CN113342796A - Data management method, device, equipment and storage medium - Google Patents
Data management method, device, equipment and storage medium Download PDFInfo
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Abstract
The application is applicable to the technical field of data processing, and provides a method, a device, equipment and a storage medium for data management. The method comprises the following steps: dividing the acquired data to be treated into a plurality of data blocks; monitoring each data block according to a preset data management requirement to obtain a monitoring result corresponding to each data block; determining abnormal data blocks in the plurality of data blocks according to the monitoring result corresponding to each data block; determining a data management strategy corresponding to the abnormal data block, and managing the abnormal data block according to the data management strategy to obtain a management result, wherein the data management strategy comprises a management scheme, management time and a management person; and managing enterprise data according to the treatment result. In the scheme, responsible persons corresponding to the abnormal data blocks are determined, a specific treatment scheme is given, the treatment duration is limited, the abnormal data blocks are ensured to be truly treated, the data treatment effect is improved, and the data treatment really forms a closed loop.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a data management method, device, equipment and storage medium.
Background
Data Governance (Data Governance) is a whole set of management activities in an organization that involve the use of Data. Initiated and enforced by enterprise data governance departments is a series of policies and procedures on how to formulate and enforce business applications and technical management for data throughout the enterprise. The final goal of data management is to improve the value of data, the data management is very necessary, and the data management is the basis for realizing digital strategy of enterprises, and is a management system comprising organization, system, process and tools.
In recent years, data governance has become a topic commonly spoken by the old, and the industry has deeper or shallower researches on model building, data maps, data quality, asset value and the like. However, most of the traditional data management methods only pay attention to how to make rules and standards for data management, the data management process is not complete, and a closed-loop data management system which tends to be stable is not formed so far, so that the data management effect is poor.
Disclosure of Invention
In view of this, the embodiments of the present application provide a method, an apparatus, a device, and a storage medium for data management to solve the problem that in the conventional data management method, most of the data management methods only pay attention to how to make rules and standards for data management, the data management process is not complete, and a closed-loop data management system that tends to be stable is not formed so far, resulting in poor data management effect.
A first aspect of an embodiment of the present application provides a method for data governance, where the method includes:
dividing the acquired data to be treated into a plurality of data blocks, wherein the data to be treated comprises enterprise data;
monitoring each data block according to a preset data management requirement to obtain a monitoring result corresponding to each data block;
determining abnormal data blocks in the plurality of data blocks according to the monitoring result corresponding to each data block;
determining a data governance strategy corresponding to the abnormal data block, and governing the abnormal data block according to the data governance strategy to obtain a governance result, wherein the data governance strategy comprises a governance scheme, a governance duration and a governance person;
and managing the enterprise data according to the treatment result.
Optionally, after determining the data governance policy corresponding to the abnormal data block and governing the abnormal data block according to the data governance policy to obtain a governance result, the method further includes:
acquiring a data management requirement corresponding to the abnormal data block;
and monitoring the treated abnormal data block again according to the data treatment requirement corresponding to the abnormal data block to obtain a secondary monitoring result.
Optionally, after monitoring the abnormal data block after being treated again according to the data treatment requirement corresponding to the abnormal data block and obtaining a secondary monitoring result, the method further includes:
evaluating the re-monitoring result according to the data management requirement corresponding to the abnormal data block to generate an evaluation result;
when the evaluation result is detected to be qualified, the treatment result is sent to a user side; or
And when the evaluation result is detected to be unqualified, treating the abnormal data block again based on the data treatment strategy.
Optionally, the monitoring each data block according to a preset data governance requirement to obtain a monitoring result corresponding to each data block includes:
judging whether the data storage time limit in the data block exceeds a first preset time length or not for each data block, and determining that the monitoring result of the data block is abnormal data in the data block when the data storage time limit in the data block exceeds the first preset time length;
or, judging whether the data storage form in the data block conforms to a storage specification, and when the data storage form in the data block does not conform to the storage specification, determining that the monitoring result of the data block is that abnormal data exists in the data block;
or, judging whether the data in the data block is not accessed within a second preset time length, and when the data in the data block is not accessed within the second preset time length, determining that the monitoring result of the data block is that abnormal data exists in the data block.
Optionally, the determining, according to the monitoring result corresponding to each data block, an abnormal data block in the plurality of data blocks includes:
and for each data block, when the abnormal data exists in the monitoring result corresponding to the data block, determining that the data block with the abnormal data is the abnormal data block.
Optionally, the method further comprises: and recording the process of treating the abnormal data block to generate a data treatment log.
Optionally, after determining an abnormal data block in the plurality of data blocks according to the monitoring result corresponding to each data block, the method further includes:
and if the abnormal data block is detected to be in a preset white list, not treating the abnormal data block.
A second aspect of the embodiments of the present application provides a device for data management, including:
the dividing unit is used for dividing the acquired data to be treated into a plurality of data blocks, wherein the data to be treated comprises enterprise data;
the monitoring unit is used for monitoring each data block according to a preset data management requirement to obtain a monitoring result corresponding to each data block;
the first determining unit is used for determining abnormal data blocks in the plurality of data blocks according to the monitoring result corresponding to each data block;
the second determining unit is used for determining a data governance strategy corresponding to the abnormal data block and governs the abnormal data block according to the data governance strategy to obtain a governance result, wherein the data governance strategy comprises a governance scheme, a governance duration and a governance person;
and the management unit is used for managing the enterprise data according to the treatment result.
A third aspect of the embodiments of the present application provides a data governance device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the data governance method as described in the first aspect above.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the method for data governance as described in the first aspect above.
A fifth aspect of embodiments of the present application provides a computer program product, which, when run on a data governance device, causes the device to perform the steps of the data governance method of the first aspect described above.
