Introduction

Data Quality Management defines the goals, approaches and plans of action that ensure data content is of sufficient quality to support defined business objectives of the organization. Data Quality Management should be developed in alignment with business requirements, measured against defined data quality dimensions and based on an analysis of the current state of Data Quality. Data Quality Management is a series of processes across the full data life cycle to ensure that the data provisioned meets the needs of its intended consumers. By ensuring that data cleansing and validation processes are efficient and effective, Data Quality Management enables the organization to maintain a robust data control environment. This ensures that data is fit for its intended purpose, supports informed decision-making, enhances operational efficiency, and delivers measurable value to the business.

Definition

Data Quality Management component encompasses a set of capabilities to implement data profiling, business-driven quality evaluation, quality control rule development, monitoring, defect management, root cause analysis, and data issue remediation. These capabilities allow the organization to execute data quality processes across the data life cycle to ensure and control that data is fit for its intended purpose.

Scope

• Establish a Data Quality Management function.
• Work with Data Management Program Management Office to design and implement sustainable business-as-usual processes and tools for Data Quality Management.
• Perform Data Quality Management processes against the organization’s prioritized data. Data Quality Management processes include profiling & grading, rule building, ongoing measurement, defect management, root cause fix, remediation.
• Establish Data Quality rules, metrics and reporting routines.
• Ensure that Data Quality Management and Data Quality Management Governance are integrated into Data Governance.

Value Proposition

Organizations that build, formalize, and assign data quality responsibilities to daily business routine can achieve improved data culture. Organizations that effectively implement Data Quality Management across the data ecosystem get a ROI from several areas:

• Better risk management
• Enhanced analytics
• Better client service and product innovation
• Improved operational efficiencies
• Lower cost of change / transformation and quality improvement

Overview

Data Quality is a broad conceptual term that needs to be understood in the context of the organizational processes where data is used. Seeking to create “perfect” data is not always a viable objective. The organization needs to develop a Data Quality Management approach and establish overall plans for managing the integrity and relevance of its data. The quality of the data needs to be defined in terms that are relevant to the data consumers to ensure that it is fit for its intended purpose. The overall goal of Data Quality is to ensure that data consumers have confidence in the data they receive from data producers. Given that Consumers are using data to support business functions and make decisions, the data must communicate facts that conform with the user’s domain, context, and operational requirements. There should be little or no need for reconciliation or manual transformation of the data on the part of the consumer. Data Quality is a process not a project. One of the essential objectives is to create a shared culture of Data Quality stemming from executive management and integrated throughout the operations of the organization. Data quality can be segmented into measurable dimensions. Organizations can benefit from using data quality dimensions to classify defects, which will enhance proper remediation efforts and enable effective monitoring. The dimensions, such as those presented below, are typical examples for consideration. DQ can be segmented into dimensions:

• Accuracy: the relationship of the content with original intent
  • Example: the correct date associated with a document
• Completeness: the availability of required data attributes
  • Example: all employees have a hire date in an HRIS
• Consistency: how well the data complies with the required formats and definitions across datasets
  • Example: an address is the same for a business across AP and Vendor Management
• Coverage:: the availability of required data records
  • Example: a data set includes all the values for the required time period
• Timeliness: the currency of content representation as well as whether the data is available/can be used when needed
  • Example: data is in a data set, but was entered too late causing missed deadlines
• Uniqueness: the degree that no record or attribute is recorded more than once
  • Example: multiple records that refer to the same individual in a CRM system
• Validity: the degree data conforms to its agreed upon definition of syntax, including format, type, and range
  • Example: month should be a numeric value between 1 and 12.

