What data quality frameworks guide your data engineering decisions?

1 Dimensions of data quality

One way to approach data quality is to use a set of dimensions or criteria that describe the characteristics of good data. For example, some of the widely used dimensions are accuracy, completeness, consistency, timeliness, validity, and uniqueness. Each dimension can be defined and measured in different ways depending on the data source, type, and purpose. For instance, accuracy can refer to the degree of conformity to a standard or a reference, completeness can refer to the coverage or availability of data elements, and timeliness can refer to the currency or freshness of data.

2 Data quality assessment

Data quality can be approached through a systematic process of assessment, which involves collecting, analyzing, and reporting on data quality issues and their impacts. This process can help identify the root causes of data quality problems, prioritize the most critical ones, and evaluate the effectiveness of data quality improvement actions. Generally speaking, the steps of data quality assessment include defining the scope and objectives of the assessment, establishing data quality requirements and expectations, selecting data quality indicators and metrics, collecting and validating data, analyzing and interpreting results, and reporting and communicating findings and recommendations.

3 Data quality improvement

The ultimate goal of data quality assessment is to improve the data quality and achieve the desired outcomes. To do this, a continuous process of planning, implementing, monitoring, and reviewing data quality actions and strategies must be undertaken. Common data quality improvement techniques include data cleansing (correcting, removing, or replacing inaccurate, incomplete, inconsistent, or invalid data), data enrichment (adding, updating, or enhancing data with additional information or attributes), data standardization (applying consistent rules, formats, and conventions to data), data integration (combining data from different sources and resolving data conflicts), and data governance (establishing policies, roles, and responsibilities for data quality management).

4 Data quality maturity model

A data quality maturity model is a framework that helps you assess the current and desired state of your data quality capabilities and practices. It also assists in determining the gaps and actions needed to reach higher levels of maturity. This model usually consists of several stages or levels that describe the degree of data quality awareness, alignment, measurement, improvement, and governance. For instance, one of the commonly used models is the DAMA-DMBOK Data Quality Maturity Model, which has six levels: Initial, Repeatable, Defined, Managed, Optimized, and Innovative. At Level 1, data quality is not recognized as an issue or priority. At Level 2, it is addressed on a project-by-project basis. Level 3 involves defining and documenting data quality with standards and guidelines. Level 4 involves measuring and monitoring data quality with metrics and reports. Level 5 involves improving and optimizing data quality with continuous feedback and learning. And at Level 6, data quality is leveraged as a strategic asset and competitive advantage.

5 Data quality best practices

To achieve and maintain high data quality, you need to follow certain best practices that can help you avoid or minimize data quality issues and their impacts. These include defining your data quality goals and expectations clearly, involving stakeholders and users in the data quality process, validating your data sources and inputs, implementing data quality checks and controls throughout the data lifecycle, documenting your data quality rules, processes, and results, reviewing and updating your data quality policies and procedures regularly, and educating and training your data quality team and users on data quality concepts and tools.

6 Data quality tools

Data quality tools are software applications or platforms that can help you perform data quality tasks and functions more efficiently and effectively. These tools offer a range of features and functionalities, such as data profiling, cleansing, enrichment, matching, monitoring, and governance. Popular data quality tools include Pandas Profiling, Great Expectations, Trifacta, Talend, and Informatica. By using these tools, you can analyze and understand the structure, content, and quality of data; apply rules and transformations to cleanse it; access and integrate external sources; identify and link duplicates or related records; track and report on performance; and manage and enforce policies and standards.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?