Why is bi so important to enterprises?

business intelligence （ or BI） It is a process used by enterprises to analyze data and guide business work through data conclusions . Usually , This process involves collecting your company's data into a data warehouse or other repository , And use specially designed tools to analyze the data . for example , You may need to check the customer's online shopping habits 、 Operating costs or regional sales information, etc , But these data are scattered in various departments , You have to use ETL（ extract 、 Transform and load ） The tool obtains the data from different departments of the enterprise into the data warehouse , And then through BI Analyze the integrated data . A foreign study shows that , Business intelligence analysis costs 1 The dollar will be recovered 13.01 dollar , Therefore, business intelligence is very important for enterprises to survive in the highly competitive business environment .

Why business intelligence （BI） Very important ？

The following list shows some common business intelligence features ：

The report . Provide summary data to key decision makers in the organization on a regular basis , To support their ability to make business decisions .

Data analysis . Discover data conclusions that can be used to make business decisions .

data mining . The process of searching hidden information from a large amount of data by algorithm .

Complex event handling . Complex event handling (CEP) It refers to the real-time analysis of convection data , Stream data is usually constantly updated data , For example, stock market information 、 Traffic report, etc .

Enterprise performance management . This is a set of analysis processes , It aims to analyze and measure the specific performance objectives defined by the enterprise for itself （ Or a set of goals ）. for example , Enterprises may set whether to deliver goods on time and customer satisfaction as operational objectives , And measure this goal by some values .

The benchmark . This is a set of analysis processes , It collects enterprise performance indicators , And compare them with the best standards defined by the industry .

Predictive analysis . Predictive analysis includes a range of statistical techniques , For example, data mining 、 Machine learning and predictive modeling , By analyzing historical data to predict the future .

business intelligence （BI） challenges

Data quality

Obtaining high-quality data is essential for good business analysis , Bad data leads to bad business intelligence . Data quality is a challenge , Here's why ：

1、 Data is out of date . In large scale 、 In a complex enterprise , The timeliness of data is very important .

2、 The company didn't take the time to maintain the data . In order to maintain quality data , The company needs to take measures to regularly clean and standardize the data .

The data is scattered in different systems

When data is scattered across different systems and other systems cannot access , It's called orphaned data . The problem with orphaned data is that other people in the organization cannot access it , This software is not compatible with other systems , Or the business department strictly controls the user permissions . When this happens , These key data will be locked , You can only get part of the data , So your business intelligence is incomplete . Use good ETL Tools can help you bring together data from different systems , So that the data can be used for analysis .ETL Tool recommendation Smartbi Self help of intelligent analysis ETL function , Complex data cleaning can be completed through simple operation .

Lack of expertise

Another challenge for business intelligence tools is that they may require a lot of expertise to use them . This means that only a few key people have the skills to use business intelligence tools effectively , This creates a bottleneck . Therefore, the convenience of operation is BI An important prerequisite for tools , But at present, in addition to Smartbi Beyond intellectual analysis , Other BI Tools have a certain technical threshold .

Business intelligence tools

Business intelligence tools are usually divided into three categories ： Local 、 Open source and cloud based tools . Using the right tools depends on your environment .

Local tools

Some popular local tools include Microsoft Power BI、Tableau and Smartbi. Local tools run primarily on the infrastructure of your enterprise , And usually used with traditional data warehouses that also run locally . however , They may not be as flexible and scalable as cloud solutions .

Open source tools

The advantage of open source tools is their low cost , If they're cloud based , It can also save you infrastructure costs . But they still need a certain degree of technical knowledge and manual coding to be used effectively . Some popular open source tools include Apache Hive and BIRT Project.

Cloud based tools

Cloud based business intelligence tools are particularly good at dealing with real-time data and large amounts of data . It's also quite cost-effective to buy them , Because the infrastructure and expertise required to maintain the environment are handled by the supplier , Users do not need to consider these specialties 、 Complex problems . Cloud based tools include Oracle Netsuite、Birst、GoodData、 Adaptive Insights And domestic Smartbi Intelligence analysis, etc .