The identification and prioritization of data quality dimensions foster effective communication about Data Quality expectations and provide benefit to the Data Management initiative. Creating a profile of the current state of data quality is an important aspect of the overall Data Quality Management function. A new profile should be created when data is onboarded or created, and periodically when data is transformed or there is change within the data ecosystem. The goal is to assess patterns in the data as well as to identify anomalies and commonalities as a baseline of what is currently stored and how actual values may differ from expected values. Once the data profile is established, the organization needs to evaluate the data against the quality tolerances and thresholds defined by the Data Quality requirements. The evaluation also examines business requirements to validate that the data is fit-for-purpose. The purpose of this evaluation process is to measure the quality of the most important business attributes of the existing data and to determine what content needs remediation. The responsibility of the data producer and data consumer is to identify the data critical to the data consumers’ business processes. Prioritizing the data based on criticality then informs the Data Quality Management function on which attributes require a heightened level of control and governance. Data that is designated as critical requires the highest level of data quality across the identified dimensions. The profile evaluation process identifies data that needs remediation to meet data consumer requirements. Data cleansing and/or remediation should be identified based on a predefined set of business rules that identify defects and are linked to business and operational processes. Data remediation should be performed as close to the point of capture or creation as possible. Implementing a root-cause analysis approach for data remediation will address additional factors impacting data quality including, business processes, user-interface updates, and other non-data related influences. There should be clear accountability and a defined strategy for data remediation to ensure that rules and processes are known thus avoiding duplicate processing at multiple points in the data life cycle. The overall goal is to remediate data once, based on verifiable documentation and business rules as well as to fix the processes that allowed defective data into the system at the root cause. Data corrections must be communicated to, and aligned with, all downstream repositories and upstream systems. It is important to have a consistent and documented process for issue escalation and change verification for both data producers and data vendors. It is also important to ensure that data meets quality standards throughout the life cycle so that producers and consumers of data have clearly defined expectations related to how data is managed both at rest and in transit. Standards support the integration of data into operational data stores, the propagation of data across the internal ecosystem, external data exchange with third parties, and expectations related to security, regulatory requirements and risk management. For Data Quality to be sustained, a strong governance structure with the highest level of organizational support from senior executive management must be in place. This supports Data Quality Management activities and ensures compliance with Data Quality processes. Data Quality processes need to be documented, operationalized and routinely validated via Data Management reviews and formal audit processes. Data Quality cannot be achieved through central control. Organization-wide Data Quality requires the commitment and participation of a broad set of stakeholders. Data Quality is the result of a series of business processes creating a data supply chain. Therefore, stakeholders, along that chain, must be authorized and held responsible for the quality of data as it flows through their respective areas. Data Quality requires coordinated organizational support. Data Quality Management processes and objectives must be part of the operational culture of an organization for it to be sustained and successful.

Core Questions

• Is it understood that poor quality data is often an indication of a broken business process or technology?
• Is it understood that instituting a Data Quality system is a cultural shift that touches all aspects of business, operations and technology processes?
• Are roles and responsibilities defined to sustain the Data Quality Management function?
• Are the necessary people and funding resources earmarked to implement and operate the Data Quality Management function?
• Are the core operational processes formalized and communicated (profiling, monitoring, remediation, standards management, change management)?
• Are Data Quality Management standards authored, approved and communicated?
• Are the necessary resources in place to provide organization-wide training to support a sustainable, Data Quality cultural change?
• Is there senior-level support formalized around a means to escalate and socialize Data Quality issues?

Core Artifacts

The following are the core artifacts required to execute an effective Data Quality Management capability. Items with an ‘*’ link to published best practice guidelines.

• Critical Data Element Identification Method & Criteria
• Data Profiling Methodology
• Data Quality Dimensions Framework*
• Data Quality Metrics & Dashboards
• Data Quality Rules Inventory
• Defect Management Methodology
• Root Cause Analysis Methodology
• Integration/Migration/Migration Impact Assessment

The Data Quality Management function approach and plan must be defined and approved by stakeholders. Roles and responsibilities across the stakeholders must be established with auditable operational processes in place.

Description

The approach and plan must be defined for the Data Quality Management function and reflect the related vision and objectives of the Data Management Approach. Once established, it must be formally empowered by senior management and its role communicated to all stakeholders.

Objectives

• Formally establish the Data Quality Management approach and plan within the organization.
• Ensure alignment of stakeholder roadmaps and plans with the Data Quality Management strategy.
• Communicate the role of the Data Quality Management function across the organization.
• Operate the Data Quality Management function collaboratively with Data Management initiative stakeholders.
• Secure authority to enforce Data Quality Management compliance through policy and documented procedures.

Advice

The Data Quality Management strategy and approach encompasses the what, how, when, who, and why of Data Quality. It needs to address what scope of data is to be scrutinized and reviewed, how the Data Quality assessments will be performed with defined metrics, when and how often assessments will take place and who will be responsible with defined roles. Data Quality Management needs to be closely aligned with the organization's business objectives to ensure that the most important data is properly maintained and monitored, demonstrating how data quality initiatives can support business outcomes and avoid the risks and costs of defective data. Data Quality Management involves cultural change. It is critical that a documented Data Quality Management strategy and approach is socialized with business, data, and technology stakeholders to ensure awareness, support and commitment. The rapidly evolving focus on data ethics is introducing new requirements for the Data Quality Management function. These requirements include an ethical review as part of determining that data produced is fit-for-purpose. Additionally, Data Quality Management is one of the areas where the use of techniques and tools such as Artificial Intelligence may assist in the processes used to achieve quality data. These requirements and opportunities should be evaluated in the strategy and approach of the Data Quality Management function. Alignment of the Data Quality Management approach and the roadmap to the Data Management Strategy and objectives is achieved by agreement between the operating-level data officer via the Data Governance structure. The operating-level data officer is accountable for establishing priorities across each of the Framework Component requirements.

