You’re asked about your experience with data quality. What’s the best way to answer?

All good points. DQ is a critical part of data posture at any company. Especially with data exploding over last few years, it’s important to have trusted good quality of data for decision making. DQ should not be an after thought but needs to be planned at inception to all the way to consumption. It should be defined, managed and measured in-line with data pipelines and ETLs using automated tools that also publish profiles and alert when quality metrics are not met. Similar to full stack developers, data engineers and architects should choose right platforms that are capable of implementing DQ.

Data Quality is keeping data in the exact character it was acquired/submitted from/by the data subject through maintaining its integrity by being complimentary to its rules of compliance

Crucial is answer sincerely: - Share how long/many times you were working in data quality area - Give examples 3 to 5 examples where you have done anything that is related to data quality in work, school or at home e.g. data cleansing, data standardization, data quality reporting and monitoring, fixing data quality issues, enhance processes for collection of data to prevent data quality issues, establish new validations, reference data management, etc.) - For each example, share actions that you did and key outcome and include some numbers i.e. number of record that you have fixed, number of issues that you have found, number of user for you DQ report. etc. - Summarize skills and tools that you have in DQ area thanks to these examples

DQ measures how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose, and it is critical to all data governance initiatives within an organization.

Data Quality is the heart of Science because for you to prove any theory you need facts. However for that we need to achieve it through CIA or confidentiality integrity and availability. There is a technological trend termed the FAIR digital objects this is to ensure that machine and people have access to quality data. For instance Climate Data, that is to say both scientist and machines should be able to make useful insights in long trends using quality data.

A persuasive approach to discuss your experience with data quality is to start with a business problem or opportunity. Drawing a picture of how tracking or improving DQ supported or achieved a key business outcome will demonstrate your understanding of why it’s important to any business.

You can complete some data quality certification which will help you stand out with your skill set. Trying to provide advise current industry problem

When discussing my experience with data quality, I emphasize my track record of driving tangible business outcomes through data excellence. My unique value proposition lies in my holistic approach, where I not only ensure data accuracy and consistency but also leverage data quality initiatives to unlock strategic insights and drive innovation. By integrating data quality practices seamlessly into the current business processes, I empower organizations to make informed decisions and enhance customer experiences. My ability to bridge the gap between technical data management and business objectives allows me to deliver measurable value by turning data into a strategic asset that fuels growth and success.