The method, the device, the equipment and the storage medium for data governance provided by the embodiment of the application have the following beneficial effects:
dividing the acquired data to be treated into a plurality of data blocks, wherein the data to be treated comprises enterprise data; monitoring each data block according to a preset data management requirement to obtain a monitoring result corresponding to each data block; determining abnormal data blocks in the plurality of data blocks according to the monitoring result corresponding to each data block; determining a data management strategy corresponding to the abnormal data block, and managing the abnormal data block according to the data management strategy to obtain a management result, wherein the data management strategy comprises a management scheme, management time and a management person; and managing enterprise data according to the treatment result. In the scheme, each data block is monitored according to the preset data governance requirements, and when one data block is monitored to be abnormal, the abnormal data block is governed according to different data governance strategies. The data management strategy comprises a management scheme, management time and management persons corresponding to the abnormal data blocks, namely, responsible persons corresponding to the abnormal data blocks are determined, the determined management scheme is given, the management time is limited, and the corresponding management persons can reasonably and effectively manage the abnormal data blocks in a reasonable time according to the management scheme. The abnormal data blocks are ensured to be truly managed, the data management effect is improved, the data value and the data quality are improved, and the data management really forms a closed loop.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart diagram of a method for data governance provided by an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram of a method of data governance provided by another embodiment of the present invention;
FIG. 3 is a schematic flow chart diagram of a method of data governance provided by yet another embodiment of the present invention;
FIG. 4 is a schematic diagram of an apparatus for data governance as provided by an embodiment of the present application;
FIG. 5 is a schematic diagram of a data governance device according to another embodiment of the present application. .
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In the description of the embodiments of the present application, "/" means "or" unless otherwise specified, for example, a/B may mean a or B; "and/or" herein is merely an association describing an associated object, and means that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality" means two or more than two.
In the following, the terms "first", "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present embodiment, "a plurality" means two or more unless otherwise specified.
Data Governance (Data Governance) is a whole set of management activities in an organization that involve the use of Data. Initiated and enforced by enterprise data governance departments is a series of policies and procedures on how to formulate and enforce business applications and technical management for data throughout the enterprise. The final goal of data management is to improve the value of data, the data management is very necessary, and the data management is the basis for realizing digital strategy of enterprises, and is a management system comprising organization, system, process and tools.
In recent years, data governance has become a topic commonly spoken by the old, and the industry has deeper or shallower researches on model building, data maps, data quality, enterprise data value and the like. However, most of the conventional data management methods only pay attention to how to make rules and standards for data management, but the problems of how to manage data, how to manage results and the like in the data management process are not solved, and full-link automatic tracking cannot be achieved. Therefore, the existing data management process is not perfect, and a closed-loop data management system which tends to be stable is not formed so far, so that the data management effect is poor.
In view of this, the present application provides a data governance method, which divides acquired data to be governed into a plurality of data blocks, where the data to be governed includes enterprise data; monitoring each data block according to a preset data management requirement to obtain a monitoring result corresponding to each data block; determining abnormal data blocks in the plurality of data blocks according to the monitoring result corresponding to each data block; determining a data management strategy corresponding to the abnormal data block, and managing the abnormal data block according to the data management strategy to obtain a management result, wherein the data management strategy comprises a management scheme, management time and a management person; and managing enterprise data according to the treatment result. In the scheme, each data block is monitored according to the preset data governance requirements, and when one data block is monitored to be abnormal, the abnormal data block is governed according to different data governance strategies. The data management strategy comprises a management scheme, management time and management persons corresponding to the abnormal data blocks, namely, responsible persons corresponding to the abnormal data blocks are determined, the determined management scheme is given, the management time is limited, and the corresponding management persons can reasonably and effectively manage the abnormal data blocks in a reasonable time according to the management scheme. The abnormal data blocks are ensured to be truly managed, the data management effect is improved, the data value and the data quality are improved, and the data management really forms a closed loop.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for data governance provided in an embodiment of the present application. The main execution body of the data governance method in this embodiment is a device for data governance, and the device includes, but is not limited to, a Domain Name Server (DNS), an independent Server, a distributed Server, a Server cluster or a cloud Server, and the like. The method for data governance as shown in fig. 1 may include S101 to S105, and the specific implementation principle of each step is as follows.
S101: dividing the acquired data to be treated into a plurality of data blocks, wherein the data to be treated comprises enterprise data.
The data to be remediated may include enterprise data and personal data of the user. The enterprise data refers to any data related to the enterprise and taking a file as a carrier. The enterprise data may be data of any enterprise, for example, the enterprise data may include mobile operator data, Unicom operator data, telecom operator data, various securities class data, stock data, fund data, insurance data, internal data of any enterprise (e.g., employee information, data generated by operating business), banking data, e-commerce data, and the like. The personal data of the user refers to various types of file-carried data generated by the user, for example, the personal data of the user may include basic information of the user (such as the name, age, height, contact telephone, address, identification number, etc. of the user), photos, videos, edited documents, written novels, etc. The description is given for illustrative purposes only and is not intended to be limiting.
Illustratively, the data to be remediated may be obtained from a database. And uniformly storing the enterprise data which needs to be subjected to data governance and the personal data of each user into a database in advance. When data management needs to be performed on a certain enterprise or a user, enterprise data corresponding to the enterprise is selected from the database correspondingly, or personal data corresponding to the user is selected from the database. For example, mobile operator data, fund data, etc. are selected in a database. The description is given for illustrative purposes only and is not intended to be limiting.
Alternatively, in a possible implementation, an open data set may also be obtained from a data mart (data market) as the data to be remediated. Or using the data set collected in the network as the data to be treated. The description is given for illustrative purposes only and is not intended to be limiting.
