The Revolution Of Today’s Company Is Called Business Intelligence (BI)

The Revolution Of Today’s Company Is Called Business Intelligence (BI)

Business Intelligence, known in Spanish as Business Intelligence, is the set of processes that allows us to analyze how a company works. Business Intelligence (BI) helps make decisions based on reading data. In practice, implementing BI in an enterprise means having a comprehensive view of all the data in the organization and then using this data to drive change, eliminate inefficiencies, and quickly adapt to changes in the market and demand.

History of Business Intelligence

BI has a relatively short history. It originally emerged in the 1960s as a system for sharing information between organizations. In the 1980s it was used to make decisions and transform data into information, later becoming IT-based service solutions. Today, factors such as flexible self-service analytics are prioritized. This means each user can access data from different locations, gain unified understanding, collaborate with other teams, and gain the visibility needed to consistently make faster, smarter decisions. All of this involves data management on reliable platforms, training of corporate users and a system that makes it possible to obtain information quickly.

Types of processes in Business Intelligence

In recent years, BI has evolved to include processes and activities that improve its performance. Among the most important processes we can point out the following:

1. Data mining

It involves using databases, statistics, and machine learning to discover trends in larger data sets.

2. Report generation

It consists of sharing data analysis with interested parties so that everyone can draw their own conclusions and make decisions.

3. Performance Metrics and Benchmarks

It is based on comparing the company’s current performance data with historical data to track performance against objectives.

4. Descriptive analyzes

It involves using preliminary data analysis to discover what happened in relation to a specific topic.

5. Consultations

A user can ask specific questions related to the data and Business Intelligence extracts the answers from the data sets.

6. Statistical analysis

Starting from the results of descriptive analyses, the data is further explored, for example, through statistics to determine how a trend occurred and why.

7. Data visualization

It consists of transforming data analysis into visual representations, such as graphs or histograms, in order to more easily understand the data.

8. Virtual Analysis

It is based on exploring data through visual storytelling to share information on the fly and stay in the flow of analysis.

9. Data preparation

You can collect various data sources, identify dimensions and measures, unify them and prepare the data for analysis.

Data analysis and business analysis

Business Intelligence (BI) includes data analysis and business analysis. However, these two aspects make up only part of the entire process.

BI-based technology helps draw conclusions from data, and to do so, they use advanced statistics and predictive analytics to discover current and future patterns. In this sense, data analysis asks the question: “Why did this happen and what might happen next?” On the other hand, all these models and algorithms are obtained, breaking down the results into an easy-to-understand representation.

Companies perform data analysis as part of a larger-scale BI strategy. The resulting information also allows decision making and can also be used to continuously improve a process. Business analysis should   not be a linear process, because the answer to one question may lead to new interactions and follow-up questions. The process should be considered as a cycle of data access, discovering, exploring and sharing information. This is called the analytics cycle , a current term that describes how companies use analytics on an ongoing basis to react to changes.

Also Read: 5 Ways To Generate Income With Technology And The Internet

Current trends in Business Intelligence

In a recent survey published by the company ManageEngine , more than 160 professionals from around the world pointed out their most common challenges when it comes to analyzing data. Here are some revealing conclusions:

1. Information analysis is no longer exclusive to information experts

Due to their high levels of complexity, Business Intelligence (BI) tools were traditionally placed in the hands of select data experts, which had the consequence that decision-making was limited to a few. However, in today’s world, data is an integral part of any business. Therefore, employees must have access to them daily to make their own decisions.

2. Empowering employees ensures better IT governance

Gone are the days when a system user had to wait patiently for the IT department to deliver a report or chart to obtain the required information. Today’s employees are no longer willing to rely on other sources to meet their reporting requirements and most prefer to do it themselves. Self-service BI tools offer much greater flexibility and allow you to quickly carry out a wide range of tasks, including the creation of customized reports, real-time understanding of the information required, and guidelines for taking necessary actions.

3. Customization replaces standardization completely

Different teams will likely have different reporting needs. With this in mind, self-service reports can provide a huge boost to productivity as they can be customized to the individual needs of staff. By enabling a higher level of customization, you can also provide insight into why certain strategies are more likely to work better than others.

4. On-demand reporting is essential

Ad-hoc reports are usually considered the most popular option, as they present answers to specific questions and analyze only selective data. With this type of self-service reports, users will have easy access to information and also the possibility of sharing it.

5. Visual data analysis is simply more popular

An intuitive and visually appealing user interface adds more context to data than other options and allows you to instantly view, interpret and analyze information.

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