Data Governance: Impact Effort Matrix
- Sash Barige
- Mar 1, 2022
- 2 min read
Updated: Nov 3, 2023
I'll share here a detailed approach on using the Impact Effort analysis for prioritization approach
Process
The process to establish the Impact Effort matrix is a collaborative approach where key members will be asked to vote on the items. The items are gathered primarily by surveying business leaders, data owners and data governance leads. The input from business leaders are critical to this process to assess the business value. The input from the data governance council and the data governance leads are critical since they (especially those who are skilled at data governance) are the subject matter experts and ensures that the critical data governance benefits (aligned with the business goals) are represented.
For identified use cases to be tackled by the data governance, gather scoring across these 5 dimensions and stack rank them. This helps to place the items on the impact effort matrix.
Score each initiative based on the following five criteria:
Alignment with business goals and objectives
Impact on data risks and vulnerabilities
Maturity of the organization's data governance program
Stakeholder input
Amount of resources needed (effort/cost/resources)
To collaborate, to identify top-ranked initiatives for implementation, to get buy-in from the leadership, to get buy-in from the tactical/operational teams to implement solutions, assemble the members and provide instructions on how to vote. Start with the initial matrix established through the scoring/ranking approach. To get the voting, we have used a digital canvas to do this successfully instead of a physical whiteboard. Each member is given the entire set of items and asked to put them in four dimensions within their own canvas. These are then compared and discussed to ensure we have a clear understanding of the business impact and the time/cost/resource impact. The items move around until we have a consensus on where it should be. This is somewhat similar to the technique I'd used for prioritizing ideas through the Six Sigma Affinity diagram. Note, there is a variation on this technique if you do it on a whiteboard in a conference room. An important consideration is to identify quick win short-cycle (3-4 month window) deliverables.
Below is a diagram I created to illustrate an example of how the Impact Effort matrix can aid in your prioritization of data governance activities
High Impact Low Effort
High Impact Low Effort
This is where I generally start to identify efforts that can be quick wins as well as have a high impact to the business needs.
High-impact, low-effort areas in data governance represent aspects of data management and governance that have a significant positive impact on the organization but can be addressed with minimal resources, time, and effort. Identifying and prioritizing these areas can lead to quick wins and improvements in data quality, efficiency, and overall data governance. Here are some examples of high-impact, low-effort areas in data governance:
Data Cleanup Automation: Implementing automated data cleansing and validation processes for critical data, reducing manual data cleaning efforts.
Data Standardization: Enforcing data standardization and naming conventions for high-impact data elements, making it easier to understand and use the data.
Data Quality Metrics: Defining and monitoring key data quality metrics for critical data to quickly identify and address data quality issues.
Data Profiling: Conducting data profiling on important datasets to gain insights into data characteristics, anomalies, and data quality problems.
Data Documentation: Creating or updating data dictionaries and metadata for high-impact data elements to improve data understanding and accessibility.
Data Governance Training: Providing targeted data governance training for employees working with critical data, ensuring they understand data governance best practices and compliance requirements.
Data Classification and Tagging: Applying data classification and tagging to high-impact data to improve data security and access controls.
Data Access Reviews: Conducting periodic access reviews and audits for high-impact data to ensure that the right people have appropriate access.
Data Quality Rules: Implementing and automating data quality rules and validation checks for critical data elements.
Data Lineage Documentation: Creating or enhancing data lineage documentation for high-impact data to improve transparency and traceability.
Data Ownership and Stewardship: Appointing data owners and stewards for high-impact data to ensure accountability and data governance oversight.
Data Retention Policies: Developing and enforcing data retention policies for critical data, ensuring compliance and efficient data management.
Data Governance Dashboard: Creating a data governance dashboard or reporting system for high-impact data to provide visibility into data quality and compliance.
Data Quality Issue Resolution: Establishing processes for quickly identifying and resolving data quality issues in critical datasets.
Data Privacy Compliance for Critical Data: Ensuring full compliance with data privacy regulations for high-impact data, including consent management and data subject rights.
By focusing on these high-impact, low-effort areas, organizations can realize immediate improvements in data governance, data quality, and overall data management efficiency. While these areas require less effort compared to other high-impact initiatives, they can yield substantial benefits and set the foundation for more comprehensive data governance practices in the future.
High Impact High Effort
High-impact, high-effort areas in data governance are critical aspects of data management and governance that have a substantial impact on the organization and require a significant allocation of resources, time, and effort to address. These areas typically affect core business processes, data quality, regulatory compliance, and overall data management. Here are some examples of high-impact, high-effort areas in data governance:
Data Quality Improvement: Implementing comprehensive data quality initiatives to ensure the accuracy, consistency, and completeness of critical data that drives core business operations.
Master Data Management (MDM): Establishing a robust MDM framework to manage critical master data entities such as customer, product, or employee data consistently across the organization.
Data Security and Privacy Compliance: Ensuring that sensitive and confidential data is protected in accordance with relevant data protection regulations (e.g., GDPR, HIPAA) through comprehensive security measures and privacy controls.
Data Classification and Sensitivity: Defining and enforcing data classification and sensitivity levels for critical data assets, including the identification of personally identifiable information (PII) and other sensitive data.
Data Governance Framework Implementation: Developing and implementing a formal data governance framework with policies, procedures, and a governance council to oversee high-impact data.
Data Lineage and Data Flow Documentation: Creating detailed data lineage and data flow documentation for data that plays a pivotal role in decision-making and critical business processes.
Data Integration and ETL Processes: Establishing efficient and reliable data integration and ETL (Extract, Transform, Load) processes for high-impact data, ensuring data consistency and reliability.
Data Catalog and Metadata Management: Building a comprehensive data catalog with robust metadata management for high-impact data assets, including detailed business glossaries and data dictionaries.
