How do you educate yourself on data quality?

I think here are few things which should be followed by anyone wanting to learn about data quality: 1. Study fundamentals: Understand data quality dimensions like accuracy, completeness, consistency, timeliness, and uniqueness. 2. Learn tools & techniques: Familiarize with data profiling, cleansing, and validation tools. 3. Real-world cases: Analyze data quality case studies for practical insight. 4. Network: Join data governance forums and groups. 5. Training: Attend workshops, courses, or obtain certifications. 6. Continuous learning: Stay updated with latest practices and trends in data quality.

To get the basics of data quality, you start by deciding what good data should look like. Then, you check the data for problems and clean up any bad you find. You use measurements to see how well the data meets your standards. Enforcing data quality rules ensures data stays clean and accurate, especially when there's a focus on data remediation. Ensuring people understand the value of quality data and fostering a culture where everyone is responsible for maintaining data integrity is crucial. Consistent evaluations and creating a feedback system with data users ensure its quality, aligning it with the organization's goals.

To learn about data quality and skills, the basic understanding of data quality dimensions is important. Common questions, in my experience, are on the difference between Integrity and Consistency and on Timeliness. "How does timeliness belong to data quality dimension?" and "How to identify the timeliness issues?" are some questions to get answered for understanding the basics.

The best way to understand data quality is to live the dream. - Collect data - Sort data - Integrate data - Solve business problems with data Once you understand the nuances and have experienced various quality issues, you are on a better path to being a senior data professional.

A solid grasp of the foundational principles is imperative before delving into the nuances of data quality. The resources mentioned, like Laura Sebastian-Coleman's Data Quality Assessment Framework and Thomas Redman's Field Guide, are excellent starting points. I would also recommend newcomers to engage with communities and forums dedicated to data quality discussions. The journey towards ensuring data quality is a collaborative effort, and staying updated with the latest practices and methodologies in the field is crucial. Additionally, considering a hands-on approach by working on small projects or participating in relevant workshops can also provide a practical understanding, complementing the theoretical knowledge gained from reading.

Every organization has a different level of application of data quality in their environment. Over the due course , I have learnt the hard way and summarizing it - Understand the business problem and impacted stakeholders - Report it in a simplistic manner - Focus on the resolution and it matters the most to business which includes liasoning , change management etc There are several other aspects of a data quality framework already highlighted which can be useful once you have really helped solving business problems.

When I started working on Data Quality space in 2008, there were not many resources available to learn about Data Quality Dimensions. Unlike the yesteryears, there are plenty of resources available presently to develop skill on Data Quality. Apart from experts, there are many online communities and forums that share the trends, tools, technologies and concepts to learn on this subject.

To learn from data quality experts, ideal way is to start by following leaders on social media platform such as LinkedIn, review case study reports, participate in online forums, take active part in your local professional networking groups and attend workshops led by experts. These approaches provide valuable insights and practical knowledge.

Engaging with seasoned experts and immersing oneself in the thriving communities around data quality are excellent strategies for upskilling. Arkady Maydanchik, Danette McGilvray, David Loshin, and Gwen Thomas have indeed contributed immensely to the field. Their insights can provide a wealth of knowledge to anyone looking to deepen their understanding of data quality. The dynamic discussions and case studies shared in such communities can significantly expedite the learning curve. Additionally, active participation in workshops and conferences not only provides learning opportunities but also opens doors for collaborations and professional growth

Practicing the skills using technical tools is one of the ways to learn data quality. The focus can be on what kind of profiling options that the tool provides, how a business rule can be converted into a technical rule, how the results are displayed and what other features present with the tool. All these can be learnt when practicing on the tools. I personally have experience on Informatica and Talend (Profiling component).

Hands-on experience is crucial when it comes to mastering data quality management. Utilizing tools like Talend Data Quality, Informatica, and IBM InfoSphere QualityStage on real or simulated datasets will not only hone your skills but also give you a practical understanding of the challenges and solutions in maintaining data quality. Online courses are fantastic resources to guide you. They often provide a structured learning path and access to experienced instructors, which can be invaluable. Furthermore, I would recommend seeking out mentorship or joining study groups to foster a collaborative learning environment. It’s through the practical application and shared experiences that the nuances of data quality are truly understood.

From my experience with Databricks: Stay up to date with Databricks blogs: Follow Databricks blogs and platform updates regularly to stay up to date on new features and advancements in data quality support. Attend Databricks webinars and trainings: Attend Databricks data quality webinars and trainings to learn about best practices and new trends. Participate in Databricks community forums: Join Databricks community forums to connect with industry experts and peers to share insights and experiences on data quality strategies and challenges. By using Databricks resources, organizations can keep up to date with the latest trends and advances in data quality to ensure they remain competitive and effective in their data management.

Applying the acquired knowledge in real-world scenarios is the cornerstone of mastering data quality. It’s through the lens of practical challenges that the theoretical knowledge truly finds its value. Sharing insights and solutions with colleagues and stakeholders not only fosters a culture of continuous improvement but also underscores the importance of data quality in driving informed decisions. Moreover, it's an opportunity to establish oneself as a thought leader in the domain, contributing towards not only personal growth but also the broader data community. Sharing case studies and lessons learned through platforms like LinkedIn or at professional meetups can also significantly contribute to the collective wisdom on data quality

The article starts with references to DQ Expectations. This is key to understanding quality is about understanding and defining the consumer’s expectations. What is fit for their purpose? Learning how to define and quantify their expectations is critical. What are the critical elements for their decision making? Knowing how to get the answers to the above questions, starts our learning and experience journey.

Knowledge about Data Quality can be learned by reading about Data Quality concepts like - Data Quality Dimensions -Data Quality KPI etc The books which I recommend for learning about concepts of Data Quality are : Data Quality by Prashant Prashanth H Southekal Driving Data Quality by Andrew Jones. Once done try to implement the concepts of these books in your job, by introducing new data quality checks, measuring data quality of current data sets, and putting in processes to improve the data quality. Basic knowledge of SQL and Python can help in this.

Every organisation should continuously enhance the knowledge and expertise of the teams involved in data quality. Organisations should invest in periodic data quality trainings, certification, peer best practices and cross functional knowledge share. One of the best ways to enhance your knowledge would be to revisit data issues that the organisation has come across recently. It would be a good practice to understand the lineage of the data issue, the remediation pathway and strategic and tactical solutions adopted. These issues would give an insight on how best to set up data quality checks.

Beyond traditional learning methods, engage in community discussions, forums, and groups focused on data quality. Sharing experiences and challenges with peers can provide practical insights and innovative solutions. Additionally, consider contributing to open-source data quality projects or writing articles to deepen your understanding and establish your expertise. Embrace a holistic approach to learning that includes both formal education and community engagement.

Learning other skills that supports to have good grip on data quality. For example, regular expressions. When writing some technical rules, regular expressions can be handy and it can help. Writing efficient SQL scripts can also support data quality skills.

Because data changes rapidly today with technological changes and human curiosities, so too does data quality. The thing I would emphasize is each time you run into a new type of dataset, ask yourself what is intuitively in play with respect to data quality. Prime example: spatial data. Spatial data can present modifications to how we understand two key data quality dimensions: duplication and consistency. They present issues that maybe we wouldn't otherwise think about. Duplication in spatial data might not just be identical rows, but it might very well be an oversaturation in an area, reflecting the same idea. When I go to do stats work on this, that oversaturation can cause the same problems that data duplication would.