What are some common data quality issues in dimensional modeling and how do you resolve them?

1 Data profiling

Data profiling is the process of analyzing the source data to understand its structure, content, and quality. Data profiling helps you identify and document the data quality issues, such as missing values, duplicates, outliers, errors, inconsistencies, and violations of business rules. Data profiling also helps you define the data quality dimensions and metrics that you want to measure and monitor in your dimensional model, such as completeness, validity, accuracy, timeliness, and uniqueness. Data profiling is an essential step before you design and implement your dimensional model, as it informs your decisions on how to transform, cleanse, and integrate the data from different sources.

2 Data cleansing

Data cleansing is the process of correcting or removing the data quality issues that you identified during data profiling. Data cleansing can involve various techniques, such as standardizing, formatting, matching, deduplicating, enriching, and validating the data. Data cleansing can be done at the source level, before loading the data into the dimensional model, or at the target level, after loading the data into the dimensional model. Data cleansing can also be done as a batch process, periodically or on demand, or as a real-time process, as the data is being loaded or accessed. Data cleansing improves the quality and reliability of the data in your dimensional model, and reduces the risk of errors and inconsistencies in your analysis and reporting.

3 Data integration

Data integration is the process of combining the data from different sources into a coherent and consistent dimensional model. Data integration can involve various challenges, such as dealing with heterogeneous data formats, schemas, and semantics, resolving data conflicts and discrepancies, and ensuring data lineage and traceability. Data integration can be done using various methods, such as extract-transform-load (ETL), extract-load-transform (ELT), or data virtualization. Data integration can also be done using various approaches, such as conformed dimensions, factless fact tables, bridge tables, or snowflake schemas. Data integration requires careful planning and testing to ensure that the data in your dimensional model is aligned with the business requirements and expectations.

4 Data governance

Data governance is the process of defining and enforcing the policies, standards, and roles for managing the data quality in your dimensional model. Data governance helps you establish and maintain the trust and confidence in your data, as well as comply with the regulatory and ethical requirements. Data governance involves various activities, such as defining the data quality objectives and indicators, assigning the data owners and stewards, implementing the data quality rules and controls, monitoring and reporting on the data quality performance, and resolving the data quality issues and incidents. Data governance also involves creating and maintaining the data quality documentation and metadata, such as data dictionaries, data catalogs, data lineage maps, and data quality reports.

5 Data testing

Data testing is the process of verifying and validating the data quality in your dimensional model. Data testing helps you ensure that the data in your dimensional model meets the data quality specifications and expectations, as well as detect and fix any data quality defects or anomalies. Data testing can be done at various stages of the dimensional modeling process, such as during data profiling, data cleansing, data integration, and data loading. Data testing can also be done using various techniques, such as data quality audits, data quality checks, data quality dashboards, and data quality alerts. Data testing is an integral part of the data quality assurance and improvement cycle in your dimensional model.

6 Data maintenance

Data maintenance is the process of updating and refreshing the data in your dimensional model. Data maintenance helps you keep your data current and relevant, as well as cope with the changes and evolution of the data sources and business needs. Data maintenance can involve various tasks, such as adding or removing data sources, modifying or enhancing data transformations and calculations, adding or changing data attributes and hierarchies, and archiving or purging old or unused data. Data maintenance can also involve various strategies, such as incremental or full data loading, slowly changing dimensions, surrogate keys, or history tables. Data maintenance requires regular monitoring and evaluation to ensure that the data in your dimensional model remains accurate, consistent, and usable.

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?