INTELLIGENT DATA QUALITY

• US 20200133929A1
• Filed: 12/23/2019
• Published: 04/30/2020
• Est. Priority Date: 05/29/2018
• Status: Active Application

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: YAMASHITA, Sabrina, MARTINES NETO, Armando, LIKHAR, Vivek, DA LUZ, Acyr
• Granted Patent: US 11,327,935 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 16/903 Querying for retrieval from the web G06F16/953
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Examples of an intelligent data quality application are defined. In an example, the system receives a data quality requirement from a user. The system obtains target data from a plurality of data sources. The system implements an artificial intelligence component sort the target data into a data cascade. The data cascade may include a plurality of attributes associated with the data quality requirement. The system may evaluate the data cascade to identify a data pattern model for each of the attributes. The system may implement a first cognitive learning operation to determine a mapping context from the data cascade and a conversion rule from the data pattern model. The system may establish a data harmonization model corresponding to the data quality requirement by performing a second cognitive learning operation. The system may generate a data cleansing result corresponding to the data quality requirement.

Claims

1. A system comprising:

a processor;

a data profiler coupled to the processor, the data profiler to implement an artificial intelligence component to;

sort target data associated with a data quality requirement relevant for data management operations into a data cascade, the data cascade to include a plurality of attributes pertaining to the target data, each of the attributes from the plurality of attributes being associated with the data quality requirement, wherein the data cascade includes information about an attribute from the plurality of attributes that is linked to another attribute from the plurality of attributes in a sequential manner; and

identify a combination of attributes from the plurality of attributes for generating a data pattern model, the combination including at least one attribute usable for generating the data pattern model;

a data mapper coupled to the processor, the data mapper to;

implement a first cognitive learning operation to;

determine at least one mapping context associated with the data quality requirement from the data cascade and the data pattern model, the mapping context to include a pattern value from the data pattern model and at least one attribute from the data cascade; and

determine a conversion rule from the data pattern model for each of the mapping context associated with the data quality requirement; and

a data cleanser coupled to the processor, the data cleanser to;

obtain the data pattern model for each attribute associated with the data quality requirement;

obtain the conversion rule determined for each of the mapping context associated with the data quality requirement;

establish a data harmonization model corresponding to the data quality requirement by performing a second cognitive learning operation on the obtained data pattern model domain and the obtained conversion rule; and

generate a data cleansing result corresponding to the data quality requirement, the data cleansing result comprising the data harmonization model relevant for addressing the data quality requirement.

2. The system as claimed in claim 1, wherein each of the attributes from the plurality of attributes includes a measurable factor, which defines a condition for processing the data quality requirement.

3. The system as claimed in claim 1, wherein the data pattern model includes a model for linking multiple datasets with each other, wherein one dataset leads to another, when a set of specified conditions defined by the data pattern model is fulfilled.

4. The system as claimed in claim 1, wherein the data profiler is to:

analyze at least one of a measurable factor pertinent to the data quality requirement and outlier data for processing the data quality requirement, the measurable factor being identified based on a plurality of sources used for gathering the target data; and

identify, based on the analysis, the at least one attribute from the data cascade usable for establishment of the data pattern model.

5. The system as claimed in claim 1, wherein the system is to further establish a profiling library, by associating the plurality of attributes included in the data cascade, the mapping context for each attribute, the conversion rule for each mapping context, and the data harmonization model with the data quality requirement.

6. The system as claimed in claim 1, wherein the data profiler is to update the target data acquired from a plurality of sources in real time to reflect an update made to a source from the plurality of sources in the target data.

