How Data Quality Impacts the Achievement of Your OKR Targets?

Overview

OKRs are critical because they provide a structured yet flexible way to set, track, and achieve meaningful goals that propel a company forward. They align individual and team efforts with the company’s vision, create accountability, and drive focus on results, all of which are key factors in long-term success.

The clarity of Objective helps everyone in the organization, from leadership to individual contributors, understand what the company's top priorities are and focus on activities that drive toward those goals. Objectives articulate the “what” the company wants to achieve, while Key Results define the measurable steps needed to reach those objectives. When teams and departments align their OKRs with company-wide objectives, it fosters collaboration, reduces siloed efforts, and ensures that resources are allocated toward the most critical tasks.

OKRs are quantifiable, which makes it easier to track progress over time. Key Results provide concrete benchmarks that indicate whether the team or company is on track to meet its objectives. OKRs emphasize results over activities. Instead of focusing on completing a long list of tasks, teams focus on achieving key outcomes that will have a measurable impact on the business.

OKRs are not just about success; they also allow companies to learn from their failures. If an objective is not met, teams can analyze why, identify areas for improvement, and adjust their approach in future cycles.

Why Quality of data is critical to success of OKRs

There are many factors that contribute to staying ahead of OKRs, but DATA plays a critical role to achieve OKR. The quality of data plays a crucial role in the success of OKRs (Objectives and Key Results). Data serves as the backbone for setting, measuring, and evaluating progress toward goals. Poor data quality can undermine the effectiveness of the OKR process, while high-quality data can greatly enhance its impact.

If the data used to track progress is flawed or outdated, the team's perception of how well they are performing can be skewed, leading to misguided decisions. For example, a sales team sets an OKR to increase customer acquisition by 15%, but if the customer data is inaccurate (e.g., duplicates, incorrect customer profiles), the actual progress may be under- or overestimated.

When data quality is high, teams trust the numbers and are more likely to take ownership of their OKRs. If data is unreliable, teams may question its validity, leading to a lack of confidence in the OKR system and lower accountability. For example, if teams don’t trust the sales metrics used to define Key Results, they may not feel responsible for achieving those results, which can lead to disengagement.

The effectiveness of OKRs depends on the ability to regularly track and assess progress toward Key Results. Poor data quality can cause delays in tracking, incorrect reporting, or even missing key insights, making it difficult to determine if an objective is on track. For example, If data pipelines are not reliable, marketing teams might not get timely updates on conversion rates, leading to missed opportunities for optimization and delayed course correction.

Why is your master data critical to success of OKRs?

Master Data Management (MDM) is foundational in ensuring that critical data is organized, accurate, and consistent across an organization’s systems. When paired with Generative AI (Gen AI), the power of MDM is elevated, allowing businesses to prepare data more efficiently for insight and strategic decision-making. In a world where data is generated from countless sources—such as customer interactions, sales transactions, and supply chain operations—ensuring that this data is clean, standardized, and readily accessible is critical for achieving insights. MDM provides the framework for consolidating and governing this data, while Gen AI automates the process of refining, validating, and enriching the information to ensure it is of the highest quality.

Gen AI can work alongside MDM to detect inconsistencies, duplicates, and anomalies within datasets, such as customer profiles, product information, or financial data. For example, if customer contact details are incorrect or missing, Gen AI can suggest appropriate corrections or even fill in missing information by cross-referencing external databases or similar entries. This automation helps organizations prepare their data for analysis and insights much faster than traditional manual processes. Furthermore, by integrating Gen AI into the MDM system, businesses can establish an ongoing process of data refinement, ensuring that the data remains accurate and actionable over time.

The combination of MDM and Gen AI is particularly valuable when it comes to achieving data-driven OKRs (Objectives and Key Results). OKRs rely heavily on timely and accurate data to measure performance and track progress toward organizational goals. MDM ensures that the data feeding into these OKRs is consistent and reliable, while Gen AI enhances this by continuously refining and improving the dataset. For instance, if an organization has an OKR related to customer retention or sales growth, Gen AI can help ensure that customer data is accurate and up to date, thus enabling precise tracking of customer behavior, preferences, and engagement. As a result, the organization can set more accurate Key Results and rely on real-time insights to adjust its strategies in order to meet its objectives.

Use case studies – Impact of poor-quality data to OKR?

A recent study found that approximately 8% of total invoices were returned by customers. Of these returned invoices, 31% were due to inaccurate customer billing master data, such as incorrect addresses, inaccurate tax calculations, or wrong billing zip codes. The remaining 69% of returned invoices were due to transactional data issues, such as errors in billing hours, payment terms, or non-billable work. In many countries, postal codes are used to calculate sales tax, further highlighting the importance of accurate customer data.

To test the hypothesis, a dataset of around 29.5 thousand invoices was analyzed. Among these, about 2.4 thousand invoices were found to be inaccurate and required rebilling. In response, an initiative was launched to standardize and clean up the master data and to establish regular data quality monitoring. This effort aimed to ensure that customer billing addresses and contact data met the necessary quality standards and expectations.

Before the data was cleansed and standardized, the proportion of inaccurate invoices stood at 8%. After the data was corrected, the percentage of inaccurate invoices dropped to 1%. Importantly, the remaining 1% of inaccurate invoices were found not to be related to customer master data issues. To validate this finding, a hypothesis test was conducted, and the results were statistically significant.