Data Retention and Data Deletion: Developing and enforcing data retention and deletion policies for high-impact data to manage storage costs and regulatory compliance.
Data Access Control and Governance: Implementing strict access controls and access governance procedures to protect and monitor access to mission-critical data.
Data Governance Training and Awareness: Conducting extensive data governance training and awareness programs for employees to ensure a shared understanding of data governance principles and practices.
Data Quality Monitoring and Reporting: Establishing continuous data quality monitoring and reporting for high-impact data, with regular audits and alerts for anomalies.
Regulatory Compliance Management: Ensuring full compliance with relevant industry-specific or regional data regulations for high-impact data, with ongoing monitoring and reporting.
Data Disaster Recovery and Business Continuity: Developing data disaster recovery and business continuity plans for critical data to minimize data loss and downtime.
Data Governance Technology Investments: Investing in advanced data governance and data management technologies and tools to support high-impact data governance efforts.
Prioritizing high-impact, high-effort areas is crucial for data governance success, as addressing these aspects directly contributes to the organization's operational efficiency, data quality, compliance, and overall data-driven decision-making capabilities. These areas often require a strong commitment from both leadership and the broader organization to allocate the necessary resources and effort for successful implementation.
Low Impact High Effort
Low-impact, high-effort areas in data governance refer to aspects of data management and governance that, while not having a significant impact on the organization, require a substantial amount of resources, time, and effort to address. These areas may not directly affect critical business processes or have a minimal impact on data quality, but they might still be important for compliance, future scalability, or maintaining a comprehensive data governance program. Here are some examples of low-impact, high-effort areas in data governance:
Legacy Data Migration: Migrating or consolidating data from legacy systems that are no longer in active use. While these data may not have a significant impact on current operations, the effort required to extract, transform, and load (ETL) the data can be substantial.
Data Archiving: Implementing a comprehensive data archiving strategy for historical data that is rarely accessed but must be retained for legal or regulatory reasons. The effort lies in defining and maintaining the archiving processes.
Data Standardization: Standardizing data formats or structures for data that is not frequently used but is stored in various formats or schemas.
Data Lineage Documentation: Creating detailed data lineage documentation for data that, while not directly impacting critical processes, is complex and spans multiple systems.
Data Quality Improvement: Initiating a data quality improvement project for non-critical data that may be poorly maintained but is of lower operational importance.
Data Governance Tool Implementation: Deploying sophisticated data governance tools and platforms for managing less critical data, which may require extensive setup and training efforts.
Data Catalog Implementation: Building a comprehensive data catalog for data that is infrequently used, ensuring that metadata, lineage, and business glossaries are fully integrated.
Data Security Enhancements: Strengthening data security controls, encryption, or access management for data with low operational significance but sensitive attributes.
Data Compliance Frameworks: Developing or enhancing data compliance frameworks for data that does not directly fall under stringent regulatory requirements but still needs to adhere to certain standards.
Data Governance Policies and Procedures: Elaborating and maintaining extensive data governance policies and procedures for data that is not mission-critical but still part of the data landscape.
It's essential for organizations to carefully consider whether investing high effort in low-impact areas is justified by their specific business, regulatory, or strategic objectives. While low-impact areas may not be a top priority, sometimes addressing them can set the stage for future growth, scalability, and regulatory compliance, making them worthwhile in the long run. However, organizations should strike a balance between addressing low-impact, high-effort areas and focusing their primary efforts on high-impact data governance initiatives.
Low Impact Low Effort
The low-impact, low-effort areas refer to data management aspects that have relatively minor significance or consequences for the organization and require minimal resources and effort to address. While these areas may not be the highest priority, they should not be completely neglected, as even low-impact issues can accumulate and affect data quality and overall data governance over time. Here are some examples of low-impact, low-effort areas in data governance:
Data Cleanup: Fixing minor data inconsistencies, such as typos or formatting issues, in non-critical datasets.
Documentation Updates: Reviewing and updating documentation for data elements, data dictionaries, or data lineage for less frequently used or less critical data.
User Training: Providing basic data governance training to users who work with less critical data, with a focus on best practices and data quality.
Metadata Management: Expanding metadata management to cover less critical data elements or datasets.
Data Classification: Applying data classification and sensitivity labels to less critical data, though not as rigorously as to highly sensitive data.
Data Retention: Establishing or reviewing data retention policies for data that is not subject to strict regulatory requirements.
Data Access Controls: Implementing basic access controls for data that doesn't contain sensitive or highly confidential information.
Data Auditing: Conducting periodic audits and compliance checks on data that is not subject to stringent regulations.
Data Quality Metrics: Defining and monitoring basic data quality metrics for non-critical data, without investing in complex data quality improvement initiatives.
Data Governance Committee: Including representatives from less critical data domains in data governance committees or working groups but with less frequent meetings and fewer resources allocated.
Data Privacy Compliance: Ensuring that less critical data adheres to privacy regulations but with a lighter compliance process compared to highly sensitive data.
It's important to note that while these areas may be considered low-impact and low-effort, they still contribute to overall data governance maturity and help maintain data quality and consistency. Organizations should strike a balance between addressing these areas and focusing their primary efforts on high-impact, high-priority data governance initiatives. The specific definition of what constitutes low-impact and low-effort can vary depending on an organization's priorities, data landscape, and regulatory environment.
There are other approaches to prioritization. Some of these approaches are to guide prioritization for idea, product, new market analysis. You can research these further. I might write a blog entry for couple of them in the future
Kano Model
MoSCoW
Story Mapping
Speed Boat
Ian McCalister Framework
Financial Analysis
Opportunity Scoring
RICE Method
Feasibility, Desirability, Viability Scorecard
Sash Barige
Mar/01/2022
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