Identify through collaboration with both business and IT the critical attributes requiring a high level of data quality. Then quantify the business impact and cost of poor data quality with these attributes in order to gain support. Example, poor data quality with invoice contacts and addresses results in a substantial percentage of invoices being returned resulting in delays in revenue payments and cost of reprocessing.

Real time data excludes static/master data generally , for such situations implement preventative controls on data capture than DQ controls in a data flow. For Real time data flows divide your datasets as 1. Columns which are absolute to run the business/transaction( e.g without email address you cant send confirmation) - create preventive controls that real time messages fail. 2. For the rest of Critical Data take a risk based approach to check DQ after consumption(e.g. you may need a field for doing analytics later ) and have a feedback loop to correct them post facto.

Begin by identifying essential quality attributes, focusing on accuracy for key metrics and completeness for critical fields, while allowing flexibility in less essential areas. Implement a tiered validation system, where basic checks happen during ingestion, and deeper validations occur afterward, minimizing delay. Use automated anomaly detection to catch issues swiftly without disrupting data flow. Establish feedback loops to adjust quality controls based on real-time insights, and leverage dashboards for continuous monitoring. This approach helps maintain both quality and speed efficiently.

I believe in uncompromising data quality standards rather than seeking a middle ground. One strategy is to scrutinize the necessity of each data point collected—reverse engineer from the desired outcomes detailed in your roadmap. Ensure every piece of data is essential and significantly contributes to delivering high-quality results. Additionally, implementing robust data validation processes can help balance real-time demands while maintaining quality. Automated checks and real-time monitoring can ensure data integrity without sacrificing speed.

Clear data quality is essential for building trust with stakeholders, but timely access to that data is equally important for informed decision-making. Therefore, finding a balance between these two factors is key to effective data governance. To achieve this, consider defining minimum data quality standards and acceptable delays for different metrics or information. This allows you to prioritize what matters most and potentially sacrifice quality or speed for metrics that stakeholders don’t rely on as heavily. By categorizing and tiering your data, you can facilitate discussions aimed at finding that middle ground. This structured approach will help ensure both trust and timely access to the data stakeholders need.

Balancing data quality with real-time demands is like walking a tightrope—hesitate, and you fall behind; rush, and you sacrifice accuracy. I encourage "fit-for-purpose" quality, not perfection—ensure the data is good enough to drive decisions in the moment, then refine as needed. Leverage automated checks and smart algorithms to catch anomalies on the fly, but don’t let perfectionism paralyze speed. Set clear SLAs for data latency and quality, and hold stakeholders accountable for trade-offs. It’s all about ruthless prioritization: deliver just-in-time data that's just right for the task.

Meeting high data quality standards while delivering real-time data demands requires implementing automated data validation & cleansing processes directly within data pipelines to catch errors early. Use data governance tools & policies that enforce accuracy, consistency & completeness, even as data flows rapidly across systems. By leveraging machine learning algorithms for anomaly detection & prioritizing critical data quality metrics, you can balance speed with accuracy. Using continuous monitoring & feedback loops helps maintain data integrity, enabling real-time data that meets reliability standards essential for fast, informed decision-making.

We should address the clash between data quality standards and real-time data demands by finding a balanced approach. We implement adaptable data quality frameworks that ensure core standards are met without compromising speed. For example, we can introduce real-time data validation processes, allowing critical checks without delaying data access. This approach ensures both high data quality and timely decision-making.

To balance data quality standards with real-time demands, Sarah introduced tiered data quality levels. * For critical, time-sensitive data, she set up automated, lightweight quality checks to maintain speed without sacrificing essential accuracy. * For less urgent data, she implemented more rigorous validation processes to ensure deeper quality assurance. * Sarah also worked with her team to establish data monitoring dashboards, which allowed them to spot and resolve quality issues in real time without slowing down the data flow. This flexible approach maintained high standards while meeting the pace of real-time needs, ensuring both quality and speed.