The hypothesis test was a two-tailed test, with the null hypothesis (H0) being rejected if the proportion of inaccurate invoices after correcting the customer data was significantly high:

Null hypothesis (H0): P1 = P2

Alternative hypothesis (H1): P1 ≠ P2

Difference in P value is calculated using the following equation:

Here, P1 represents the proportion of inaccurate invoices before customer data was corrected, and P2 represents the proportion after the correction has been made, with n1 and n2 being the respective sample sizes.

Standard error (Se) is calculated using the following equation:

The calculated P-value is 0.0001, which is lower than the significance level of 0.05. This means the null hypothesis was rejected. There is significant improvement in invoice accuracy after customer data has been corrected and a governance framework with continuous data quality monitoring and remediation actions.

If an organization defines an OKR for invoicing accuracy at achieving this OKR would require the establishment of a robust data governance practice. In addition, maintaining high-quality customer data would be essential for sustaining this level of invoicing accuracy

Achieve Your OKR Targets with Effective Data Governance

Microsoft Purview's data governance integrated experience enables organizations to define their OKRs and link them to relevant data products, helping to effectively govern these products and related data assets to stay on track and ahead of OKR targets. OKRs link data products directly to real business objectives to cross the gap between the business and the data estate.

Data governance isn't just an IT task or engineering best practice, it's a critical part of value generation. In order to make use of your data, your data estate needs to be well maintained, and the work can be shared out among the experts that know that data best.

The success of OKRs relies on the ability to consistently monitor and assess progress toward Key Results. Poor data quality and inadequate governance can lead to delays in tracking, inaccurate reporting, or even missed insights, making it challenging to gauge whether objectives are being met. To effectively govern the data that influences your organization's OKRs, follow these simple steps:

• Define and configure your OKRs within Microsoft Purview
• Add key results to track progress
• Link data products that have a direct impact on your OKRs
• Manage the quality of data within these products and associated data assets
• Apply data quality rules to key data attributes that directly affect OKR calculations

By managing your data products, governing linked data assets, and maintaining high data quality, you’ll gain clear visibility into your OKRs. One of the key advantages of OKRs is their focus on outcomes rather than outputs. Achieving meaningful outcomes requires data-driven decision-making throughout the OKR cycle. If your data is incomplete, inconsistent, or inaccurate, it hinders your ability to make informed decisions, causing your organization to fall short of its OKR targets.

Defining processes, rules, and accountability for maintaining data quality—ensures that the data driving OKRs is reliable. This governance structure helps mitigate risks, such as data errors or inconsistencies, that could derail OKR progress.? Purview data governance integrated data quality experience enables organizations to use end to end data governance experience to manage all critical business concepts like data product, glossary terms, policies, OKRs, Critical Data Elements, and custom attributes.?

In addition, Microsoft Purview is integrated with number of Master data Application to provide end to end master data management and governance experience in Purview. Master data often needs to be integrated from various systems (e.g., CRM, ERP, HR) to provide a holistic view of performance. Proper integration of master data across these systems allows for a more comprehensive measurement of OKRs and helps avoid silos.

Set up alerts to notify you of potential risks that could impact achieving your OKR targets.

You can configure alerts to notify you when data quality thresholds fall below expectations, directly impacting your OKRs. For more information, refer to this document: Microsoft Purview data quality alerts and notifications | Microsoft Learn .?

Additionally, you can configure alerts in OneLake, as all metadata is available for subscription in Fabric OneLake for analytics and insights. To set up alerts in Fabric OneLake, you will need to use Fabric Data Activator to configure rules and set up notification channels, such as email or Microsoft Teams.

Summary

OKRs are essential because they provide a structured yet flexible framework for setting, tracking, and achieving meaningful goals that drive a company forward. They align individual and team efforts with the company’s overall vision, create accountability, and focus attention on results—key factors in long-term success. OKRs in Microsoft Purview are trackable business objectives linked to governance domains and data products, emphasizing the value of business data.

Data quality directly impacts every stage of the OKR process, from goal setting to progress tracking and evaluation. High-quality data ensures that OKRs are based on accurate insights, fostering trust and enabling teams to make informed, data-driven decisions. Conversely, poor data quality can cause misalignment, lead to incorrect measurements, and result in missed opportunities, ultimately diminishing the effectiveness of the OKR framework. Strong data governance and a reliable data infrastructure are critical to the success of OKRs.

Master data plays a crucial role in OKR success by ensuring goals are found on accurate, consistent, and high-quality data. By investing in strong master data management and governance practices, organizations can more effectively track progress, make informed decisions, and ultimately achieve their OKRs.

In the age of AI, companies must invest in creating a data-driven culture to meet their Objectives and Key Results. A federated data governance strategy should be established to engage and empower every part of the organization, helping them collectively build and execute a robust data strategy. Microsoft Purview enables organizations to drive federated data governance by providing the tools necessary to configure and implement practical, best-in-class data governance practices.

MDM and Gen AI together create a data ecosystem that supports advanced analytics and reporting, which are critical for evaluating progress toward OKRs. By standardizing and governing the data with MDM, and then applying Gen AI to derive deeper insights, organizations can uncover hidden patterns and trends that would otherwise go unnoticed. This enables more informed decision-making, allowing businesses to stay agile and responsive in pursuit of their objectives. Ultimately, the synergy between MDM and Gen AI fosters a data-driven culture where high-quality data powers every stage of the OKR cycle, from goal setting to execution and evaluation, ensuring that teams remain aligned and focused on achieving meaningful business outcomes.