Questions

• Has the Data Quality Management function been formally established?
• Is there a Data Quality Management Approach in place?
• Are the Data Quality Management Approach and roadmap aligned to the Data Management Strategy?
• Have innovative technologies such as Artificial Intelligence been considered as part of the Data Quality Management process and infrastructure?
• Has the Data Quality Management function been formally communicated to business, technology, operations, finance, and risk stakeholders?
• Has executive management demonstrated its support?
• Has authority been granted to the Data Quality Management function to implement and enforce best practice via policy and standards?
• Has authority been communicated to stakeholders?
• Is there a functional partnership in place with Internal Audit?
• Have the integration impacts been reviewed?

Artifacts

• Data Quality Management Approach & Plan
• Communication of specific support from executive management with distribution lists

Scoring

Description

Data Quality Management requires collaboration from a network of individuals within the organization, including but not limited to, data stewards and subject matter experts, to ensure accurate data is properly captured, processed, and delivered. Accountable parties must be identified, and the roles and responsibilities must be clearly communicated.

Objectives

• Define and communicate the roles and responsibilities of the Data Quality Management function throughout the data life cycle.
• Proper staffing is identified and responsibility understood in the Data Quality Management function.

Advice

Data Quality Management involves numerous stakeholders who are responsible for data requirement capture, data profiling, remediation, definitions, metadata, transformation, root cause analysis, and coordination across the full data ecosystem and the data life cycle. These efforts involve the assignment and empowerment of owners, stewards, curators, custodians and others. These accountable parties need to be at the right levels of seniority as well as understand all the internal processes associated with Data Quality Management. With the addition of a data ethics review in the Data Quality Management process, subject matter expertise will be required either through the addition of experts or appropriate training of Data Quality Management resources. Similarly, to the extent that AI is used to support the Data Quality Management process, additional skills will need to be added or developed within the stakeholders.

Questions

• Is the Data Quality Management function appropriately staffed?
• Have the roles and responsibilities of the Data Quality Management function been defined, documented and socialized?

Artifacts

• RACI matrix or other evidence of accountability assignment
• Description of the roles and responsibilities of the Data Quality Management function
• List of stakeholder assignments and bi-directional communication
• Gap analysis of skills needed and in place

Scoring

Description

Formal processes must be established for the activities of the Data Quality Management function. These processes align with the Data Management policy and standards of the organization and include procedures, tools and routines. The routines are required for steady-state operations.

Objectives

• Establish formal Data Quality Management processes in alignment with the Data Management policy and standards.
• Ensure processes are engaged and aligned to business, operational, and audit stakeholders.
• Integrate the Data Quality Management processes into the overall end-to-end processes of the Data Management initiative.
• Data Quality Management processes are auditable.

Advice

The Data Quality Management subject matter experts should work with the business process design and optimization service(s) within the Data Management Program team. Together they will create and monitor the implementation of the Data Quality Management processes in alignment with the end-to-end process across the full Data Management initiative. Data Quality Management processes must be implemented to streamline and support organization requirements for review and audit. Audit review processes need to be established and, where appropriate, supported by reporting capabilities to streamline the process. The Data Quality Management process design should include the requirements for ethical review as part of determining the data is fit-for-purpose. The design should also incorporate Artificial Intelligence into the process if included in the Data Quality Management strategy and approach.

Questions

• Have formal processes been defined and implemented?
• Are the procedures, tools and routines in place for implementing the processes?
• Has the review of data ethics been included within the defined processes?
• Are Data Quality Management activities part of the normal operational routine of stakeholders?
• Are there standing meetings, planning sessions and regular communication about data initiatives?