And dividing the acquired data to be treated into a plurality of data blocks according to a preset division rule. And different data to be treated correspond to different preset division rules. Illustratively, different preset division rules are set in advance according to different enterprises, and the enterprise data is divided according to the different preset division rules. For example, the data to be managed of a certain company may be divided according to the department category of the company, the data to be managed of the company may be divided according to the business operated by the company, the data to be managed of the company may be divided according to the job of the employee of the company, and the like.
Illustratively, when the preset division rule is division according to department categories, after data to be managed is acquired, data belonging to the same department in the data to be managed is searched, the data of the same department is placed in a folder, and the folder and the data contained in the folder form a data block. And dividing the data to be processed to obtain a plurality of data blocks.
Similarly, the personal data of the user is also divided according to different preset division rules. For example, the preset division rule is that the basic information is a kind of data, the image information is a kind of data, and the document information is a kind of data. After personal data are obtained, basic information in the personal data are extracted, the basic information is placed in a folder, and the folder and the basic information contained in the folder form a data block. Image information is extracted and placed in a folder, the folder and the image information contained in the folder constituting a data block. Extracting document information, and placing the document information in a folder, wherein the folder and the document information contained in the folder form a data block. The description is given for illustrative purposes only and is not intended to be limiting.
S102: and monitoring each data block according to a preset data management requirement to obtain a monitoring result corresponding to each data block.
Because the data governance requirements of each enterprise and each user are different, different data governance requirements can be made in advance according to the requirements of different enterprises and different users. Specifically, data governance requirements can be respectively formulated for a plurality of data blocks corresponding to data to be governed of different enterprises and different users. It can be understood that the data governance requirements corresponding to different data blocks may be the same or different, and are set according to actual situations, which are not limited.
Monitoring a plurality of data blocks obtained by dividing enterprise data of an enterprise according to a preset data governance requirement corresponding to the enterprise, and obtaining a monitoring result corresponding to each data block. Or monitoring a plurality of data blocks obtained by dividing personal data of a certain user according to a preset data management requirement corresponding to the user to obtain a monitoring result corresponding to each data block. The monitoring result corresponding to the data block may include that the data block is abnormal or that the data block is normal.
For example, the predetermined data governance requirements may include a plurality of governance rules, each of which may correspond to one or more data blocks. Specifically, the governance rules may include whether a data storage deadline in a data block exceeds a first preset duration (e.g., the data storage deadline in the data block exceeds 3 years), whether a data storage form in the data block meets a storage specification (e.g., whether the data may include numbers, english, special symbols, etc., whether the data storage form is in a standard picture format, a standard text format, a standard audio format, etc.), whether the data in the data block is not accessed within a second preset duration (e.g., the data in the data block is not accessed within three months), whether small files in the data block exceeds a preset number (e.g., the number of small files in the data block exceeds 100), whether the data block has no new data added within a third preset duration, whether the data block has only new data added within a fourth preset duration and no old data added, whether the data in the data block has old data added, and the data in the data block is stored in the data block in the data in the, Whether the data block has neither new data added nor old data accessed within a fifth preset time period, whether the quality of data in the data block meets an expected standard, whether the data in the data block is called out, whether the data in the data block is scheduled to occupy an exception (e.g., the schedule occupies more than 60 minutes), whether the number of times the data in the data block is consulted meets a standard, whether the data in the data block is safe (e.g., whether the level of data being encrypted meets a standard), whether the data in the data block fluctuates normally (e.g., the data is suddenly stored in large quantities or taken out in large quantities), and the like. It can be understood that, the first preset time period, the second preset time period, the third preset time period, the fourth preset time period and the fifth preset time period are all set by the user according to different data to be treated, and the setting is not limited.
Illustratively, for each data block, judging whether the data storage deadline in the data block exceeds a first preset time length, and when the data storage deadline in the data block exceeds the first preset time length, determining that the monitoring result of the data block is that abnormal data exists in the data block; and when all the data storage time limits in the data block exceed the first preset time limit, determining that the monitoring result of the data block is that the data in the data block are normal.
Or, judging whether the data storage form in the data block conforms to the storage specification, and when the data storage form in the data block does not conform to the storage specification, determining that the monitoring result of the data block is that abnormal data exists in the data block; and when the data storage forms in the data block all accord with the storage specification, determining that the monitoring result of the data block is that the data in the data block are all normal.
Or, judging whether the data in the data block is not accessed within a second preset time length, and when the data in the data block is not accessed within the second preset time length, determining that the monitoring result of the data block is that abnormal data exists in the data block; and when the data in the data block are accessed within a second preset time period, determining that the data in the data block are all normal according to the monitoring result of the data block.
For ease of understanding, this is exemplified herein. When the preset division rule is that division is carried out according to the business of operation, after the data to be managed is obtained, the data belonging to the same business in the data to be managed is searched, the data of the same business is placed in a folder, and the folder and the data contained in the folder form a data block. After the data to be processed is divided, a plurality of data blocks belonging to different services can be obtained, namely a data block A, a data block B, a data block C and a data block D.
The multiple governing rules for acquiring the data governing requirement preset for the data block a are respectively as follows: whether the data in the data block has not been accessed within the second predetermined time period and whether the data block has neither new data added nor old data accessed within the fifth predetermined time period. Each piece of data in the data block a is monitored according to the two governing rules, and specifically, whether each piece of data in the data block a meets the conditions listed by the two governing rules is judged. If a certain piece of data meets the conditions listed by the two governing rules or meets any one of the conditions listed by the two governing rules, the data is judged to be abnormal or abnormal data exists in the data block A. And when the condition that each piece of data in the data block A meets the condition listed by the corresponding treatment rule is detected, judging that the data in the data block A are normal.
