Data governance management and control system and method based on artificial intelligence

Abstract

The invention relates to the field of data management, and discloses a data governance management and control system and method based on artificial intelligence, and the data governance management and control method comprises the following steps: S1, presetting a customer portrait through a preset module; s2, obtaining marketing data of each channel through a marketing module; (S3); obtaining business data of each channel through a business module; s4, analyzing the preset customer portrait, marketing data and business data of each channel through an analysis module to obtain a comprehensive judgment index of each channel; s5, analyzing the comprehensive judgment index of each channel through an analysis module to obtain a data quality rating of each channel; the data quality is comprehensively evaluated from multiple perspectives of the preset customer portrait, the marketing data and the business data, the accuracy of data quality evaluation is improved, a quality rating result can enable a manager to visually know the data quality of each channel, and a scientific basis is provided for subsequent adjustment.

Description

Technical Field

[0001] The present invention relates to the field of data management, and in particular to an artificial intelligence-based data governance and control system and method. Background Art

[0002] Data governance is a set of management behaviors involving the use of data. For example, through the Internet of Things and electronic sentinel systems, data governance is performed on the information of people entering designated areas, the security level of designated areas is determined, and the management unit of the area is helped to manage regional security. Data governance can also be performed on enterprise data to facilitate business decisions of enterprise decision makers.

[0003] With the rapid development of the intelligent era and digital technology, enterprises are in fierce competition. In order to obtain more market resources, they need to promote on more channels. Usually, different staff members within the enterprise will be responsible for promoting different channels. Each channel obtains a large amount of data, which is then distributed to the business department for follow-up. Usually, the business department will evaluate the data quality of each channel based on the number of transactions and adjust the promotion structure of each channel.

[0004] However, each channel has a different promotion period, and it is difficult to accurately evaluate the data quality of each channel based solely on the number of transactions. Therefore, this evaluation cannot be used as a basis for adjusting the promotion structure. Summary of the invention

[0005] The purpose of the present invention is to provide a data governance control system and method based on artificial intelligence to solve the following technical problems:

[0006] How to improve the accuracy of evaluation data quality.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A data governance and control method based on artificial intelligence, the data governance and control method comprising the following steps:

[0009] S1: Preset customer profiles through preset modules;

[0010] S2: Obtain marketing data from various channels through the marketing module;

[0011] S3; obtain business data of each channel through the business module;

[0012] S4: Analyze the preset customer portraits, marketing data and business data of each channel through the analysis module to obtain the comprehensive judgment index of each channel;

[0013] S5: Analyze the comprehensive judgment index of each channel through the analysis module to obtain the data quality rating of each channel.

[0014] As a further solution of the present invention: the marketing data includes channel operation time, daily marketing customer acquisition data and marketing expenses;

[0015] The business data includes the number of Class A customers and the completion amount.

[0016] As a further solution of the present invention: the process of obtaining the comprehensive judgment index of each channel is:

[0017] S10: Analyze the daily marketing customer acquisition data of each channel to obtain the daily customer portrait of each channel;

[0018] S20: By analyzing the daily customer portraits and preset customer portraits of each channel, a portrait matching degree change curve of the daily customer acquisition of each channel over time is obtained;

[0019] S30: randomly obtaining enough points as marking points on the portrait matching degree change curve of the daily customer acquisition of each channel over time; and obtaining the derivative corresponding to each marking point;

[0020] S40: Obtain the first judgment index of each channel by analyzing the portrait matching degree change curve over time of the portrait matching degree of each channel's daily customer acquisition, the channel operation time, and the derivative corresponding to each marked point;

[0021] S50: Obtain the second judgment index of each channel by analyzing the daily marketing customer acquisition data, marketing expenditure, number of A-level customers and completion amount of each channel;

[0022] S60: Obtaining a comprehensive judgment index of each channel by analyzing the first judgment index and the second judgment index of each channel.

[0023] As a further solution of the present invention: the first judgment index is determined by the formula:

[0024] Calculate the First Judgment Index of each channel ;

[0025] in, Number the channel; For the The profile matching curve of the daily customer acquisition of each channel over time; is the current time; For the Channel operation time of each channel; For the The maximum value of the profile matching degree of daily customer acquisition through the channels; is the basic portrait matching value; is the preset adjustment coefficient; is the number of randomly selected annotation points on the portrait matching degree change curve; For the The derivative corresponding to the marked points; It is the first basic judgment index; is the first weight coefficient; is the second weight coefficient; is the third weight coefficient.

