Big data era , Massive growth of data in various industries . However , Data resources are too scattered , There is a delay in getting , The phenomenon of islanding is serious , Make enterprises unable to quickly identify valuable data information , Affect the evaluation of enterprise data assets . therefore , Build enterprise data management system , It is a value depression to be excavated .

The dilemma of enterprise data management

at present , Most enterprises are facing the same dilemma ： A lot of fragmented data , Data is fragmented and accumulated for a long time , It is difficult to create effective value for enterprises . So , Enterprises do not hesitate to spend a lot of costs , We hope to use fragmented data to build “ Eiffel Tower ”, However, the reality is very skinny .

According to the IDC Survey results show that ： Data analysis and report production take a long time 、 Not enough depth 、 Lack of professional data analysis personnel and other problems , Making data-driven growth very difficult . among , Some enterprises have made great efforts , Analyze valuable data conclusions , However, due to their lack of understanding of business , Finally, it can't be used , Can't create value for the enterprise .

Business level requirements of enterprise data management

Actually , In the course of business operation , financial 、 sales 、 The market and other businesses have a strong demand for data analysis , Data analysis itself cannot bring maximum performance and efficiency to enterprises , However, the correct analysis results are applied to the business level in the most practical way and continue to produce benefits , This is It is the data management that enterprises constantly pursue . According to business logic , Data management can be divided into the following levels ：

1、 Business level

adopt Data collection 、 Statistics 、 Tracking and monitoring , Build a business management model , So as to scientifically guide and manage the business . Business is usually the level that most directly touches data , Therefore, there are often data without analysis and processing , Directly applied to routine business management . for example , Daily sales in sales business 、 Monthly sales 、 Completion of annual sales ; Traffic in the process of e-commerce marketing , Number of new users , Daily trading volume, etc .

2、 At the operational level

generally speaking , Enterprise operation pays more attention to the analysis and management of collected data , Specifically, it can be summarized as human 、 cargo 、 site 、 Only four aspects of analysis and management . For example, customer relationship management , Financial analysis management , Supply chain analysis management and so on .

3、 Business strategy

We will make the management decisions of the enterprise leadership , Analyze the data of each business link , Accordingly, we modify and formulate strategies, which are called enterprise business strategy management . for example , Analysis of consumer buying behavior , Member strategy making , Integral system / Discount system , Commodity pricing strategy , The marketing strategy , Advertising configuration , Product promotion mix and so on .

4、 Strategic planning level

strategic planning , That is, the enterprise combines internal and external , The process of making long-term plans for market external data , Such as enterprise competitiveness analysis , Industry environment analysis , Strategic goal planning and so on . The details are shown in the following figure ：

5、 Data management process

From a technical point of view , We can divide the implementation process of data management into the following 8 A step ： Demand analysis 、 data collection 、 Data collation 、 Data analysis 、 Data visualization 、 Template development 、 Analysis report 、 Template application .

Data management , More emphasis on process standardization , A clear division of responsibilities , Communicate effectively , Application templating and analysis intellectualization . Information department manages data , Business departments submit data requirements , The information or business department makes templates , Analyze data and generate reports , Leaders check the relevant data and make real-time summary and strategy adjustment , The management can view the operation status of the enterprise in real time , Major strategic adjustments of enterprises can directly call the analysis results and summary reports , This is the result of enterprise data management .

Data management product selection

With the acceleration of the global industrial economy era to the digital economy era , And the more profound business model changes brought about by information technology in the post epidemic era , Accelerating digital transformation has become an inevitable choice for enterprises . According to McKinsey research ： To 2025 year , The application of digital breakthrough technology will bring up to every year 1.2 Trillions to 3.7 The economic impact value of trillions of dollars , High digital transformation will increase the growth rate of enterprise revenue and profit compared with the average level 2.4 times ; And on the other hand , The agency is globally 800 The research of many traditional enterprises also shows that , The failure rate of enterprise digital transformation is as high as 80％.

