After several years of accumulation, most of the medium and large enterprises and institutions have established relatively complete basic information systems such as CRM, ERP, and OA. The unified characteristics of these systems are: through the operation of business personnel or users, the database is finally added, modified, deleted and other operations. The above systems can be collectively called OLTP (Online Transaction Process), which means that after the system runs for a period of time, it will inevitably help enterprises and institutions to collect a large amount of historical data. However, the large amount of data scattered and independent in the database is just some incomprehensible books for business personnel. What business people need is information, abstract information that they can read, understand and benefit from. At this time, how to convert data into information, so that business personnel (including managers) can fully grasp and utilize this information, and assist decision-making, is the main problem solved by business intelligence.
How to transform the data existing in the database into the information needed by business personnel? Most of the answers are reporting systems. Simply put, the reporting system can already be called BI, which is the low-end implementation of BI. Most of today's enterprises have entered the middle-end BI, which is called data analysis. Some enterprises have already started to enter high-end BI, called data mining. However, most of the enterprises in our country are still in the reporting stage.
Data reports are irreplaceable
The traditional reporting system is quite mature in technology, and the familiar Excel, Crystal Reports, Reporting Service, etc. have been widely used. However, with the increase of data and the improvement of demand, the traditional reporting system faces more and more challenges.
1. Too much data, too little information
Dense tables are piled with a lot of data. How many business people look at each data carefully? What information and trends do these data represent? The higher the level of leaders, the more concise information is needed. If I were the chairman, I might just need one sentence: Are we in good, moderate, or poor form right now?
2. Difficulty in interactive analysis and understanding of various combinations
Customized reports are too rigid. For example, we can list the sales of different regions and products in one table, and the sales of customers in different regions and different age groups in another table. However, these two tables cannot answer questions such as "the situation of young and middle-aged customers in North China buying digital camera type products". Business problems often require interactive analysis from multiple perspectives.
3. Difficulty digging out potential rules
The report system often lists superficial data information, but what rules are potentially contained in the depths of massive data? What customers are of the greatest value to us, and how interconnected products are? The greater the value, however, the more difficult it is to dig out.
4. It is difficult to trace history, and data forms isolated islands
There are many business systems, and data exists in different places. Too old data (such as data from a year ago) is often backed up by the business system, which makes macro analysis and long-term historical analysis very difficult. Therefore, with the development of the times, the traditional reporting system can no longer meet the growing business needs, and enterprises are looking forward to new technologies.
The era of data analysis and data mining is coming. It is worth noting that the purpose of data analysis and data mining systems is to bring us more decision support value, not to replace data reports. The report system still has its irreplaceable advantages, and will coexist with the data analysis and mining system for a long time.
Data mining sees through your needs
Broadly speaking, any process of mining information from a database is called data mining. From this point of view, data mining is BI. But in technical terms, data mining (Data Mining) specifically refers to: the source data has been cleaned and transformed into a data set suitable for mining. Data mining completes knowledge extraction on this fixed-form data set, and finally uses appropriate knowledge patterns for further analysis and decision-making. From this narrow point of view, we can define: data mining is the process of extracting knowledge from a specific form of data set. Data mining often selects one or more mining algorithms for specific data and specific problems, and finds the hidden laws under the data. These laws are often used to predict and support decision-making.
Related sales case:
American supermarkets have such a system: when you purchase a cart of goods and check out, after the saleswoman scans your products, some information will be displayed on the computer, and then the salesperson will ask you kindly: We have a disposable paper cup that is being Promotion, on the F6 shelf, do you want to buy it?
This sentence is by no means a general promotion. Because computer systems have long since figured it out, if you have napkins, large bottles of Coke, and salads in your shopping cart, there's an 86 percent chance that you're going to buy disposable paper cups. It turns out, you say, ah, thank you, I just haven't been able to find the paper cups. This is not some magical scientific fortune-telling, but a system implemented by the association rule algorithm in data mining.
Every day, new sales data will enter the mining model, and together with the historical data of the past N days, it will be processed by the mining model to obtain the most valuable association rules. The same algorithm, analyzing the sales performance of the online bookstore, the computer can find the correlation between the products and the strength of the correlation.
Data reporting, data analysis, and data mining are the three levels of BI. We believe that the trend in the next few years is that more and more enterprises will enter the field of data analysis and data mining on the basis of data reporting. The decision support function brought by business intelligence will bring us more and more obvious benefits.