In the dynamic business landscape, strategic decision-making is the compass guiding organizations toward success. At the heart of this process lies data, the invaluable asset that fuels analytics and shapes the trajectory of strategic initiatives. However, the accuracy and reliability of data are the linchpins that determine the efficacy of these decisions. From this perspective, data governance plays a pivotal role in maintaining data integrity.
Data governance is a set of policies, processes, roles, and standards that ensure the effective and efficient use of data across an organization, in addition to compliance with relevant regulations and ethical principles. Data governance aims to establish clear rules and responsibilities for data creation, collection, storage, access, sharing, analysis, and reporting.
In that sense, a robust data governance strategy is indispensable in the context of strategic analytics. A data governance strategy is crucial for maintaining data accuracy and reliability and ensuring that the information driving decision-making processes is consistent, timely, and aligned with organizational goals.
assigning ownership of data assets and defining roles for data stewards, data analysts, and other stakeholders
implementing data quality KPIs and procedures to monitor and improve data accuracy and completeness
implementing robust data security measures to securely store and access data and to comply with data privacy regulations and ethics
investing in data management tools to automate data cleaning, profiling, and lineage tracking tasks
promoting a data-driven culture by educating employees on data governance policies and best practices
There are several examples of how the aforementioned measures help companies with their strategy management. One of these case studies is Wells Fargo, one of the largest banks in the United States. Wells Fargo adopted a data governance operating model, which defines the roles, responsibilities, and processes for the effective management of data across the organization. The company was better able to do this by connecting its data sources with a data fabric that integrates inputs from multiple systems.
In another case study, GE Aviation, the aviation division of General Electric, consolidated its scattered data sources in a data lake. A data lake is a large-scale data storage and processing platform that can handle structured and unstructured data from various sources, making information more manageable, reliable, and accessible for all users within the organization.
The two examples show that strategy management improved when built on accurate and reliable data, leading to better outcomes. Simply put, if the accuracy of the inputs is jeopardized, then the outputs can only be expected to be flawed.
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Editor’s Note: This article was originally published in the print edition of Performance Magazine Issue No. 29, 2024 – Strategy Management Edition
When implementing a Performance Management System (PMS) based on Key Performance Indicators (KPIs), the organization needs to create a favorable context to plan, organize, coordinate, communicate, and control performance. Such endeavor implies multiple initiatives, resources, and most of all, employee engagement. However, challenges are inevitable. These challenges often arise from the mechanisms and relations by which the KPI Measurement Framework and KPI-related processes are controlled and directed.
As such, unclear definitions and overlap of roles and responsibilities and lack of ownership, commitment, or clarity in terms of target achievement accountability are some of the most common challenges that may endanger the achievement of strategic business objectives and goals. The root cause of these dysfunctions is that KPI governance structure has never been clearly defined or described.
KPI governance structure
There are multiple parties involved in governing and managing KPI-related processes, and all play a specific role in promoting, supporting, designing, implementing, and maintaining the KPI measurement framework. A typical KPI governance structure includes the following components:
Performance Manager – Responsible for supervising the entire process
PMO specialists – Support the persons involved in the process, analyze data and check it for accuracy
KPI owner – Responsible for KPI target achievement
Data custodian – Responsible for KPI results collection/ data collection
KPI owners and data custodians have two of the most operational KPI governance roles within the organization. While the data custodians are responsible for ensuring that high-quality KPI data is gathered and communicated to all interested stakeholders, the KPI owners are mainly responsible for the KPIs under their management, making sure that they are viable and measurable.
KPI owners’ role and responsibilities
Within a standard Data Governance Framework, a data owner is in charge of ensuring that processes are followed to guarantee the collection, security, and quality of data. Frequently in a senior or high-level leadership position, a data owner has a role in planning the data, supervising access to it, ensuring data security, and defining a repository to contextualize the data.
Similarly, a KPI owner is responsible for overseeing the process, function, or initiative that the KPI is monitoring. That person has access to the data, knowledge of how that domain functions, and, most of all, is empowered to make decisions on improving operations.
In a nutshell, the KPI owner is responsible for reaching KPI targets through the following actions:
Monitoring (looking at) the measure over time
Interpreting its trends and patterns and seeking causes for them
Communicating this information to people affected by that performance area
Initiating action to improve performance in that area
Following up to be sure that actions have the desired effect on performance
Data custodians’ role and responsibilities
Within a KPI governance framework, data custodians are involved in the design of performance data collection, receipt and storage, process, analysis, reporting, publication, dissemination, and archival or deletion of data. The daily processing and management of performance data are therefore under the control of appointed data custodians. The person assigned with such a role must demonstrate high levels of data literacy as well as skills in data management software systems and tools.
Other required competencies for a data custodian are as follows:
The ability to intuitively identify and recognize any variance from the data quality dimensions
Focused on the improvement and automation of the process
Can competently apply the behaviors and skills of managing change
Uses change as an opportunity to advance business objectives
Works to minimize complexities, contradictions, and paradoxes or reduce their impact
Unifies leadership support for direction and smoothens the process of change
We may say that the data custodians are the guarantors of a sound performance data gathering process. Because of that, the profile of such an individual should also cover an analytical mind, experience in measuring and reporting metrics/ KPIs, information technology skills (basic Microsoft Excel or more advanced data analysis tools, depending on the data architecture`s level of automation), and a strong sense of integrity and ethics.
While some companies may hire specialized professionals, such as data analysts, other organizations may assign the data custodian roles to the existing employees.
Conclusion
Building a strong KPI governance team is a key part of the KPI-related processes and functionalities and of successfully overcoming the inherent challenges of implementing a PMS. Once the right people are on board, they need to be guided towards making the right decisions and focusing on the correct issues, ultimately making sure that information is being governed for a purpose that aligns with business objectives.