7. A method comprising:

implementing, by a processor, an artificial intelligence component to;

sort target data associated with a data quality requirement relevant for data management operations into a data cascade, the data cascade to include a plurality of attributes pertaining to the target data, each of the attributes from the plurality of attributes being associated with the data quality requirement, wherein the data cascade includes information about an attribute from the plurality of attributes that is linked to another attribute from the plurality of attributes in a sequential manner; and

identify a combination of attributes from the plurality of attributes for generating a data pattern model, the combination including at least one attribute usable for generating the data pattern model;

implementing, by the processor, a first cognitive learning operation to;

determine at least one mapping context associated with the data quality requirement from the data cascade and the data pattern model, the mapping context to include a pattern value from the data pattern model and at least one attribute from the data cascade; and

determine a conversion rule from the data pattern model for each of the mapping context associated with the data quality requirement;

obtaining, by the processor, the data pattern model for each attribute associated with the data quality requirement;

obtaining, by the processor, the conversion rule determined for each of the mapping context associated with the data quality requirement;

establishing, by the processor, a data harmonization model corresponding to the data quality requirement by performing a second cognitive learning operation on the obtained data pattern model domain and the obtained conversion rule; and

generating, by the processor, a data cleansing result corresponding to the data quality requirement, the data cleansing result comprising the data harmonization model relevant for addressing the data quality requirement.

8. The method as claimed in claim 7, wherein each of the attributes from the plurality of attributes includes a measurable factor, which defines a condition for processing the data quality requirement.

9. The method as claimed in claim 7, wherein the data pattern model includes a model for linking multiple datasets with each other, wherein one dataset leads to another, when a set of specified conditions defined by the data pattern model is fulfilled.

10. The method as claimed in claim 7, wherein identifying the combination of attributes comprises:

analyzing at least one of a measurable factor pertinent to the data quality requirement and outlier data for processing the data quality requirement, the measurable factor being identified based on a plurality of sources used for gathering the target data; and

identifying, based on the analysis, the at least one attribute from the data cascade usable for establishment of the data pattern model.

11. The method as claimed in claim 7, wherein the method further comprises establishing a profiling library, by associating the plurality of attributes included in the data cascade, the mapping context for each attribute, the conversion rule for each mapping context, and the data harmonization model with the data quality requirement.

12. The method as claimed in claim 7, wherein the method further comprises updating the target data acquired from a plurality of sources in real time to reflect an update made to a source from the plurality of sources in the target data.

13. A non-transitory computer readable medium including machine readable instructions that are executable by a processor to:

implement an artificial intelligence component to;

sort target data associated with a data quality requirement relevant for data management operations into a data cascade, the data cascade to include a plurality of attributes pertaining to the target data, each of the attributes from the plurality of attributes being associated with the data quality requirement, wherein the data cascade includes information about an attribute from the plurality of attributes that is linked to another attribute from the plurality of attributes in a sequential manner; and

identify a combination of attributes from the plurality of attributes for generating a data pattern model, the combination including at least one attribute usable for generating the data pattern model;

implement a first cognitive learning operation to;

determine at least one mapping context associated with the data quality requirement from the data cascade and the data pattern model, the mapping context to include a pattern value from the data pattern model and at least one attribute from the data cascade; and

determine a conversion rule from the data pattern model for each of the mapping context associated with the data quality requirement;

obtain the data pattern model for each attribute associated with the data quality requirement;

obtain the conversion rule determined for each of the mapping context associated with the data quality requirement;

establish a data harmonization model corresponding to the data quality requirement by performing a second cognitive learning operation on the obtained data pattern model domain and the obtained conversion rule; and

generate a data cleansing result corresponding to the data quality requirement, the data cleansing result comprising the data harmonization model relevant for addressing the data quality requirement.

14. The non-transitory computer-readable medium of claim 13, wherein each of the attributes from the plurality of attributes includes a measurable factor, which defines a condition for processing the data quality requirement.

15. The non-transitory computer-readable medium of claim 13, wherein the data pattern model includes a model for linking multiple datasets with each other, wherein one dataset leads to another, when a set of specified conditions defined by the data pattern model is fulfilled.

16. The non-transitory computer-readable medium of claim 13, wherein to identify the combination of attributes, the processor is to:

analyze at least one of a measurable factor pertinent to the data quality requirement and outlier data for processing the data quality requirement, the measurable factor being identified based on a plurality of sources used for gathering the target data; and

identify, based on the analysis, the at least one attribute from the data cascade usable for establishment of the data pattern model

17. The non-transitory computer-readable medium of claim 13, wherein the processor is to further establish a profiling library, by associating the plurality of attributes included in the data cascade, the mapping context for each attribute, the conversion rule for each mapping context, and the data harmonization model with the data quality requirement.