The multiple governing rules for acquiring the data governing requirement preset for the data block B are respectively: whether the number of times of data in the data block being consulted reaches the standard or not and whether the data in the data block is safe or not. Each piece of data in the data block B is monitored according to the two governing rules, and specifically, whether each piece of data in the data block B belongs to the conditions listed by the two governing rules is judged. If a certain piece of data meets the conditions listed by the two governing rules or meets any one of the conditions listed by the two governing rules, the abnormal data in the data block B is judged to exist. And when the condition that each piece of data in the data block B meets the condition listed by the corresponding governing rule is detected, judging that the data in the data block B are normal. Data block C and data block D are similar to each other, and are not described herein again.
Illustratively, personal data of a user is divided according to a preset dividing rule, and a data block E (for example, data in the data block E is basic information of the user) and a data block F (for example, data in the data block F is image information of the user) are obtained. The multiple governing rules for acquiring the data governing requirements formulated for the data block E in advance are respectively: whether age is represented using numbers, whether contact is a mobile or landline number, whether the address is complete (e.g., whether the address includes province, city, district, street, house number, etc.). And judging whether each piece of data in the data block E meets the conditions listed by the corresponding governing rule. And if a certain piece of data does not accord with the conditions listed by the corresponding treatment rule, judging that abnormal data exists in the data block E. And when the condition that each piece of data in the data block F meets the condition listed by the corresponding treatment rule is detected, judging that the data in the data block E is normal. The data block F is similar to the above, and is not described in detail here.
Optionally, each abnormal data can be marked abnormally, so that the abnormal data can be managed in a subsequent targeted manner, and the data management speed is increased laterally.
Optionally, each data block includes multiple pieces of data, and each piece of data in each data block is monitored according to multiple governing rules to obtain a monitoring result corresponding to each piece of data in each data block. The monitoring result corresponding to each piece of data may include that the piece of data is abnormal or that the piece of data is normal.
In the embodiment, the data is judged according to the treatment rule corresponding to each piece of data, so that whether the current state of the data is normal or abnormal can be accurately monitored, the abnormal data can be subjected to data treatment in a subsequent targeted manner, and the accuracy and the effectiveness of the data treatment are improved.
S103: and determining abnormal data blocks in the plurality of data blocks according to the monitoring result corresponding to each data block.
The monitoring result corresponding to the data block may include that abnormal data exists in the data block or that the data in the data block is normal. And determining abnormal data blocks in a plurality of data blocks corresponding to the data to be treated according to the monitoring result corresponding to each data block. For example, if the monitoring result corresponding to a certain data block indicates that abnormal data exists in the data block, the data block is marked as an abnormal data block. Optionally, the data block marked as the abnormal data block is extracted, so that the abnormal data block can be subjected to data governance in the following process.
And for each data block, when abnormal data exists in the monitoring result corresponding to the data block, judging that the data block with the abnormal data is an abnormal data block. Illustratively, each data block contains a plurality of pieces of data, each piece of data corresponds to one monitoring result, and when any piece of data in the data block is abnormal, the data block is judged to be an abnormal data block. And when a plurality of pieces of data in the data block are abnormal, judging the data block as an abnormal data block. It can also be understood that if there is an exception to a piece of data, the data block is determined to be an abnormal data block. The above determination is performed for each data block, and an abnormal data block among the plurality of data blocks can be determined. One or more abnormal data blocks in the plurality of data blocks may be determined according to actual conditions, and the determination is not limited to this.
In the above embodiment, whether the data block is an abnormal data block is determined according to the monitoring result corresponding to each piece of data. If abnormal data exists in the data block, the data block is judged to be an abnormal data block. The method and the device realize accurate monitoring of the data to be managed, facilitate follow-up targeted data management of abnormal data, and improve accuracy and effectiveness of data management.
S104: and determining a data governance strategy corresponding to the abnormal data block, and governing the abnormal data block according to the data governance strategy to obtain a governance result, wherein the data governance strategy comprises a governance scheme, a governance duration and a governance person.
When different data governance requirements are made in advance according to the requirements of different enterprises and different users, different data governance strategies are made for different data to be governed and stored in a database. Specifically, a data governance strategy corresponding to each data block is established for a plurality of data blocks corresponding to data to be governed. It can be understood that the data governance policies corresponding to different data blocks may be the same or different, and are set according to actual situations, which are not limited.
Each data governance strategy comprises a governance scheme, governance duration and governance people corresponding to the data block. The management scheme is used for recovering the abnormal data in the abnormal data block to be normal. The administration duration refers to a time that is set in advance to be taken to solve such an abnormality. The treating person refers to a person who is set in advance to solve the abnormality. It should be noted that the administrator is also a predetermined person responsible for managing the data block. Optionally, when the data to be treated is divided into a plurality of data blocks, a corresponding treating person is set for each data block, and the treating person is responsible for managing the data blocks in the whole process. The responsible person can add, delete, adjust, handle the exception and so on to the data block. The description is given for illustrative purposes only and is not intended to be limiting.
Illustratively, a corresponding identifier and a data governance strategy corresponding to the identifier are preset for each data block, and are stored in the database in an associated manner. And when the abnormal data block is determined, acquiring an identifier corresponding to the abnormal data block, and searching a data management strategy associated with the identifier in the database according to the identifier. And informing a governing person to adopt the governing scheme to implement governing on the abnormal data block within the governing time length to obtain a governing result corresponding to the abnormal data block. The treatment result includes a data block obtained by adjusting the abnormal data, that is, after the abnormal data in the abnormal data block is adjusted, a new data block is generated based on the adjusted data, and the new data block is the treatment result corresponding to the abnormal data block.
