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Method and System for Automatic Prioritization of Data Exceptions Based On Classification, Sensitivity and Criticality of Data

IP.com Disclosure Number: IPCOM000243606D
Publication Date: 2015-Oct-05
Document File: 3 page(s) / 257K

Publishing Venue

The IP.com Prior Art Database

Abstract

A method and system is disclosed for automatic prioritization of data exceptions based on classification, sensitivity and criticality of data. Such prioritization of data enables remediating of exceptions in timely manner.

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This is the abbreviated version, containing approximately 51% of the total text.

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Method and System for Automatic Prioritization of Data Exceptions Based On Classification, Sensitivity and Criticality of Data

In enterprise data quality parlance an exception may arise if an instance of data does not follow/comply to a rule. Examples of types of exceptions include but not limited to data inconsistencies, missing values, incorrect or out of range values etc. Exceptions are generated when data in one or more data sources are validated using rules (data rules). Examples for data sources include but not limited to operational type databases, analytical type databases, flat files etc. In case of relational database type data sources, data rules are first bound to one or more columns before running them. Related exceptions, typically identified by running the same data rule using the same bindings, can be logically grouped to form exception sets. There exist products that can perform data validation and identify exceptions. Collectively these products that do validation and generate exceptions are considered as exception providers.

In order to monitor and help manage life cycle in remediation process, exception set includes details such as data rule applied, creation time, records processed, exception count, priority, owner, status, list of implemented data resources (IDRs) etc. An IDR

represents an information asset in a database or in a flat file. An information asset

might represent a specific column in a database along with its parent table, schema, database, host etc. or a specific field in a file along with its parent folder and so on all the way to host. In addition to IDR list itself, an exception provider may be able to furnish classification, sensitivity and criticality details for each of the IDRs included. Such information may also be obtained from other enterprise catalog systems.

Exception set are regularly monitored and subject to remediation, the process typically takes place according to internal business policies with a goal to cleanse the data. At a

high level, these processes typically include prioritizing, assigning an owner and actually remediating data. In order to facilitate monitoring, console system (DQEC) indexes exception sets and allows users to filter using full or partial text or faceted search on various key properties such as exception count, priority owner, status etc.

Disclosed is a method and system for automatic prioritization of data exceptions based on classification, sensitivity and criticality of data. Such prioritization of data enables remediating of exceptions in timely manner.

Depending upon an exception provider, exception set may or may not contain classification, sensitivity or criticality details along with its IDR list. Some e...