To help organizations make more data-driven decisions, business intelligence (BI) combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices. In practice, you've got modern business intelligence when you have a holistic view of your company's data and can use it to drive change, eliminate inefficiencies, and quickly adapt to market or supply changes. However, to give you a better sense of the concept, we have structured this article to explain the key aspects of Business Intelligence. #TWN
The procedural and technical infrastructure that collects, stores, and analyses the data generated by a company's activities is referred to as business intelligence (BI).
Data mining, process analysis, performance benchmarking, and descriptive analytics are part of business intelligence. BI parses all of a company's data and presents it in easy-to-understand reports, metrics, and trends that help managers make better decisions.
The need for BI arose from the idea that managers who have inaccurate or incomplete information make worse decisions on average than those who have better information. It is known as "garbage in, garbage out" among financial model creators.
BI tries to solve this problem by analyzing current data and presenting it in the form of a dashboard of quick metrics that can help you make better decisions.
Most businesses can benefit from BI solutions; managers who have inaccurate or incomplete data are more likely to make poor decisions than those who have better data.
To be useful, BI must strive to improve data accuracy, timeliness, and volume.
These requirements imply finding new ways to capture data that isn't already being recorded, double-checking data for errors, and organizing data in a way that allows for broad analysis.
In practice, however, companies have data that is unstructured or in a variety of formats, making collection and analysis difficult. As a result, software companies offer business intelligence solutions to help users get the most out of their data. These are enterprise-level software applications that combine data and analytics for a company.
Even though software solutions are evolving and becoming more sophisticated, data scientists must still manage the trade-offs between speed and reporting depth.
Companies are scrambling to capture all of the insights emerging from big data, but data analysts can usually filter out sources to find a set of data points that can represent the health of a process or business area as a whole. It can reduce the amount of data that needs to be captured and reformatted for analysis, saving time and speeding up reporting.
BI software and tools come in a variety of shapes and sizes. Let's take a look at some of the most common BI solutions.
Spreadsheets: Some of the most widely used BI tools are spreadsheets such as Microsoft Excel and Google Docs.
Reporting software: Data is reported, organized, filtered, and displayed using reporting software.
Data visualization software: To quickly gain insights, data visualization software converts datasets into easy-to-read, visually appealing graphical representations.
Data mining tools: Artificial intelligence, machine learning, and statistics are used in data mining tools to "mine" large amounts of data for patterns.
Online analytical processing (OLAP): OLAP tools enable users to examine datasets from a variety of perspectives based on various business objectives.
Companies use BI for a variety of reasons. Many people use it to help with things like hiring, compliance, production, and marketing. It's difficult to find a business area that doesn't benefit from better data to work with when it comes to BI.
Faster, more accurate reporting and analysis, improved data quality, better employee satisfaction, reduced costs and increased revenues, and the ability to make better business decisions are just a few of the many advantages that companies can reap after incorporating BI into their business models.
BI was created to help businesses avoid the problem of "garbage in, garbage out," which occurs when data is analyzed incorrectly or insufficiently.
If, for example, you are in charge of several beverage factories' production schedules and sales in a particular region are increasing month over month, you can approve extra shifts in near real-time to ensure your factories can meet demand.
Similarly, if sales begin to suffer as a result of a cooler-than-normal summer, you can quickly shut down that same production. This production manipulation is just one example of how, when used correctly, BI can boost profits and cut costs.
Lowe's Corp., the nation's second-largest home improvement retailer, was one of the first big-box retailers to use business intelligence tools. It has used BI tools to optimize its supply chain, analyze products for potential fraud, and resolve issues with collective delivery charges from its stores, among other things.
Coca-Cola Bottling had an issue with its daily manual reporting processes: access to real-time sales and operations data was restricted.
However, by replacing the manual process with an automated business intelligence system, the company was able to completely automate the process and save 260 hours per year (or more than six 40-hour work weeks). With just a few clicks, the company's team can now quickly analyze metrics like delivery operations, budget, and profitability.
Microsoft's Power BI is a business analytics platform. It allows individuals and businesses to connect to, model, and visualize data using a scalable platform, according to the company.
Self-service BI is a type of analytics that allows people with no technical knowledge to access and explore data. In other words, it gives data control to people across the organization, not just those in the IT department.
Self-service BI has several drawbacks, including the end-users false sense of security, high licensing costs, a lack of data granularity, and sometimes too much accessibility.
One of the main Business Intelligence products of IBM is Cognos Analytics Tool, which the company touts as an AI-powered, all-inclusive BI solution.
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