For example, the specific exception of a certain exception data block is monitored as follows: the data storage period exceeds 3 years, and the data governance strategy corresponding to the abnormal data block is as follows: and informing a governance person A to delete or move the data with the storage period exceeding 3 years in the abnormal data block into a preset historical library within the range of governance time length a. In this example, if the data storage period is too long, the occupied memory is too large, and it may cause a slow query speed when querying new data in the database where the data is located, so that the data needs to be managed. The data with the overlong storage period is deleted or moved into the independent historical library, so that the occupation of the data on the memory of the current database can be reduced, and the speed is improved to a certain extent when other data in the database are inquired subsequently. And the data are not lost, and the data can be inquired in the history library subsequently after being moved into the history library.
For example, the specific exception of a certain exception data block is monitored as follows: the number of the small files in the data block exceeds 100, and the data governance strategy corresponding to the abnormal data block is as follows: and informing a governing person B to combine a plurality of small files in the abnormal data block into one file or delete useless and empty small files within the range of governing duration B. In this example, in the case of an excessive number of small files, it is inconvenient to manage these data, and when these data are queried, the query time is increased. Therefore, the data needs to be managed, the managed data is convenient to manage, and the query time is reduced.
For example, the specific exception of a certain exception data block is monitored as follows: the data scheduling in the data block takes more than 60 minutes, and the data management strategy corresponding to the abnormal data block is as follows: and the admin C deletes redundant data or adjusts and calls codes within the scope of the administration time length C. For example, the specific exception of a certain exception data block is monitored as follows: the data in the data block is not accessed within three months, and the data governance strategy corresponding to the abnormal data block is as follows: and (4) deleting or storing the data which is not accessed within three months into a historical library by the treating person D within the range of the treating time length D. And treating the unit data according to the data treatment strategy. For example, the specific exception of a certain exception data block is monitored as follows: the data block has no new data to be added or old data to be accessed within half a year, and the data governance strategy corresponding to the abnormal data block is as follows: and the admin person E deletes the data in the abnormal data block within the scope of the admin time length E. And treating each abnormal data block according to the data treatment strategy corresponding to each abnormal data block. It should be noted that, in the process of treating the abnormal data block, a treating person is fully responsible for the treatment process of the abnormal data block, and the specific treatment mode is also treated by the data treatment equipment, and the abnormal data is not manually deleted by the treating person. The description is given for illustrative purposes only and is not intended to be limiting.
Illustratively, following the above example in S102, the data in the data block E is the basic information of the user, and the data governance requirement corresponding to the data block E includes: whether age is represented using numbers, whether contact is a mobile or landline number, whether the address is complete (e.g., whether the address includes province, city, district, street, house number, etc.). When the data block E is determined to be an abnormal data block, a certain age in the data block E is represented by Chinese characters, and the data governance strategy corresponding to the piece of data is as follows: and the admin person F modifies the Chinese characters representing the age into numbers within the scope of the administration duration F. Specifically, the admin F is informed that a certain piece of data in the data block E is abnormal, the abnormality is embodied in the way that the age in the data is represented by Chinese characters, and the representation mode of the age is automatically modified into numbers based on equipment.
If the address in a certain piece of data does not fill in the area name, the data management strategy corresponding to the piece of data is as follows: and the admin G completely supplements the address corresponding to the data within the scope of the administration time length G. Specifically, the administering person G is notified that a certain piece of data in the data block E is abnormal, which is specifically represented by that the area name is not filled in the address in the piece of data, and the current address information is acquired, the area name is deduced according to the current address information, and the address is completely supplemented according to the deduced area name. For example, a certain address is a long-safe-road street meadow slope in the city of xi province of Shaanxi province, the current area name can be inferred to be a tombstone area according to the current address information, the address is supplemented, and the supplemented address information is the long-safe-road street meadow slope in the tombstone area in the city of xi province of Shaanxi province. The description is given for illustrative purposes only and is not intended to be limiting.
Optionally, in a possible implementation manner, the data governance policy corresponding to the abnormal data block may be sent to a specific governance person in a manner that the system dispatches a work order. For example, the data governance strategy corresponding to the abnormal data block is sent to a system of a governance person, and the governance person can log in the system to check the specific data governance strategy and reasonably arrange data governance work. The abnormal data blocks can be managed by the management personnel, and the management effect of the abnormal data blocks can be improved by the implementation mode.
S105: and managing enterprise data according to the treatment result.
Each data block in the treatment result is a data block after the abnormal data block is treated, each piece of data in the treated data block is accurate, standard and concise, a user can conveniently know the current condition of an enterprise according to the data, and the enterprise data are conveniently managed. And the method does not occupy excessive memory, and improves the speed of data query.
In the scheme, each data block is monitored according to the preset data governance requirements, and when one data block is monitored to be abnormal, the abnormal data block is governed according to different data governance strategies. The data management strategy comprises a management scheme, management time and management persons corresponding to the abnormal data blocks, namely, responsible persons corresponding to the abnormal data blocks are determined, the determined management scheme is given, the management time is limited, and the corresponding management persons can reasonably and effectively manage the abnormal data blocks in a reasonable time according to the management scheme. The abnormal data blocks are ensured to be truly managed, the data management effect is improved, the data value and the data quality are improved, and the data management really forms a closed loop.
Referring to fig. 2, fig. 2 is a schematic flow chart of a data governance method according to another embodiment of the present invention. The difference between the present embodiment and the embodiment corresponding to fig. 1 is S205 to S206, where S201 to S204 in the present embodiment are completely the same as S101 to S104 in the previous embodiment, and reference is specifically made to the related description of S101 to S104 and S105 in the previous embodiment, which is not repeated herein.
S205: and acquiring a data management requirement corresponding to the abnormal data block.
