Benefits and disadvantages of business intelligence (BI)

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In your management strategy, we recommend using Business Intelligence (BI) tools as a solution that helps you establish your goals. By utilizing BI tools, you can get out of business operations that rely solely on knowledge and experience by planning data-backed plans. You can also expect business efficiency and profit margin improvement by using it in each department within the company. This article describes BI and BI tools.

Purpose of BI tool

So why have BI tools been used by each company in recent years.

Until now, management decisions are often based on experience and accounts, and even if you use the data, you still need to manually analyze it using Excel. However, as the society becomes more digital, the amount of data exchanged will increase. In this, there will be limits in time and effort.

Business intelligence and process management tools born with this background have a primary purpose, such as 「 data, which is basically used to make it more efficient and business-friendly, although there are minor differences in functional aspects depending on the type. The vast amount of data we collect each day is just a number just by looking at it. It is the role of the BI tool to visualize such raw data using graphs and the like to help users analyze it, and ultimately to help them make speedy decisions and solve challenges. It is also characterized by limited organizations such as the IT department and those who have specialized knowledge and skills, as well as being designed to be easy for everyone to use.

In this way, BI tools are being introduced and leveraged in a variety of industries to meet the needs of creating new businesses that want to leverage their data to streamline their operations.

Data warehouse ( DWH )

Data Warehouse (DWH) is a system for collecting and storing various corporate data in one place. Corporate data is typically stored in a database. However, the database is divided into departments, and there are differences in data systems. Therefore, it cannot be used for analysis even if it is removed as it is.

So, for data analysis, put it once in a large hangar called DWH and then remove it. This is to facilitate analysis and removal by defining the storage form.

The data warehouse can also be queried in parallel by a parallel processing architecture. This allows for quick extraction at low loads.

OLAP analysis

OLAP analysis is an abbreviation for On-line Analytical Processing and has implications for online analytical processing. Online here means real-time, and quick aggregation is possible by handling data models of multidimensional structures.

The multidimensional structure is a data structure like a hexahedral puzzle, which allows you to handle multiple information in one block. The aggregated value ( measure ) is stored in a block and another information is inserted in the vertical axis, horizontal axis, and depth ( dimension ). For example, if you want to build a sales floor for a product by store, 「 the number sold to the block 」 「 Duration to depth 」 「 Product on vertical axis 」 「 Store on horizontal axis It is possible to store four pieces of information as 」.

Data mining

Data mining ( Data Mining ) means data mining and is a technique for using large amounts of data to find new lawfulness. You can discover the laws that cannot be obtained by human power, so you can use them for new management strategies.

Report creation

The data obtained by the analysis can be displayed on the dashboard for visual clarity. The aggregated results can be graphs as well as quadratic tables, and output is possible as a report.

Data analytics companies combined with IoT products, you can graph remote information in real time and regularly refer to the data as a standard report.

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