[0026] As a further solution of the present invention: the preset adjustment coefficient To pass the formula: get;

[0027] in, is the first preset constant.

[0028] As a further solution of the present invention: the second judgment index is obtained by the formula:

[0029]

[0030] Calculate the The second judgment index of the channel ;

[0031] in, For the Number of A-level customers in each channel; For the Total number of customers acquired through marketing through each channel up to the current time; For the The total amount of completion of each channel; For the Total marketing spend by channel; It is the second basic judgment index.

[0032] As a further solution of the present invention: the comprehensive judgment index is obtained by the formula:

[0033]

[0034] Calculate the Comprehensive judgment index of each channel .

[0035] As a further solution of the present invention: the rating process for data quality is:

[0036] The first Comprehensive judgment index of each channel With preset threshold Make comparisons;

[0037] when , explain The data quality of each channel is high, and the rating result is level 1; no warning is issued;

[0038] when , explain The data quality of the channels is average, and the rating result is level 2; no warning is issued;

[0039] when , explain The data quality of the channels was low and the rating result was level three; a warning was issued.

[0040] A data governance control system based on artificial intelligence, the control system comprising:

[0041] Preset module, used to preset customer portraits;

[0042] Marketing module, used to obtain marketing data from various channels;

[0043] Business module, used to obtain business data of each channel;

[0044] The analysis module is used to analyze the preset customer portraits, marketing data and business data of each channel to obtain the comprehensive judgment index of each channel; and to analyze the comprehensive judgment index of each channel to obtain the data quality rating of each channel.

[0045] Beneficial effects of the present invention:

[0046] (1) The present invention presets customer portraits through a preset module; obtains marketing data of each channel through a marketing module; obtains business data of each channel through a business module; analyzes the preset customer portraits, marketing data and business data of each channel through an analysis module to obtain a comprehensive judgment index of each channel; analyzes the comprehensive judgment index of each channel to obtain a data quality rating of each channel; comprehensively evaluates data quality from multiple perspectives of preset customer portraits, marketing data and business data to improve the accuracy of data quality evaluation. The quality rating results can enable managers to intuitively understand the data quality of each channel and provide a scientific basis for subsequent adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention will be further described below in conjunction with the accompanying drawings.

[0048] Figure 1 A method flow chart of an embodiment of the present invention;

[0049] Figure 2 A system module framework diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] See also Figure 1 As shown, in one embodiment, a data governance and control method based on artificial intelligence is provided, and the data governance and control method includes the following steps:

[0052] S1: Preset customer profiles through preset modules;

[0053] S2: Obtain marketing data from various channels through the marketing module;

[0054] S3; obtain business data of each channel through the business module;

[0055] S4: Analyze the preset customer portraits, marketing data and business data of each channel through the analysis module to obtain the comprehensive judgment index of each channel;

[0056] S5: Analyze the comprehensive judgment index of each channel through the analysis module to obtain the data quality rating of each channel;

[0057] Through the above technical scheme, this embodiment presets customer portraits through a preset module; obtains marketing data of each channel through a marketing module; obtains business data of each channel through a business module; analyzes the preset customer portraits, marketing data and business data of each channel through an analysis module to obtain a comprehensive judgment index of each channel; analyzes the comprehensive judgment index of each channel to obtain a data quality rating of each channel; comprehensively evaluates data quality from multiple perspectives of preset customer portraits, marketing data and business data to improve the accuracy of data quality evaluation. The quality rating results can enable managers to intuitively understand the data quality of each channel and provide a scientific basis for subsequent adjustments.