Artifacts

• Process design artifacts, procedure guides, and published routines
• Process performance metrics reports
• Meeting minutes, status reports, and Data Management Program announcements

Scoring

Profiling and measuring the data includes:

• Prioritizing the data in scope based on criticality and materiality
• Measuring that the data is fit-for-purpose
• Defining and testing data quality rules based on business rules to validate fit-for-use

Description

The data in scope as defined by the business objectives must be prioritized based on its criticality and materiality to the data consumer and business process.

Objectives

• Define a process for prioritizing in-scope data.
• Identify the scope of data subject to Data Quality Management, both current and historical.
• Prioritize the scope of data in alignment with the DS and business priorities.

Advice

An organization may establish data prioritization tiers. The Data Management policy and standards should define levels of data control to apply to each prioritization tier. The highest-level tier is a critical data element. Designated critical data elements receive the highest level of control to ensure the quality of these attributes is maintained. Critical data element designation is a controlled process, typically captured in data classifications, to achieve agreement between the data producer and data consumer.

Questions

• Has the process to prioritize data been defined?
• Has the scope of data subject to Data Quality Management been identified, prioritized and verified?

Artifacts

• Prioritized critical data element inventory related to data domains
• Bi-directional communication about the inventories and data quality expectations

Scoring

Description

The in-scope data must be profiled to determine the full spectrum of data quality dimensions for your organization (accuracy, completeness, coverage, conformity, consistency, timeliness, uniqueness). Metadata must also be reviewed to ensure the description and intended use of data is properly defined.

Objectives

• Define a process for profiling, analyzing, and grading data.
• Profile, analyze and grade in-scope data.
• Capture profiling results on a routine basis.
• Report profiling results to business, data, and technology stakeholders.

Advice

The Data Quality Management function is to establish that the data is fit-for-purpose and can be trusted. Data profiling creates a quality benchmark for the organization. Evidence of data profiling will be expected in any Internal Audit review or regulatory examination. Data needs to be assessed against both fit-for-purpose criteria and the data quality dimensions. Certain types of data such as time-series data, need to be evaluated against additional criteria like gaps, spikes and abnormalities. The primary stakeholders involved in this process are the data producer and data consumer. Ultimately, quality is defined by the business process requirements of the data consumer and should be formally agreed to by the data producer. Metrics are used to track Data Quality and drive data remediation efforts. Control points along the data supply chain capture Data Quality metrics that are used to produce Data Quality dashboards. The requirements of the data consumer are used to establish quality thresholds for the data. These thresholds permit the grading of the data as to the defined levels of acceptable Data Quality based on the minimal requirements of specific data consumer. Creating a standard and automated process for routinely executing the quality metrics and reporting the results is critical to meet the time constraints of the data supply chain. A mechanism for executing Data Quality rules and generating outcome reports is required to support the data profiling, analyzing and grading process. The use of Artificial Intelligence may assist in the process.

Questions

• Has the in-scope data been profiled, analyzed and graded?
• Is Data Quality profiled against business logic rules as well as for reasonableness against statistical expectations?
• Are the right business, operational, analytical, data, and technical stakeholders involved in the process?
• Are innovative technologies such as AI used to identify and suggest rules or perform cleansing operations as part of the process?
• Are standard criteria for measuring data quality defined and verified?
• Are metrics being collected and reported on a routine basis?
• Are the results of data measurement and grading captured as metadata in a data catalog?
• Are periodic reviews of data quality and business rules being performed for relevance?

Artifacts

• Business rules and data profiling measurement criteria
• Data Quality analysis results
• Mechanism for assigning and reporting grades for Data Quality
• Data Quality metric reports, dashboards, heat maps, and other forms of output
• List of stakeholders and evidence of bi-directional communication

Scoring

Description

Data quality rules based on business rules must be defined and tested to confidently validate the data is fit-for-use throughout its life cycle.

Objectives

• Define a process for the development of data quality rules across the data life cycle.
• Define business rules which can be translated into data quality rules and used to measure the quality of data.
• Define a process for the testing of data quality rules.
• Establish an environment and toolset for the running and testing of rules.
• Socialize Data Quality rules and test results with stakeholders.

