What are some data quality standards and frameworks that you follow or refer to?

2 Data quality assessment

Another way to approach data quality is to conduct a data quality assessment, which is a systematic process of evaluating the state and suitability of your data for your intended use. A data quality assessment can involve various methods and techniques, such as data profiling, data auditing, data cleansing, and data monitoring. Data profiling is the process of collecting and analyzing metadata, such as data types, formats, values, ranges, distributions, and relationships. Data auditing is the process of checking and verifying the accuracy and validity of your data against predefined rules or standards. Data cleansing is the process of identifying and correcting errors, inconsistencies, duplicates, or outliers in your data. Data monitoring is the process of tracking and reporting the changes and trends in your data quality over time.

3 Data quality frameworks

A third way to approach data quality is to adopt or adapt a data quality framework, which is a set of principles, guidelines, best practices, and tools that can help you plan, implement, and manage your data quality activities and processes. There are various data quality frameworks that you can follow or refer to, depending on your domain, industry, or organization. For example, the DAMA International Data Management Body of Knowledge (DAMA-DMBOK) is a comprehensive framework that covers all aspects of data management, including data quality. The ISO 8000 Data Quality Standards are a series of standards that specify the requirements and procedures for data quality management. The Total Data Quality Management (TDQM) framework is a methodology that focuses on the continuous improvement of data quality through the alignment of data producers and consumers.

5 Data quality challenges

A fifth way to approach data quality is to recognize and address the common challenges and issues that you may encounter as a data scientist. Data quality challenges can arise from various sources and factors, such as data complexity, data diversity, data volume, data velocity, data veracity, data security, data governance, and data ethics. These challenges can affect the quality of your data at different stages of your data lifecycle, such as data collection, data storage, data processing, data analysis, and data communication. Therefore, you need to be aware and proactive in dealing with these challenges and ensuring that your data quality meets the expectations and standards of your data users and stakeholders.

6 Data quality benefits

A sixth way to approach data quality is to appreciate and communicate the benefits and value of having high-quality data for your data science projects and applications. Data quality benefits can be tangible or intangible, such as improved efficiency, productivity, performance, accuracy, reliability, usability, trustworthiness, compliance, and customer satisfaction. Data quality benefits can also be measured and quantified using various metrics and indicators, such as error rates, completeness rates, consistency rates, timeliness rates, relevance rates, and accessibility rates. By demonstrating and highlighting the benefits and value of data quality, you can increase the awareness and support for your data quality initiatives and efforts.