[0058] As an implementation mode of the present invention, the marketing data includes channel operation time, daily marketing customer acquisition data and marketing expenses;

[0059] The business data includes the number of Class A customers and the amount completed;

[0060] As an implementation mode of the present invention, the process of obtaining the comprehensive judgment index of each channel is as follows:

[0061] S10: Analyze the daily marketing customer acquisition data of each channel to obtain the daily customer portrait of each channel;

[0062] S20: By analyzing the daily customer portraits and preset customer portraits of each channel, a portrait matching degree change curve of the daily customer acquisition of each channel over time is obtained;

[0063] S30: randomly obtaining enough points as marking points on the portrait matching degree change curve of the daily customer acquisition of each channel over time; and obtaining the derivative corresponding to each marking point;

[0064] S40: Obtain the first judgment index of each channel by analyzing the portrait matching degree change curve over time of the portrait matching degree of each channel's daily customer acquisition, the channel operation time, and the derivative corresponding to each marked point;

[0065] S50: Obtain the second judgment index of each channel by analyzing the daily marketing customer acquisition data, marketing expenditure, number of A-level customers and completion amount of each channel;

[0066] S60: Obtaining a comprehensive judgment index of each channel by analyzing the first judgment index and the second judgment index of each channel;

[0067] Through the above technical solution, this embodiment obtains the daily customer portrait of each channel by analyzing the daily marketing customer acquisition data of each channel; the process of obtaining the customer portrait is an existing technology and will not be described in detail here; then, by analyzing the daily customer portrait and the preset customer portrait of each channel, the portrait matching degree change curve of the daily customer acquisition of each channel over time is obtained; the method of analyzing two customer portraits to obtain the portrait matching degree is an existing technology and will not be described in detail here; then, randomly obtain enough points as marking points on the portrait matching degree change curve of the daily customer acquisition of each channel over time; obtain the derivative corresponding to each marked point; then, by analyzing the portrait matching degree change curve of the daily customer acquisition of each channel over time, the channel operation time and the derivative corresponding to each marked point, obtain the first judgment index of each channel; then, by analyzing the daily marketing customer acquisition data, marketing expenses, number of A-level customers and completion amount of each channel, obtain the second judgment index of each channel; finally, by analyzing the first judgment index and the second judgment index of each channel, obtain the comprehensive judgment index of each channel; specifically, the A-level customer can be a transaction customer.

[0068] As an implementation mode of the present invention, the first judgment index is obtained by the formula:

[0069] Calculate the First Judgment Index of each channel ;

[0070] in, Number the channel; For the The profile matching curve of the daily customer acquisition of each channel over time; is the current time; For the Channel operation time of each channel; For the The maximum value of the profile matching degree of daily customer acquisition through the channels; is the basic portrait matching value; is the preset adjustment coefficient; is the number of randomly selected annotation points on the portrait matching degree change curve; For the The derivative corresponding to the marked points; It is the first basic judgment index; is the first weight coefficient; is the second weight coefficient; is the third weight coefficient;

[0071] Through the above technical solution, this embodiment The preset portrait matching degree corresponding to the current time; For the The difference between the maximum value of the portrait matching degree of the daily customer acquisition through the channels and the preset portrait matching degree; , explain The maximum value of the profile matching degree of the daily customer acquisition through the channels exceeds the preset profile matching degree, and the The difference between the maximum value of the profile matching degree of the daily customer acquisition channels and the preset profile matching degree The larger the value, the higher the data quality of the channel. Therefore, the first judgment index The bigger; when , explain The maximum value of the portrait matching degree of the daily customer acquisition through the channels is lower than the preset portrait matching degree, and The absolute value of the difference between the maximum value of the profile matching degree of the daily customer acquisition channels and the preset profile matching degree The larger the value, the lower the data quality of the channel. Therefore, the first judgment index The smaller; For the Channel operating time The cumulative value of the portrait matching degree of daily customer acquisition; For the The average profile matching degree of customers acquired through channels every day; , explain The average profile matching degree of customers acquired daily by the channels is greater than the preset profile matching degree, and the The difference between the average profile matching degree of customers acquired daily by each channel and the preset profile matching degree The larger the value, the higher the data quality of the channel. Therefore, the first judgment index The bigger; when , explain The average profile matching degree of customers acquired through the channels every day is lower than the preset profile matching degree, and The absolute value of the difference between the average profile matching degree of customers acquired daily by each channel and the preset profile matching degree The larger the value, the lower the data quality of the channel. Therefore, the first judgment index The smaller; For the The derivative corresponding to the marked point, that is, The profile matching change curve of the channel is The slope of the marked points; , explain The profile matching change curve of the channel is The number of marked points is increasing; for The average slope of the marked points; , explain The matching degree of the channel portraits in the channel operation time The internal whole is growing over time, so the data quality is gradually improving; therefore, the first judgment index The bigger; when , explain The matching degree of the channel portraits in the channel operation time The overall value is decreasing over time, so the data quality is gradually decreasing; therefore, the first judgment index The smaller;