Enterprises to ensure the realization of digital transformation , And the pursuit of a better transformation experience , Therefore, they actively adopt data platforms 、 Data application tools , Data management tools, etc , Data management products are bound to lay a solid foundation for the construction of enterprise data system .

Enterprise data construction , The method of combining data management products with general templates for enterprise management can be adopted . among , The general template of enterprise management refers to the process of data management , General 、 Repetitive operations , encapsulate , Modularization forms a general template or function . for example , Bank 、 Financial industry, etc , Through the existing data , Gender 、 regional 、 Age 、 Consumption frequency 、 Likes and so on label , Make a portrait of the user , Combined with big data algorithm , Refine labels consistent with enterprise products , Carry out precise push , Complete general template data management . The details are as follows ：

1、 General data analysis template

When designing templates, enterprise data analysts , Understand your own business and business logic , Implanted into the analysis template , Finally, the index module is formed 、 Business module 、 Global module . In this case , The template has strong versatility , It can be divided into documents 、 Applications 、OA、ERP System components, etc . Of course , Enterprises can also be in On this basis, develop a data management system , And develop in the direction of flattening Taiwan , It can also be used only as a data management and analysis module , Deployed in the existing management system .

Production management data analysis board

2、 Real time report & Real time large screen visual analysis

After the application of general data analysis template is mature , Data visualization applications can be used , So as to better support enterprises to quickly build customized data application systems . for example , Highly scalable datart Data visualization applications can be easily integrated 、 Embedded into the enterprise's internal business system 、 Third party Cloud Applications , As well as the local Excel and CSV It can also connect to its data center , Fully meet the scenario requirements of enterprise data management applications .

Production company level 、 Department level reports , Big data screen , Mobile applications, etc , The user can be in datart Data visualization applications can quickly build reports at all levels and complete classification . Its visual data chart is better than excel Clearer and more beautiful , Highlight data characteristics , Contrast relationship , Help management decision makers quickly analyze and judge existing problems , Find a way to cope .datart There are many chart types , Like a bar chart 、 Bar chart 、 The pie chart 、 Percentage chart 、 Indicator card 、 Data changes , Single line text and other rich graphics , as well as There are many agile and easy-to-use self-service data processing functions , Help businesses Users can complete data management by themselves , Such as data collaboration 、 Report sharing and mobile office , Data visualization tools help enterprises better interpret business data , Build enterprise data value , Improve the ability of application innovation , Help enterprises realize digital transformation and upgrading .

3、 Real time data platform

Big data era , Business data precipitation and real-time data processing have penetrated into all walks of life and created new business value , for example , Real time data platform flashflow Second level aging is handled end-to-end , Tens of megabytes / second / Throughput of nodes , Data processing does not omit 、 No repetition , Adapt to different integrated systems , Real time database provides a high-precision storage format , Just a simple configuration , The system can automatically generate according to metadata information catalog, Data development experience integrating streaming and batch , There is no need to write two sets of logic 、 Two sets of code , Just choose “ flow ” or “ batch ” Processing mode , It can be operated and debugged separately , Help enterprise users easily realize real-time data collection , Real time data processing and operation and maintenance , already Become the mainstream form of enterprise data application system .

Real time data collection

Data processing of stream batch integration

Enterprises manage product records through data 、 analysis 、 Reorganizing data , Efficient analysis of feedback data , Realize Market Research 、 New product development 、 Marketing activities 、 After sales service and other Omni channel data gathered , Realize scientific guidance for business . Besides , Real time data products in simple and agile application scenarios , Small investment , Easy to operate , The cost is low , There is little demand for technicians , Easily achieve the goal of cost reduction and efficiency increase , Help enterprises transform data information into their own product capabilities from multi-channel data , Improve the innovative ability of data application , Finally, maximize the value of data , It is the best choice for enterprise data construction products .