Digital transformation has entered a substantive stage, and enterprises' demand for data has also deepened. However, some enterprises have accumulated a large amount of data, but it is difficult to deeply release the value of data.
In recent years, as a powerful tool for data applications, the BI business intelligence analysis platform has also entered a turning point, and its development trend has clearly shown the characteristics of moving from IT to business, from reporting tools to analysis and decision-making, and gradually entering into the business analysis of business users. era. The essential reason is to meet the needs of enterprises to deeply apply data and produce practical effects.
Agile BI can quickly generate analysis reports by dragging and dropping, which can be completed by business personnel without technical background. Requirements such as creating new reports and changing reports can be completed within one day. Deep mining of data.
So, how can we use data thoroughly to drive business growth?
From an enterprise point of view, there are four main levels:
1. Data visualization
Data visualization is often referred to as "data report", and its function is to graphically describe the facts that have occurred. For example, show the company's overall sales performance, cost situation, profit situation, etc., as well as the corresponding year-on-year, chain-quarter, trend charts, etc. For BI products, it is possible to drill down and roll up detailed data, understand detailed business conditions, and conduct overall control of existing business conditions.
Data visualization is also the result achieved by most enterprises using BI products, which belongs to the primary stage of data application.
2. Business diagnosis
The data report shows the past and current business situation, but it cannot provide the specific reasons for the current situation. If it only relies on the data report, it still needs business personnel or managers to judge the data results based on their own experience and make an attribution . Obviously, there are still many subjective elements in this process.
Therefore, diagnostic analysis using BI tools is required to address the "why". Entering this stage, you need to use the AI-enhanced analysis module in the BI tool.
For example, for the automotive industry, IPTV is one of the key indicators. IPTV is the failure rate per 1,000 vehicles. If you only use data reports, you can only see the current changes in this indicator, and you cannot determine the core factors that affect its changes.
Through business diagnosis through BI, different car models can be selected for analysis. After the target is determined, the IPTV target achievement analysis status and the IPTV status of the whole car can be checked for the specific car model, and the specific failure mode and responsible department can be checked. Through data linkage, Check which link the responsibility problem is distributed in when a fault occurs, so as to perform corresponding optimization.
In addition, BI's AI-enhanced analysis module has its own data interpretation function, which can automatically count and analyze the factors that have a greater impact on the data; data insights can automatically provide the reasons for discovering the growth and decrease of business data.
3. Business forecast
The increasing uncertainty of the external environment makes enterprises urgently need to use data to judge the changes in business and market in the future. This requires the use of BI tools to achieve predictive analysis and solve the problem of "what may happen".
Predictive analysis requires the use of technologies such as predictive modeling, regression analysis, forecasting, multivariate statistics, pattern matching, and machine learning (ML), which can be easily realized through BI's built-in operators and models.
Take wind power equipment failure prediction as an example. First of all, data preprocessing is carried out, the processed data is divided into training set and test set, fault labeling and exploration analysis are carried out on the data, and the difference between fault data and normal data is analyzed. It is found that the fault data is obvious in the frequency domain. Rotational speed, mean value, variance, fault characteristic frequency, etc. are used to establish a fault prediction classification model, and the method can choose logistic regression, random forest, GBDT, etc.
Since the fault data only records whether there is a fault, but not the fault location, the binary classification algorithm of supervised learning is used when establishing the model. After the model is established, the test data can be used to calculate the evaluation index for model optimization, and the model with the best effect can be selected for deployment. , The accuracy rate of the final online model can reach more than 90%, which can effectively predict faulty wind turbines, predict faults in advance, reduce unplanned interruptions, increase overall equipment efficiency, reduce maintenance costs, and increase production capacity.
4. Normative Analysis
Prescriptive analysis uses data intelligence to directly give action suggestions for business decisions, so as to solve the problem of "how to do it". Obviously, to achieve accurate action recommendations, the process has a certain complexity.
Often prescriptive analytics needs to be combined with predictive analytics and use techniques such as operations research, image analysis, simulation, simulation, complex event processing, and recommendation engines.
At present, the new generation of agile BI is working in this direction and has reached the basic goal. For example, the data question answering function allows users to use text to enter questions, and the system automatically displays the answers in a visual way.
Agile BI can quickly generate analysis reports by dragging and dropping, which can be completed by business personnel without technical background. Requirements such as creating new reports and changing reports can be completed within one day. Deep mining of data.
It can be seen that data is like a treasure buried deep in the ground, and it needs to be dug deep to truly release its value. To make full use of data, on the one hand, enterprises need to use more intelligent BI tools to lower the threshold of data; on the other hand, enterprises also need to change their understanding of data application and gradually realize in-depth application of data.