[0072] It should be noted that the basic portrait matching value , the first basic judgment index The first weight coefficient , the second weight coefficient and the third weight coefficient It is a preset value obtained based on experience and will not be described in detail here.

[0073] As an implementation mode of the present invention, the preset adjustment coefficient To pass the formula: get;

[0074] in, is the first preset constant;

[0075] Through the above technical solution, this embodiment uses the formula Adjustment coefficient As channel operation time increases The matching degree of preset portraits increases with the growth of channel operation time. , which makes the preset portrait matching degree of the channel lower in the early stage of marketing, and the requirements for the preset portrait matching degree in the middle and late stages of marketing gradually increase;

[0076] It should be noted that the first preset constant It is a preset value obtained based on experience and will not be described in detail here.

[0077] As an embodiment of the present invention, the second judgment index is obtained by the formula:

[0078]

[0079] Calculate the The second judgment index of the channel ;

[0080] in, For the Number of A-level customers in each channel; For the Total number of customers acquired through marketing through each channel up to the current time; For the The total amount of completion of each channel; For the Total marketing spend by channel; It is the second basic judgment index;

[0081] Through the above technical solution, this embodiment For the The higher the customer business rate, the greater the business intention. The higher the quality of the data from each channel, the higher the second judgment index The bigger; is the average customer unit price; the higher the average customer unit price, the greater the customer demand. The higher the quality of the data from each channel, the higher the second judgment index The bigger; The customer acquisition cost for a single A-level customer; is the cost of acquiring a single A-level customer. The larger the cost of acquiring a customer, the higher the The higher the data cost of each channel source, the higher the second judgment index The smaller;

[0082] It should be noted that the second basic judgment index It is a preset value obtained based on experience and will not be described in detail here.

[0083] As an implementation mode of the present invention, the comprehensive judgment index is obtained by the formula:

[0084]

[0085] Calculate the Comprehensive judgment index of each channel ;

[0086] in, is the first comprehensive weight coefficient; is the second comprehensive weight coefficient;

[0087] Through the above technical solution, the first judgment index of this embodiment The bigger, the The higher the data quality of each channel, the higher the first judgment index The smaller, the The lower the data quality of each channel, the lower the second judgment index The bigger, the The higher the data quality of each channel, the higher the second judgment index The smaller, the The lower the data quality of each channel;

[0088] It should be noted that the first comprehensive weight coefficient and the second comprehensive weight coefficient It is a preset value obtained based on experience and will not be described in detail here.

[0089] As an implementation of the present invention, the data quality rating process is as follows:

[0090] The first Comprehensive judgment index of each channel With preset threshold Make comparisons;

[0091] when , explain The data quality of each channel is high, and the rating result is level 1; no warning is issued;

[0092] when , explain The data quality of the channels is average, and the rating result is level 2; no warning is issued;

[0093] when , explain The data quality of the channels is low, and the rating result is level three; an early warning is issued;

[0094] Through the above technical solution, this embodiment passes Comprehensive judgment index of each channel With preset threshold For comparison, , explain The data quality of the channels is high, and the rating result is level one; no warning is issued; when , explain The data quality of the channels is average, and the rating result is level 2; no warning is issued; , explain The data quality of a channel is low, and the rating result is level three; issue an early warning; promptly remind managers to deal with the channels with data quality; provide managers with a basis for decision-making;

[0095] It should be noted that the preset threshold It is a preset value obtained based on experience and will not be described in detail here.

[0096] See also Figure 2 As shown, a data governance control system based on artificial intelligence, the control system includes:

[0097] Preset module, used to preset customer portraits;

[0098] Marketing module, used to obtain marketing data from various channels;

[0099] Business module, used to obtain business data of each channel;

[0100] The analysis module is used to analyze the preset customer portraits, marketing data and business data of each channel to obtain the comprehensive judgment index of each channel; and to analyze the comprehensive judgment index of each channel to obtain the data quality rating of each channel.