And after the data blocks determined as the abnormal data blocks are treated, acquiring the data treatment requirements corresponding to the abnormal data blocks again. The data governance requirement is the same as the data governance requirement when determining whether the data block is an abnormal data block. For example, the data governance requirement corresponding to the data block a is a, and the data block a is monitored based on the data governance requirement a to obtain a monitoring result corresponding to the data block a; and determining the data block A as an abnormal data block according to the monitoring result, acquiring a data management strategy corresponding to the abnormal data block, and after the abnormal data block is managed according to the data management strategy, acquiring a data management requirement corresponding to the abnormal data block again, wherein the data management requirement is also a data management requirement a.
S206: and monitoring the treated abnormal data block again according to the data treatment requirement corresponding to the abnormal data block to obtain a secondary monitoring result.
And monitoring the treated abnormal data block again according to the obtained data treatment requirement, wherein the obtained secondary monitoring result can comprise that the treated abnormal data block is abnormal or the treated abnormal data block is normal.
The specific monitoring manner is the same as the manner of monitoring the starting database in S102, and only the monitored object is changed, which is only briefly described here. For example, the data block a is monitored by the data governance requirement a, and at this time, the governed abnormal data block is monitored again by the data governance requirement a, so as to obtain a re-monitoring result corresponding to the governed abnormal data block.
In the scheme, the abnormal data block after being treated is monitored again, so that whether the effect of the first-time data treatment is good or not can be further ensured. If the result of monitoring again is that the abnormal data block after the treatment is normal, the effect of the first data treatment is proved to be very good; if the result of secondary monitoring is that the abnormal data block after treatment is abnormal, the primary data treatment is proved to be incomplete, the abnormal data block still has problems, at the moment, a person who treats the abnormal data block can be traced, and the person who treats the abnormal data block can be supervised to treat the abnormal data block again until the abnormal data block is normal. The mode of monitoring once more can further ensure that the state of the data block is normal, and each data management strategy has a specific management scheme, long management time and a management person, can accurately trace responsibility, and can supervise the management person to timely manage the abnormal data block.
Referring to fig. 3, fig. 3 is a schematic flow chart of a data governance method according to another embodiment of the present invention. The difference between the embodiment of the present embodiment and the embodiment corresponding to fig. 2 is S307 to S309, where S301 to S206 in the present embodiment are completely the same as S201 to S106 in the previous embodiment, and reference is specifically made to the related description of S201 to S106 in the previous embodiment, which is not repeated herein. It should be noted that, in parallel with S308 and S309, the execution 308 is not executed after S309, and the specific execution manner is not limited to practice. S307 to S309 are specifically as follows:
s307: and evaluating the re-monitoring result according to the data management requirement corresponding to the abnormal data block to generate an evaluation result.
The monitoring result again can comprise that the abnormal data block after treatment is abnormal or the abnormal data block after treatment is normal. And acquiring a plurality of treatment rules in the data treatment requirements corresponding to the abnormal data block, judging each piece of data in the treated abnormal data block one by one based on the treatment rules, and ensuring whether the monitoring result is accurate again. And if one or more data are abnormal in the abnormal data block after treatment, judging whether the abnormal data block after treatment is abnormal or not, and at the moment, judging that the result of the secondary monitoring result evaluation is unqualified. That is, if the monitoring result corresponding to the abnormal data block after treatment is that the abnormal data block after treatment is abnormal, the evaluation result corresponding to the re-monitoring result is unqualified. The data treatment is proved to be incomplete, and the treated abnormal data blocks need to be treated again.
And if the treated abnormal data block has no data abnormality, judging that the treated abnormal data block is normal, and judging that the result of the secondary monitoring result evaluation is qualified. That is, if the monitored result corresponding to the abnormal data block after treatment is that the abnormal data block after treatment is normal, the evaluation result corresponding to the re-monitored result is qualified. The data is proved to be thoroughly treated, and the treated abnormal data block does not need to be treated again.
S308: and when the evaluation result is detected to be qualified, the treatment result is sent to the user side.
The user side refers to the side of the admin. And when the evaluation result is detected to be qualified, the treatment result can be sent to the terminal equipment of the treating person. Besides the data block after treatment, the treatment result can also comprise treatment result prompt information, and the prompt information prompts a treatment person by a user, so that the abnormal data block is successfully treated.
S309: and when the evaluation result is detected to be unqualified, treating the abnormal data block again based on the data treatment strategy.
And when the evaluation result is detected to be unqualified, the data governance strategy in the acquisition 304 is used for again governance of the governed abnormal data block. The specific governing process is the same as the process of governing the started abnormal data block in S104, and is not described here again.
Optionally, after the second treatment, the abnormal data block after the second treatment may be monitored again based on the corresponding data treatment requirement, and if the result of the second monitoring is still abnormal, the treatment and the monitoring are continued until the abnormal data block returns to normal.
In the embodiment, the data blocks which are still abnormal after being treated are treated again, so that the abnormal data blocks can be ensured to be recovered to be normal, the whole process of data treatment is perfected, the condition of incomplete treatment is avoided, and the effect of data treatment is improved.
Optionally, in a possible implementation manner, a process of treating the abnormal data block may be recorded, and a data treatment log is generated. Illustratively, a data governance request corresponding to the abnormal data block, a state (normal or abnormal) corresponding to each piece of data in the abnormal data block, a data governance strategy corresponding to the abnormal data block, a number of governance, a governance result of each governance, a governance duration used by each governance, and the like can be recorded, and a data governance log corresponding to the abnormal data block is generated based on the information. The data management log can be convenient for other users in the future to check the management condition of the abnormal data block and know what kind of problem the abnormal data block is easy to generate, follow-up things can be avoided as much as possible, and also is convenient for knowing what kind of management scheme the abnormal data block adopts and the management time length, and when similar abnormality occurs in other follow-up data blocks, new data management strategy formulation can be carried out by referring to the data management strategy corresponding to the abnormal data block.
