitutes the opposite of a BI framework. This inevitably results in a lack of coordination between the many tasks and divisions, a reduction in efficiency, and increased expenses for the business. On the contrary, by offering consolidated access to corporate data, BI software help firms save money and time. Over time, a collaborative environment will be deployed across the entire organization, and every relevant stakeholder will be connected.
What is a data warehouse?
A data management system called a “data warehouse” is used to store a lot of data for processing and analysis in the future. Consider it a gigantic warehouse where trucks containing source data discharge their data. After sorting the data, it is placed on rows and rows of neatly arranged shelves, making it simple to locate the information you need later.
In layman’s terms, this means that DWHs are excellent at storing data that are;
- Integrated: Data from numerous databases and data sources are combined in this type.
- Granular: They store extremely comprehensive data that is useful in various ways.
- Historical: They can store an ongoing record of data for many years.
On real servers that your business owns and maintains, on-premise data warehouses are operated. In fully online cloud DWHs, like Amazon Redshift, you pay for server space that is maintained by a different business. Businesses gradually moving to the cloud utilize hybrid DWHs, which combine on-premise and cloud storage.
Data warehouses use a particular method of processing data known as online analytical processing (OLAP), which is well suited for sophisticated queries because all the data is kept in one location.
OLAP is a technique of organizing and navigating among the rows and rows of shelves in your data warehouse so that you can locate the information you’re looking for fast when you go there to figure out how one data set relates to another.
This is beneficial for business intelligence because it is uncommon to make choices by asking simple inquiries about your facts. Data warehouses make it incredibly efficient to find answers to these difficult issues since they employ OLAP. They have so evolved into the basis for numerous effective business intelligence systems.
What is a data mart in business intelligence?
For some applications, using a DWH can be like using a sledgehammer to swat a fly. For instance, you can build up a data mart if the marketing team frequently visits the warehouse to run related queries.
Data marts are carefully selected data sets designed for certain use cases. The marketing staff can visit the treatment center only sometimes they require water, to use another example from Dixon’s description. Data/water can be packaged using the data warehouse and put into “water bottles” that are ready to drink.
The data warehouse continues to be the foundation of this data storage ecosystem. It provides a comprehensive, consolidated picture (like a data lake) and is structured and somewhat simple to grasp (like source data), making it much simpler to use that data however you need (like creating data marts).
How do data warehouses work?
Although they are highly sophisticated systems, DWHs mainly consist of labor, software, and storage components. It would be best if you weighed the costs of each of these three when deciding whether to establish a DWH.
Your data warehouse might be hosted on-premises, in the cloud, or using a hybrid strategy. Some believe that on-premises hosting is going to disappear. Because you are renting space on someone else’s server, cloud hosting is far more affordable and flexible.
There is no need for maintenance, you can add to it or reduce it as necessary, and new features are continuously being introduced. Hybrid hosting, which, as we previously indicated, is the preferred option for businesses moving from on-premises to cloud hosting, bridges the gap between these two strategies.
You need to utilize a sort of software generally referred to as ETL software to get data into your data warehouse. Data is extracted, prepared for usage, and then put into the DWH using the extract, transform, and load (ETL) process.
What’s the role of data warehouses in business intelligence?
Every effective BI system has a potent DWH at its core. Just because a data warehouse is a platform used to centrally gather, store, and prepare data from many sources for later use in business intelligence and analytics. Consider it as a single repository for all the data needed for BI analyses.
Historical and current data are kept structured, ideal for sophisticated querying in a data analytics DWH. Once connected, it produces reports with forecasts, trends, and other visualizations that support practical insights using business intelligence tools.
ETL (extract, transform, and load) tools, a DWH database, DWH access tools, and reporting layers are all parts of the business analytics data warehouse. These technologies are available to speed up the data science procedure and reduce or completely do away with the requirement for creating code to handle data pipelines.
The ETL tools assist in data extraction from source systems, format conversion, and data loading into the DWH. Structured data for reporting is stored and managed by the database component. Users of business intelligence and data analytics can interact with the data stored in the DWH thanks to the access tools. In order to analyze and visualize the data kept in the DWH, the reporting layer provides a BI interface.
The differences between business intelligence and data warehousing
Business intelligence and data warehousing differ in a number of meaningful areas. Prior to going into these differences, it is crucial to understand that they are both equally significant for a comprehensive business intelligence strategy because they operate in the same field.
Data analysis and providing decision-makers with key insights are the main goals of BI. A data warehouse in this context serves as a central location for collecting, analyzing, and storing data from numerous unrelated sources.
Through forecasting and predictive analytics, BI aims to assist business users in making informed and fact-based business decisions. On the other hand, a data warehouse’s goal is to centrally store structured data so that BI users can access a comprehensive view of the organization’s data.
Dashboards, reports, data visualizations, charts, and graphs that show trends and insights are examples of BI output. Business users can make sense of complex data with the help of such outcomes. Data records maintained in fact and dimension tables of data models make up a DWH’s output.
C-level executives, managers, or data analysts are typically BI users who want to perform rapid data analysis for enhanced decision-making. In contrast, data architects and engineers often manage and maintain DWHs and deliver analysis-ready data to business users.
SAP, Power BI, Tableau, and Qlik are some examples of frequently used BI tools. On the other hand, well-known providers of data warehouses include Azure Synapse, Google BigQuery, and Amazon Redshift.
How is data analyzed using a data warehouse?
DWHs process huge swathes of data using online analytical processing (OLAP), which makes use of the fact that all the data is gathered on a single platform. It is a method of data processing that DWHs use to simplify difficult queries. It is, in essence, a computer technique that enables users to extract and query the necessary data for analysis.
As an illustration, OLAP processing would be used to swiftly search through the stored data to find, identify, and summarize the needed information if someone asked about the relationship between two separate datasets in a DWH. A data warehouse gives BI the data it needs to evaluate through OLAP.
Benefits of business intelligence and data warehousing for enterprises
Without a DWH, business intelligence architecture is comparable to a vehicle without an engine. As a result, despite their differences, data warehouse and business intelligence work best together to offer firms a solid BI architecture.
Organizations frequently incorporate enterprise DWHs in business analytics architecture to implement business intelligence and data warehousing, adhering to industry best practices (BIDW). The term “BIDW” refers to the complete BI architecture in which reliable and accurate data are effortlessly retrieved from DWHs to produce insightful knowledge that can be used to make rapid and informed decisions.
By this point, you should know how databases, data warehouses, and business intelligence work together to create efficient workflows. Business intelligence and data warehousing are separate terms that fall under the BI umbrella and are collectively referred to as BIDW.
In today’s competitive corporate world, the outdated ETL system is unacceptable. By streamlining and simplifying the data preparation process, businesses save money and operate more quickly to integrate innovative solutions.