[0101] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A data governance and control method based on artificial intelligence, characterized in that: The data governance control method comprises the following steps: S1: Preset customer profiles through preset modules; S2: Obtain marketing data from various channels through the marketing module; S3; obtain business data of each channel through the business module; S4: Analyze the preset customer portraits, marketing data and business data of each channel through the analysis module to obtain the comprehensive judgment index of each channel; S5: Analyze the comprehensive judgment index of each channel through the analysis module to obtain the data quality rating of each channel.

2. According to the data governance and control method based on artificial intelligence in claim 1, it is characterized in that: The marketing data includes channel operation time, daily marketing customer acquisition data and marketing expenditure; The business data includes the number of Class A customers and the completion amount.

3. According to the data governance and control method based on artificial intelligence in claim 2, it is characterized in that: The process of obtaining the comprehensive judgment index of each channel is as follows: S10: Analyze the daily marketing customer acquisition data of each channel to obtain the daily customer portrait of each channel; S20: By analyzing the daily customer portraits and preset customer portraits of each channel, a portrait matching degree change curve of the daily customer acquisition of each channel over time is obtained; S30: randomly obtaining enough points as marking points on the portrait matching degree change curve of the daily customer acquisition of each channel over time; and obtaining the derivative corresponding to each marking point; S40: Obtain the first judgment index of each channel by analyzing the portrait matching degree change curve over time of the portrait matching degree of each channel's daily customer acquisition, the channel operation time, and the derivative corresponding to each marked point; S50: Obtain the second judgment index of each channel by analyzing the daily marketing customer acquisition data, marketing expenditure, number of A-level customers and completion amount of each channel; S60: Obtaining a comprehensive judgment index of each channel by analyzing the first judgment index and the second judgment index of each channel.

4. According to the data governance and control method based on artificial intelligence in claim 3, it is characterized in that: The first judgment index is through the formula: Calculate the First Judgment Index of each channel ; in, Number the channel; For the The profile matching curve of the daily customer acquisition of each channel over time; is the current time; For the Channel operation time of each channel; For the The maximum value of the profile matching degree of daily customer acquisition through the channels; is the basic portrait matching value; is the preset adjustment coefficient; is the number of randomly selected annotation points on the portrait matching degree change curve; For the The derivative corresponding to the marked points; It is the first basic judgment index; is the first weight coefficient; is the second weight coefficient; is the third weight coefficient.

5. According to the artificial intelligence-based data governance and control method of claim 4, it is characterized in that: The preset adjustment coefficient To pass the formula: get; in, is the first preset constant.

6. The data governance and control method based on artificial intelligence according to claim 5 is characterized in that: The second judgment index is calculated by the formula: ； Calculate the The second judgment index of the channel ; in, For the Number of A-level customers in each channel; For the Total number of customers acquired through marketing through each channel up to the current time; For the The total amount of completion of each channel; For the Total marketing spend by channel; It is the second basic judgment index.

7. The data governance and control method based on artificial intelligence according to claim 6 is characterized in that: The comprehensive judgment index is obtained by the formula: ； Calculate the Comprehensive judgment index of each channel .

8. The data governance and control method based on artificial intelligence according to claim 7 is characterized in that: The process of rating data quality is as follows: The first Comprehensive judgment index of each channel With preset threshold Make comparisons; when , explain The data quality of each channel is high, and the rating result is level 1; no warning is issued; when , explain The data quality of the channels is average, and the rating result is level 2; No warning is given; when , explain The data quality of the channels was low and the rating result was level three; a warning was issued.

9. An artificial intelligence-based data governance and control system, applicable to an artificial intelligence-based data governance and control method according to any one of claims 1 to 8, characterized in that: The control system includes: Preset module, used to preset customer portraits; Marketing module, used to obtain marketing data from various channels; Business module, used to obtain business data of each channel; The analysis module is used to analyze the preset customer portraits, marketing data and business data of each channel to obtain the comprehensive judgment index of each channel; and to analyze the comprehensive judgment index of each channel to obtain the data quality rating of each channel.

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