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
One of the most common challenges faced by professionals in working with key performance indicators (KPIs) relates to data. They grapple with collecting and analyzing data to establish targets accurately, as indicated by 42% of respondents in The KPI Institute’s State of Strategy Management Practice 2023 Report.
This is particularly important as the collected data is expected to be of high qualityand “fit for their intended uses in operations, decision making, and planning,” according to the book “Modern Data Strategy,” by Mike Fleckenstein and Lorraine Fellows. Drawing from its advisory experience, The KPI Institute recommends employing the following data quality dimensions as a framework for assessing your data (see Figure 1).
Figure 1. Data Quality Dimensions | Source: Certified KPI Professional training program
Overcoming Issues with Data Quality Dimensions
Figure 2 highlights a dataset that has encountered significant data quality issues. Through an initial audit, several faulty elements have been identified, revealing potential inaccuracies that could have an adverse impact. This section presents approaches for effectively resolving these faulty elements to improve data reliability.
Figure 2. Sample quality troubled dataset | Source: The KPI Institute
A – Completeness: There is a missing value in the Actual Result column. One way to prevent this is to develop and utilize a data collection template that clearly outlines the necessary data fields. It is also important to regularly review the completeness of the data and address missing information that affects analysis.
B – Consistency: The structure of the data does not correspond with the template, the name, and the position of the Data Custodian being switched. To prevent this issue, one must make sure the data presents the same values across different systems and follows the same structure.
C – Timeliness: This issue pertains to the data being received after the specified deadline. One potential solution is to establish a data collection cycle time and set clear deadlines for data submission. Communicating these deadlines to all relevant parties and sending reminders for data submission can also help address this issue.
D – Conformity: The KPI is expressed as a percentage rate, but the data provided for the result includes a numerical value. To ensure conformity, organizations must provide clear guidelines on data format and how the KPI should be calculated.
E – Accuracy: This issue concerns the usage of an inappropriate sign. The KPI measures a rate, but the sign used in the KPI name is “$.” To ensure accuracy, one should make sure the data reflects real information, including the use of appropriate units. To adhere to accuracy, The KPI Institute developed a naming standard, which designates the symbols ”#” for units, ”%” for rates, and ”$” specifically for monetary value.
Maintaining data quality is essential to generate meaningful and effective KPIs. Reliable data ensures that business decisions are based on trustworthy information, resulting in improved marketing, increased customer satisfaction, enhanced internal processes, and reduced costs.
On the other hand, unreliable data can cause significant challenges. KPIs based on inaccurate data lead to wrong decisions, resulting in wasted resources and a negative impact on the organization’s performance. Poor data quality can impede the identification of trends or the accuracy of forecasts, leading to missed opportunities. In addition, it can hold back innovation, causing businesses to lose competitiveness.
Therefore, it is recommended that organizations prioritize data quality management and take actions to assess and improve data quality to enhance KPIs and drive business success.
Enhance your understanding of KPIs and read more about them on our KPI section.
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Editor’s Note: This article was originally published in Performance Magazine: Issue No. 26, 2023 – Data Analytics Edition and has been updated as of September 17, 2024.