Advice

Business rules are the basis for developing data quality rules necessary to quantify the quality of the data. A partnership with business subject matter experts is crucial to being able to define quality data. The data quality dimensions establish a range of potential rules that may be needed to determine overall Data Quality. A critical part of defining quality rules is to test the outcome of the rule. Testing is an iterative activity during the design of an individual rule. Testing and re-testing with each rule refinement is an essential activity for identification of the range of rules across the data quality dimensions. These dimensions are required for the data quality ruleset to accurately measure quality. Various changes can occur in the data life cycle that necessitate retesting of data quality rules, such as the introduction of new data into an existing process or changes in data structure. Understanding these changes is essential for determining when data quality re-testing is required. Data quality rules should be developed to test the various dimensions of quality. Not all dimensions are applied to the testing scenario. Care should be taken to ensure as many dimensions have rules as required. There is an art and a science to writing quality rules. The rules and the range of rules will evolve over time. As new quality defects surface, they will guide the design of new rules to detect those issues in the future. A mature data quality rule set is a coveted asset of an organization. Data quality rules require a testing environment, typically done in a development where rules can be established, tested, and results reviewed for successful outcomes. Additionally, the proper technical infrastructure to write, store, run and analyze rules is needed. The skill set required to test rules may include business process subject matter expertise, Data Quality management expertise, and technical infrastructure and coding expertise. Make sure you assess the skills and available resources carefully as this range of skills may not be available through a single individual.

Questions

• Is there a defined process for the design, testing, and deployment of data quality rules?
• Is there a library of business rules that are associated with data sets or products?
• Are the data quality rules based on defined business rules?
• Is there a data sandbox and appropriate tools for running data quality rule tests?
• How is stakeholder feedback and approval captured when running and evaluating data quality tests?
• Have the stakeholders validated that the range of data quality dimensions applied in the rule set is adequate to determine the Data Quality?

Artifacts

• Process design artifacts, procedure guides, and published routines
• Documented data quality dimensions
• Criteria used to evaluate Data Quality
• Data quality rules repository
• Data quality rules recorded as metadata
• Testing result reports and dashboards
• Data Quality rule-deployment process

Scoring

Maintaining the data quality includes:

• Implementing data quality control points
• Capturing Data Quality metrics to identify defective data
• Continuous monitoring of the data

Description

Data control points must be developed to quantitatively assess the quality of data as it flows through business and technology processes.

Objectives

• Establish a process to define Data Quality control points.
• Put Data Quality control points in place and bring them to a fully operational state along the data supply chain.
• Record Data Quality controls as metadata.

Advice

Data Quality is governed by developing control points along the data supply chain. Data Quality control points need to be applied at the point of data entry into the organization, the point of entry into the consuming application, and when data moves and transforms along the supply chain. Data Quality controls include the implementation of business rules, establishing workflows, setting Data Quality tolerances and monitoring data movement.

Questions

• Are control points defined, verified and documented?
• Are business rules defined, verified, documented and approved?
• Are business process flows defined and the way they handle exceptions verified?
• Are control points, business rules and process flow operational?
• Are controls reviewed and updated, as necessary, regularly?

Artifacts

• Documentation of control points, business rules and process flows
• Control process review and sign-off

Scoring

Description

Control points along the data supply chain capture Data Quality metrics that are used to produce Data Quality dashboards which are used to identify defective data. The Data Quality defects must be part of the issue management routine of the Data Management initiative. The Data Quality issue management process must track an issue to resolution and provide continuous stakeholder communication.

Objectives

• Manage data quality issues to resolution.
• Drive and prioritize remediation efforts using Data Quality metric reporting.
• Establish issue-management reporting routine and infrastructure.

Advice

Stakeholder engagement, inclusive of the data consumer and data producer, is critical for successful and strategic remediation of data issues. The data issues need to be managed through all stages of resolution. These stages include defect triage, prioritization, root-cause analysis, root-cause fix, and remediation of defective data. Remediation of data issues can include permanent solutions through to tactical fixes to enable a critical process to continue to operate. It is important to note that best practice is permanent solutions that address root causes. Communication with all stakeholders throughout this process is critical. Stakeholders must be made aware of the defective data and its impact on business processes and data consumption. The appropriate stakeholders may need to participate in the analysis and determination of an acceptable resolution. Important tools to support the resolution process are an issue log and a status tracking system. The link to the issue record should be part of the metadata for all instances of defective data. This record will communicate to all users across an organization and can be used to help minimize duplication of effort when an issue is uncovered at multiple points along the data supply chain. Often, particularly in the early stages of the Data Management initiative, the volume of defective data may be greater than the resources required to resolve the data quality issues. Documenting the prioritization process in the Data Quality issue log, even when it results in a backlog, demonstrates that these issues were known and evaluated, rather than being discovered during a future audit.

Questions

• Are Data Quality metric reports and dashboards distributed on a routine basis?
• Are metrics used to identify Data Quality issues and drive remediation?
• Are the Data Quality issues captured as metadata and linked to assets in a data catalog?
• Are there communications processes related to data issues/remediation?

