How do you ensure data validation is aligned with your business objectives and stakeholder expectations?

A clear understanding on business objectives and on the expectations of data owners and consumers would help define the data requirements for validation. A set of SQL scripts can be prepared and run periodically to monitor the metrics and that would throw light on the reach towards objectives. Data profiling (periodical) results can also help disruptions and progress, as part of validation, towards the objectives.

I emphasize not only answering key questions relevant at the moment but also aligning them with the overall business strategy. This includes identifying metrics that not only measure performance but contribute directly to informed decision-making, which nowadays is one of the most challenging things to do. In the business environment, things can drastically change from one day to another.

Working with the data and learning data analytics, I realized that to understand that data validation is aligned with business objectives and stakeholder expectations, there are a few things to take into consideration: You must define the data requirements , which means what are you going to do with it, design a plan and what to do with the results of the analysis of it by making sure you have an effective data process.

To ensure data validation aligns with business objectives and stakeholder expectations, establish clear criteria and standards for data quality based on the specific needs and goals of the organization. Engage stakeholders early in the validation process to understand their requirements and expectations regarding data accuracy, completeness, and reliability. Implement validation procedures that address these criteria, ensuring that data meets the defined standards before being used for analysis or decision-making. Regularly communicate with stakeholders to gather feedback and make adjustments to validation processes as needed, ensuring alignment with evolving business needs and expectations.

What I would do is conduct a thorough assessment of data requirements and business goals. Engage with stakeholders to understand their expectations and incorporate key metrics. Define clear validation criteria based on business logic and compliance standards. Implement checks for accuracy, completeness, and relevance. Regularly update the plan to adapt to changing business needs and emerging data sources. By closely involving stakeholders, addressing business-specific needs, and maintaining flexibility, your data validation plan becomes a strategic tool tailored to meet organizational objectives and stakeholder expectations.

The data validation results reveal the status of the "journey" towards achieving the business objectives and expectations of the business and data owners. The results need to be monitored to understand whether we are in "off-track" or "on-track". We may need to enhance the validation methods and/or scripts, that we learn during monitoring.

In any place, fostering a culture of continuous improvement is critical. In my experience, innovation happens after several small improvements and not after one sigle brainstorming session. Our tendency to get excited over projects and their completion sometimes can prevent us from getting the greater value. It is the day-to-day continuous work and small improvements that will bring the most value over a long period of time. (Contrary to the silver-bullet solutions that most are looking for nowadays)