Optionally, in a possible implementation manner, if it is detected that the abnormal data block is in a preset white list, the abnormal data block is not treated. For example, for some abnormal data blocks which cannot be currently treated, a corresponding white list may be preset, and if it is detected that the abnormal data block is in the preset white list, the abnormal data block may not be treated. For example, for some abnormal data block anomalies, the current technology cannot solve the data block anomalies, or the time for solving the anomalies needs to be long, the cost is not cost-effective, but the current anomalies do not affect the use, the abnormal data blocks can be added into a white list, and the abnormal data blocks are not treated. Therefore, certain economic cost can be saved, and too much manpower and material resources are prevented from being wasted in unimportant data management.
Optionally, in a possible implementation manner, some data blocks may be added to the white list, and for the data blocks added to the white list, the data blocks are no longer monitored based on the preset data governance requirements. It should be noted that the white list in this example is different from the above-mentioned white list, and the above-mentioned white list does not treat the abnormal data blocks in the white list, and the white list in this example does not monitor the data blocks in the white list.
Referring to fig. 4, fig. 4 is a schematic diagram of a data management apparatus according to an embodiment of the present application. The device comprises units for performing the steps in the embodiments corresponding to fig. 1, 2, 3. Please refer to the related descriptions in the corresponding embodiments of fig. 1, fig. 2, and fig. 3. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 4, including:
the dividing unit 410 is configured to divide the acquired data to be treated into a plurality of data blocks, where the data to be treated includes enterprise data;
the monitoring unit 420 is configured to monitor each data block according to a preset data management requirement, and obtain a monitoring result corresponding to each data block;
a first determining unit 430, configured to determine, according to a monitoring result corresponding to each data block, an abnormal data block in the multiple data blocks;
a second determining unit 440, configured to determine a data governance policy corresponding to the abnormal data block, and governs the abnormal data block according to the data governance policy to obtain a governance result, where the data governance policy includes a governance scheme, a governance duration, and a governance person;
and the management unit 450 is configured to manage the enterprise data according to the governance result.
Optionally, the apparatus further comprises:
the acquisition unit is used for acquiring the data management requirements corresponding to the abnormal data blocks;
and the secondary monitoring unit is used for monitoring the treated abnormal data block again according to the data treatment requirement corresponding to the abnormal data block to obtain a secondary monitoring result.
Optionally, the apparatus further comprises:
the evaluation unit is used for evaluating the re-monitoring result according to the data management requirement corresponding to the abnormal data block to generate an evaluation result;
the first detection unit is used for sending the treatment result to a user side when the evaluation result is detected to be qualified; or
And the second detection unit is used for treating the abnormal data block again based on the data treatment strategy when the evaluation result is detected to be unqualified.
Optionally, the monitoring unit is specifically configured to:
judging whether the data storage time limit in the data block exceeds a first preset time length or not for each data block, and determining that the monitoring result of the data block is abnormal data in the data block when the data storage time limit in the data block exceeds the first preset time length;
or, judging whether the data storage form in the data block conforms to a storage specification, and when the data storage form in the data block does not conform to the storage specification, determining that the monitoring result of the data block is that abnormal data exists in the data block;
or, judging whether the data in the data block is not accessed within a second preset time length, and when the data in the data block is not accessed within the second preset time length, determining that the monitoring result of the data block is that abnormal data exists in the data block.
Optionally, the first determining unit 430 is specifically configured to:
and for each data block, when the abnormal data exists in the monitoring result corresponding to the data block, determining that the data block with the abnormal data is the abnormal data block.
Optionally, the apparatus further comprises:
and the generating unit is used for recording the process of treating the abnormal data block and generating a data treatment log.
Optionally, the apparatus further comprises:
and the third detection unit is used for not treating the abnormal data block if the abnormal data block is detected to be in a preset white list.
Referring to fig. 5, fig. 5 is a schematic diagram of a data governance device according to another embodiment of the present application. As shown in fig. 5, the data abatement apparatus 5 of this embodiment includes: a processor 50, a memory 51, and computer instructions 52 stored in said memory 51 and executable on said processor 50. The processor 50, when executing the computer instructions 52, implements the steps in the various data governance method embodiments described above, such as S101-S105 shown in fig. 1. Alternatively, the processor 50, when executing the computer instructions 52, implements the functions of the units in the above embodiments, such as the units 410 to 450 shown in fig. 4.
Illustratively, the computer instructions 52 may be divided into one or more units, which are stored in the memory 51 and executed by the processor 50 to accomplish the present application. The one or more elements may be a series of computer instruction segments capable of performing specific functions, which are used to describe the execution of the computer instructions 52 in the data governance device 5. For example, the computer instructions 52 may be divided into a dividing unit, a monitoring unit, a first determining unit, a second determining unit, and a managing unit, each of which functions as described above.
The data governance device may include, but is not limited to, a processor 50, a memory 51. Those skilled in the art will appreciate that FIG. 5 is merely an example of a data governance device 5, and does not constitute a limitation of a data governance device, and may include more or fewer components than shown, or some components in combination, or different components, e.g., the data governance device may also include input output devices, network access devices, buses, etc.
The Processor 50 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 51 may be an internal storage unit of the data governance device, such as a hard disk or a memory of the data governance device. The memory 51 may also be an external storage terminal of the data governance device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are equipped on the data governance device. Further, the memory 51 may also include both an internal storage unit and an external storage terminal of the data governance device. The memory 51 is used for storing the computer instructions and other programs and data required by the terminal. The memory 51 may also be used to temporarily store data that has been output or is to be output.
The embodiment of the present application further provides a computer storage medium, where the computer storage medium may be nonvolatile or volatile, and the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the steps in the above-mentioned data governance method embodiments.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not cause the essential features of the corresponding technical solutions to depart from the spirit scope of the technical solutions of the embodiments of the present application, and are intended to be included within the scope of the present application.