Artifacts

• Data Quality dimension metrics
• Data Quality issue reports, dashboards, heat maps, and other forms of output
• List of stakeholders and evidence of bi-directional communication

Scoring

Description

Data quality is monitored at control points. Control points must be established where data enters a business process or when it enters a consuming application. To achieve continuous monitoring, the data must be checked at any time there is data entering either type of control point. Notifications of identified data quality issues must be enabled.

Objectives

• Continuous monitoring of Data Quality.
• Establish infrastructure for continuous monitoring of Data Quality.

Advice

The process of continuous monitoring has costs, benefits, and operational challenges. This monitoring should be real-time, part of a batch process, or able to be run on demand. Some form of automation is required to achieve continuous monitoring, alerting, and in some cases cleansing of data. Legacy systems often are not capable of continuous monitoring. It would be cost prohibitive to add these quality checks at the point of data capture or data use. The challenge, then, is to define a technical solution that allows execution of the quality checks as close to the point of data capture or load that is an acceptable cost.

Questions

• Is continuous monitoring of Data Quality performed?
• Is the infrastructure in place to support continuous monitoring of Data Quality?
• Are the defined control points and respective data quality rules being monitored?

Artifacts

• Schedule of Data Quality monitoring
• Data Quality defect reports and alerts

Scoring

Data remediation processes must be developed, documented, and executed to resolve the most pressing data quality issues. The process must include correcting the existing data and performing root-cause-fix to eliminate future data defects or accepting the defect. Problems with data impacting a business process must be logged, prioritized, and have a remediation plan developed and executed. In some cases, remediation of the data is a low priority due to lack of materiality, resources, or severity. Remediation processes should be developed to include the documentation and acceptance of these defects by stakeholders.

Description

Root cause analysis process is documented. Data remediation must include both correcting the existing data that is defective and determining the root-cause of the data issue to avoid the recurrence of defective data in the future.

Objectives

• Data issue root cause analysis
• Data issue/defect root cause identification
• Remediation alternative analysis

Advice

Before proceeding with any resolution, the source of the defect must be identified and understood. Data defects may have a people, process, data, or technical source. Having the right subject matter expertise from each of these areas will be important to the analysis of the root-cause. Remediating data issues is not merely an exercise in data correction. Data issues can be systemic. It is important to perform the analysis to evaluate the depth and breadth of data issues to determine if the organization is focused more on tactical repair versus the upstream remediation of the root cause. With the root cause identified, the team should perform the analysis of possible alternative solutions to be evaluated by the remediation prioritization (see 5.4.2). A strong reporting structure is needed to ensure that upstream systems are aware of repetitive or continuing data problems. Equally, a strong governance structure, sponsored at Executive level, is needed to ensure appropriate resolution of root causes, especially where the root cause is not in the same organizational area as the impacted process.

Questions

• Is root cause analysis performed?
• Are corrective alternatives identified to support prioritization?

Artifacts

• Evidence of data issue reporting across the data supply chain
• Evidence of root cause analysis and remediation being performed
• Evidence of benefit realization and verification of successful remediation

Scoring

Description

Data issues are logged with appropriate materiality or severity, to enable prioritization. Data issues should be logged irrespective of the suspected root cause. Based on the analysis of the data issue, remediation plans must be developed to address the most pressing issues. Ongoing Data Quality evaluation and maintenance and timelines must also be established.

Objectives

• Prioritize and execute data remediation.
• Implement data remediation planning and management.
• Properly manage data remediation needs.

Advice

Data remediation is about correcting defective data that has been identified and preventing future defects. The defective data should be corrected as close to the source of data capture as possible, and the root cause of the defect is resolved. It may also be appropriate to implement tactical fixes to partially remediate the defective data at the point of usage, and the tactical fixes should be retired as part of the remediation. Make sure the remediation activities are not one-off processes but rather established as part of the Data Quality Management routine. Data remediation needs to be implemented for both data-at-rest and data-in-motion.

Questions

• Are data remediation plans managed and tracked to completion?
• Are prioritization criteria for remediation documented, communicated and used?
• Have data remediation plans been developed, verified and prioritized?

Artifacts

• Data Quality defect reports
• Data issue and remediation log
• Data remediation plan
• Evidence of issue prioritization
• Evidence of remediation being accomplished
• List of stakeholders and evidence of bi-directional communication

Scoring