Quality Overview

The Syniti Knowledge Platform’s Quality module uses rules and relationships to drive data quality initiatives across your organization, granting stakeholders access to key metrics about overall data health. Data Catalog, Migrate/ADMM, and other modules within the platform can integrate with Quality to monitor and evaluate business outcomes and to see how improvements resulting from data quality reporting have a positive impact on the business.

Data quality is driven by enforcements and implementations which are aligned by subject areas, business concepts and business processes, all driven by your business requirements as defined in your rules.

The diagram below shows the relationships among the key Knowledge Platform assets involved in data quality.

Subject Areas

A subject area provides a method for categorizing rules, allowing for easier management of rules and security profiling of rules by a company’s logical business structures. Every rule must be assigned to a subject area so that data health and business requirements can be evaluated by category.

Rules

Business users write system-agnostic rules to evaluate data health and measure whether business requirements are met. Rules can be used to evaluate data quality, document requirement compliance, indicate that a required manual task has been completed, or for many other purposes.

Technical users can further define the rule using the rule’s associated enforcements, added on the Enforcements tab, which includes the system where the rule is executed and specifications (i.e., technical requirements).

Rules can be associated with zero or more business processes and/or zero or more business concepts. Assigning rules to a business process or business concept allows the average data quality score of the latest run of all associated implementations to be rolled up to that asset.

Business Concepts

A business concept is a Knowledge Platform asset of type term, with an applied language, name, and definition. For example, a business concept may relate to a data object such as Customer Master or a data domain such as Customer Data, which may cover a much broader set of Data Objects.

Assigning rules to a business concept allows the average data quality score of the latest run of all of the associated implementations to be rolled up to that business concept.

Business Processes

Assigning rules to a business process allows the average data quality score of the latest run of all of the associated implementations to be rolled up to that business process.

Enforcements

Enforcements document how a particular rule is to be enforced in a particular system for a specified purpose, such as Data Quality, Data Migration or MDM. Enforcements are assigned to further define rules.

Enforcements document information about the implementation process and the technology behind rule enforcement. With this information, business and technical users can easily understand how rules are enforced and the impact that rules have on the business. Enforcements accommodate different types of rules that are automatically enforced via enforcement technology (for example, the Stewardship Tier), rules that are enforced via manual tasks, rules that are enforced for data quality purposes and rules that are not enforced but require documentation.

Enforcements contain a Status and Priority field to help track progress, and the following Enforcement Types to help users document relevant information about rule enforcement:

• Data Migration—Documents rule enforcement during transition to a new platform

• Data Quality—Documents rule enforcement through monitoring and remediation

• Master Data Management—Documents rule enforcement as it defines and manages an organization’s critical data

Note

To be used in data quality, an enforcement’s Roll Up Data Quality Score check box must be checked on the Create Enforcement or Edit Enforcement page.

DQ Reports (Implementations)

Enforcements can be implemented against a system connection to create a DQ Report.  A Report consists of user-defined Opportunity and Error queries to be run against a specific connection to measure the number of Errors.   The Data Quality score is calculated as the number of Errors divided by the number of Opportunities.

For example, a rule that requires all customers to have a phone number could define an error query such as

SELECT Customer Number, Name, Address, Email, Phone, Fax
 FROM Customer
WHERE status = 'active'
    AND Phone IS NULL

which returns 100 records.  

The Opportunity query will select to the total number of active customers

SELECT COUNT(*)
 FROM Customer
WHERE status = 'active’

which returns a count of 1000 total active customers.

The DQ Score is then calculated as
1 - (100/1000) =  90%
because 10% of customers are missing a phone number.

Note

When creating an implementation of Method Syniti Cloud DQ, users can choose whether to calculate just the data quality score or to also create a report that contains the records that failed the rule. When creating the implementation, if the Save Report toggle is set to on, you can access the detailed records that failed the rule from within the Syniti Knowledge Platform. If set to off, or, if no data store is configured in Admin, only the record counts display. Refer to View Data on Reports for more information.

This section contains the following topics about data quality:

• How the Data Quality Score Rolls up Across Assets

• Data Quality Score Calculation

• Data Quality Thresholds

• View Data on Reports

• Configure Assets for Data Quality