What are the best ways to teach data engineers about data quality?

Get the perspective of the individuals or Stakeholders who are closest to the data and understand what data quality means to them. Examples: if you're working with a CRM, talk to the sales team who uses / enters the customer data. If you're working with a CMMS, talk to the technicians who respond to the work order requests. Not only can these individuals inform you on the specific data dimensions that drive their business processes the most, but you also can usually identify root causes for bad data quality through these conversations.

Data quality is essential for data engineers to do their jobs effectively. When data is accurate, complete, consistent, timely, and unique, data engineers can use it to build reliable data pipelines and applications that deliver valuable insights to the business. There are a number of ways to teach data engineers about data quality. One approach is to start with the basics, such as the different dimensions of data quality and why they are important. Data engineers should also learn how to assess data quality using a variety of tools and techniques. Once data quality issues have been identified, data engineers need to know how to fix them using data cleaning, data enrichment, or data integration.

Because data quality can also depend on users' understanding of the data, an important aspect is setting the right definitions. For example, a "user" does not need to be an "individual" or a "customer" but you might want to ensure all business lines understand this difference, and how that's represented in the data. Documentation and metadata is important here. Using the dimensions you objectively assess the data itself, but users might still think the data is garbage as they cannot use it. Suppose one wants to get data on customers, but there's only a users table. That person might think it's a bad quality table. Therefore take into account how data differs in meaning for different stakeholders.

Of course! Making educated judgements requires having high-quality data, which must be evaluated along a number of criteria. Completeness and consistency, in my opinion, are essential because they guarantee uniformity and prevent altered insights caused by missing data. However, accuracy should not be neglected, particularly in this big data era. It resembles the base of a solid data structure!

A few more dimensions that add depth to the evaluation of data quality, ensuring a comprehensive assessment: Uniqueness: This measures whether each piece of data is distinct and not duplicated within the dataset. Precision: It evaluates the level of detail and granularity in the data. Conformity: This dimension checks if the data complies with industry or organizational standards. Reliability: It assesses how dependable and trustworthy the data source is. Accessibility: This dimension considers how easily the data can be retrieved and utilized. Interpretability: It looks at how easily the data can be understood and used for analysis.

To teach data engineers about data quality dimensions, a multifaceted approach is effective. Begin with structured training programs covering the dimensions comprehensively. Incorporate practical examples that demonstrate real-world impact. Promote interactive workshops for hands-on experience, and establish feedback mechanisms (like a retrospective meeting) to ensure understanding and application of data quality dimensions.

the quality of data forms the bedrock for insightful decision-making. Ensuring top-notch data is no accident—it demands a deliberate and organized strategy. This is where Data Quality Frameworks come into play! Adopting a Data Quality Framework offers myriad advantages: from sharpening decision-making processes and boosting operational effectiveness to heightening customer contentment and ensuring adherence to regulations. Equally vital is the manner in which the stages of the framework are detailed and relayed, particularly in intricate projects that span long durations, encompass diverse teams, or have multifaceted scopes.

?? Embrace Data Quality Frameworks for Your Data Excellence ?? Data quality is the foundation of informed decision-making and meaningful insights in the digital age. To achieve data excellence, it's essential to have a systematic and structured approach in place. Enter Data Quality Frameworks! The benefits of implementing a Data Quality Framework are profound: better decision-making, improved operational efficiency, enhanced customer satisfaction, and regulatory compliance, to name a few. #DataQuality #DataExcellence #DataFrameworks

How the phases are documented and communicated also deserves attention, especially in very challenging projects, multidisciplinary teams involved, or that require a very long period for execution and completion. Nowadays, there are many tools available that make it possible to apply fundamentals such as consistency, clarity, and timelessness.

Community Mods: I'd like to point to another example where this Kiran Peddireddy guy has simply re-"contributed" content based on the previous author's contribution, almost to the T, by either re-wording it or putting it into ChatGPT.

Embrace a proactive approach by instilling a culture of data stewardship. Prioritize clear metadata management and maintain a robust lineage trail. Implement automated validation checks and foster collaboration between data stakeholders. Leverage AI-powered tools for anomaly detection and invest in continuous training to fortify data literacy. Emphasize transparency and accountability throughout the data lifecycle.

Understanding and applying the principles that data quality is not only a technical skill but also a mindset and culture can bring many benefits in the long run. By doing so, we can broaden our horizons and enhance our ability to comprehend and adopt the best practices that come with experimentation. Also, this will assist us in developing and communicating the best practices that will shape the future.

After working with several data teams, I've seen firsthand how critical data quality is for any analytics-driven enterprise. Embracing best practices in data quality is not just about adherence to standards; it's about cultivating a culture that prioritizes data integrity. For instance, my team once inherited a project riddled with data discrepancies, and it took a robust implementation of validation rules and a comprehensive audit to restore trust in the data. Hence, I recommend using automated tools for continuous data quality checks, and not to overlook the human element. Remember, data quality is a journey, not a destination; it requires ongoing vigilance and adaptability to changing data landscapes.

Teaching data engineers about data quality should start with helping them understand the business context around the data. Understanding what the data means can quickly help you understand for example when you have received the wrong data set even before any data cleansing or transformation. Understanding the business context around the data also guides the design of transformation to reduce the probability of data transformation introducing data quality issues. For example understanding what metric data is collected in , stored in and consumed in across multiple system/business areas will help engineers design the right transformation logic for consolidating data from those systems

Kinda satirical, but also true, if you want to show data engineers why data quality matters and why it should be implemented from the start, just tell them to work on an enterprise level system that has no data quality checks or data quality control. When they try to implement anything on a system like that, they will soon fight for there to be any kind of data quality control or checks.

We are facing a massive wave of innovation. In addition to the benefits, this wave also brings numerous challenges for those who work with data. For example, the Vesuvius Challenge, a large library of antiquitiy where we have many papyri that are 1940+ years old, charred, and look like pieces of charcoal. How to read the contents of the papyrus without opening it? Infrared spectroscopy data? CT scan data? X-ray synchrotron data? What do I need to do with this data to find a solution? This is just one example, challenging and fascinating at the same time.

I relate thus I confirm what my fellow Tomislav said "satirically": indeed nothing beats experience. Once Data engineers are confronted to the raw enterprise level data, they will soon understand and fight themselves for better data "sanity checks". The name says it all haha

I think the while the DQ implementation belongs in the Data Engg potentially - DQ is owned by the business - as is the Data itself - it is important to lay the context of the Data and its usage for the business process in questions. Data Quality - by default does not imply that the data is bad - in most cases, especially in enterprises, it could mean there is a lot of legacy data from other contexts also in the mix -may not be relevant in this context - i.e. business context that is being analyzed - hence may need to be excluded so as not to skew the outcomes. In this light, knowing the context becomes very critical - just so determination can be made what to include and what to exclude.