Technologies and innovative solutions invented with their help enable companies to save money and time. Today’s technologies make different industries more informed about events, trends, and changes. Furthermore, they help to make business more transparent. Data has a special place in modern business and production.
Data analysis and business analytics are essential to running a modern business, as they give vast opportunities for comprehensive improvement. However, these definitions are often used as synonyms, and it seems they are the same. Is it so? While data analytics and business intelligence have a lot in common, they are not the same thing. In this article, you can find out about these two technologies and their difference.
Business Intelligence vs. Data Analytics
Business intelligence (BI) helps companies make better decisions based on accurate information. With business intelligence, companies gain a comprehensive view of enterprise data and can use the information to adapt to the market, increase efficiency and make changes. BI includes data mining and visualization. It also contains data tools and infrastructure. In addition, companies need the best methods to enable them to make data-based decisions and draw conclusions. toward
Data analytics is a process whose goal is to generate ideas for decision-making improvement. Recently, companies are also using geospatial data analytics, and the demand for it has grown. The advantage of this technology is that in addition to the usual data types, they also contain information about the place and time, which allows you to get a complete picture of events and changes.
Geospatial data analysis companies process a massive amount of geographic and geometric data, extracting useful information from them and enabling users to plot specific points on the map. In addition, users can receive geospatial visualizations and view them in real time. Users can evaluate the changes in different periods: from several days to several years. To learn more about geospatial data analysis, follow the link.
So, business intelligence and data analytics have many similarities. Still, in its purest form, data analytics focuses on the minor details of the process and raises questions about why a particular event happened and what is coming next. BI uses these models and algorithms and translates the results into the language of action. Accordingly, the main difference between these two concepts is that data analytics is more oriented toward forecasting, while the main task of BI is to provide information for making effective decisions.
How BI Works
The architecture of BI is not just software. BI data resides in an organization’s data warehouse or small data marts containing information for various departments linked to a company’s data warehouse. Furthermore, data lakes based on extensive data systems are used for analytics as repositories or landing pads for BI data.
BI tools allow you to make tactical and strategic decisions using historical and real-time data. In addition, integration tools help clean and consolidate raw data from different systems, integrate it, and manage its quality. In this way, stakeholders receive accurate information without contradictions.
Benefits of Business Intelligence
Companies are adopting BI for many different reasons. For example, it can be used to support marketing, recruitment, compliance, and more. In general, finding a business area that does not need accurate and high-quality information is difficult. The advantage of implementing BI is that this technology allows companies to receive fast and accurate reporting and analysis of high-quality data. Together with these benefits, employee satisfaction also increases, and revenues rise. Of course, access to reliable data and geospatial data predictive analytics enables you to increase the effectiveness of decisions.
For example, a scheduling manager in the beverage industry can assign additional shifts in real time if analytics show sales growth in a particular region. Thus, it is possible to increase production by satisfying the increased demand.
Otherwise, production can be suspended. It could be helpful if the summer turned out to be cooler and the demand for soft drinks fell due to the weather, which certainly affects sales. It is an example of how BI can help manufacturers save money or increase profits if the data is used correctly.