Claims (10)
1. A method of data governance, comprising:
dividing the acquired data to be treated into a plurality of data blocks, wherein the data to be treated comprises enterprise data;
monitoring each data block according to a preset data management requirement to obtain a monitoring result corresponding to each data block;
determining abnormal data blocks in the plurality of data blocks according to the monitoring result corresponding to each data block;
determining a data governance strategy corresponding to the abnormal data block, and governing the abnormal data block according to the data governance strategy to obtain a governance result, wherein the data governance strategy comprises a governance scheme, a governance duration and a governance person;
and managing the enterprise data according to the treatment result.
2. The method of claim 1, wherein the determining a data governance policy corresponding to the abnormal data block and governs the abnormal data block according to the data governance policy to obtain a governance result further comprises:
acquiring a data management requirement corresponding to the abnormal data block;
and monitoring the treated abnormal data block again according to the data treatment requirement corresponding to the abnormal data block to obtain a secondary monitoring result.
3. The method of claim 2, wherein the method further comprises, after monitoring the abnormal data block after treatment again according to the data treatment requirement corresponding to the abnormal data block and obtaining a result of monitoring again:
evaluating the re-monitoring result according to the data management requirement corresponding to the abnormal data block to generate an evaluation result;
when the evaluation result is detected to be qualified, the treatment result is sent to a user side; or
And when the evaluation result is detected to be unqualified, treating the abnormal data block again based on the data treatment strategy.
4. The method of claim 1, wherein the monitoring each data block according to a preset data governance requirement to obtain a monitoring result corresponding to each data block comprises:
judging whether the data storage time limit in the data block exceeds a first preset time length or not for each data block, and determining that the monitoring result of the data block is abnormal data in the data block when the data storage time limit in the data block exceeds the first preset time length;
or, judging whether the data storage form in the data block conforms to a storage specification, and when the data storage form in the data block does not conform to the storage specification, determining that the monitoring result of the data block is that abnormal data exists in the data block;
or, judging whether the data in the data block is not accessed within a second preset time length, and when the data in the data block is not accessed within the second preset time length, determining that the monitoring result of the data block is that abnormal data exists in the data block.
5. The method of claim 4, wherein determining abnormal data blocks in the plurality of data blocks according to the monitoring result corresponding to each data block comprises:
and for each data block, when the abnormal data exists in the monitoring result corresponding to the data block, determining that the data block with the abnormal data is the abnormal data block.
6. The method of any of claims 1 to 5, further comprising: and recording the process of treating the abnormal data block to generate a data treatment log.
7. The method of claim 1, wherein after determining the abnormal data block in the plurality of data blocks according to the monitoring result corresponding to each data block, the method further comprises:
and if the abnormal data block is detected to be in a preset white list, not treating the abnormal data block.
8. An apparatus for data governance, comprising:
the dividing unit is used for dividing the acquired data to be treated into a plurality of data blocks, wherein the data to be treated comprises enterprise data;
the monitoring unit is used for monitoring each data block according to a preset data management requirement to obtain a monitoring result corresponding to each data block;
the first determining unit is used for determining abnormal data blocks in the plurality of data blocks according to the monitoring result corresponding to each data block;
the second determining unit is used for determining a data governance strategy corresponding to the abnormal data block and governs the abnormal data block according to the data governance strategy to obtain a governance result, wherein the data governance strategy comprises a governance scheme, a governance duration and a governance person;
and the management unit is used for managing the enterprise data according to the treatment result.
9. An apparatus for data governance comprising a memory, a processor and a computer program stored in said memory and executable on said processor, wherein said processor when executing said computer program implements a method according to any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109753499A (en) * | 2018-12-17 | 2019-05-14 | 云南电网有限责任公司信息中心 | A kind of O&M monitoring data administering method |
| CN110008208A (en) * | 2019-04-04 | 2019-07-12 | 北京易华录信息技术股份有限公司 | A kind of data administering method and system |
| CN111290916A (en) * | 2020-02-18 | 2020-06-16 | 深圳前海微众银行股份有限公司 | Big data monitoring method, device and equipment and computer readable storage medium |
| CN111639077A (en) * | 2020-05-15 | 2020-09-08 | 杭州数梦工场科技有限公司 | Data management method and device, electronic equipment and storage medium |
| CN111782433A (en) * | 2020-06-30 | 2020-10-16 | 北京百度网讯科技有限公司 | Exception troubleshooting method, device, electronic equipment and storage medium |
| CN112184491A (en) * | 2020-10-13 | 2021-01-05 | 南方电网数字电网研究院有限公司 | Abnormal data identification method and device in power grid, computer equipment and storage medium |
-
2021
- 2021-06-30 CN CN202110735359.3A patent/CN113342796A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109753499A (en) * | 2018-12-17 | 2019-05-14 | 云南电网有限责任公司信息中心 | A kind of O&M monitoring data administering method |
| CN110008208A (en) * | 2019-04-04 | 2019-07-12 | 北京易华录信息技术股份有限公司 | A kind of data administering method and system |
| CN111290916A (en) * | 2020-02-18 | 2020-06-16 | 深圳前海微众银行股份有限公司 | Big data monitoring method, device and equipment and computer readable storage medium |
| CN111639077A (en) * | 2020-05-15 | 2020-09-08 | 杭州数梦工场科技有限公司 | Data management method and device, electronic equipment and storage medium |
| CN111782433A (en) * | 2020-06-30 | 2020-10-16 | 北京百度网讯科技有限公司 | Exception troubleshooting method, device, electronic equipment and storage medium |
| CN112184491A (en) * | 2020-10-13 | 2021-01-05 | 南方电网数字电网研究院有限公司 | Abnormal data identification method and device in power grid, computer equipment and storage medium |
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