Can we achieve good quality data without implementing any enterprise data quality tool?

I have been working with few enterprises recently to define their Data Strategy and Data Quality roadmap. As the overall IT business has been impacted by global recession, one of the key challenges I have faced during defining the data quality roadmap is securing executive level funding to procure a standard Data Quality tool.

The enterprises understand the need of a defined Data Quality process backed by a strong capable Data Quality tool. However, IT teams get push back from business due to lack of approved funding . Now the important question is — can we get good quality of data without even a Data Quality tool?

I would say, yes, it’s possible. Let’s discuss some of the strategies and best practice recommendations which can help the enterprises achieve better quality data .

Data Governance: Establish a robust data governance framework within your organization. Identify core Data Domains (like finance, retail, products etc.) and critical business data elements and then define the RACI (Responsible, accountable, consulted and informed), means data ownership, data stewardship roles, and responsibilities. Having clear guidelines for data management and oversight can significantly improve data quality with guided control over the enterprise data. Assign data quality ownership to specific individuals or teams who are responsible for monitoring and improving data quality.

Data Profiling: Conduct regular data profiling and data quality assessments manually. You can use normal excel to do that. This involves analysing your data to understand its structure, content, and potential issues. You can also create simple scripts or queries to check for anomalies and inconsistencies.

Data Validation: Develop validation rules and checks for data at various touchpoints in your data pipeline. For example, validate data during data extraction, transformation, and loading (ETL) processes. This can help catch errors early in the data lifecycle. Enforce strict data entry standards and guidelines for data collection at the source systems. Ensure that data is consistently and accurately entered at the source. Implement validation checks and data entry forms that help users adhere to these standards.

Data Documentation: Maintain comprehensive documentation for your data sources. This should include metadata, data dictionaries, and data lineage information. Documenting the meaning and source of each data element helps in understanding and maintaining data quality. You can use excel/csvs to document the dictionary and share it across the enterprise teams through common channels like sharepoint, teams etc.

Data Cleansing: Establish data cleansing processes to correct and standardize data when issues are identified. Manual or scripted data cleansing can be performed as part of regular data maintenance activities. Again, excel , csvs or any SQL database can help in performing this. Allocate resources to periodic data quality improvement or cleansing projects. These projects can focus on specific data domains or sources that consistently exhibit poor quality.

Data Quality Metrics: Define key data quality metrics that are relevant to your organization and data. Regularly monitor these metrics and set thresholds for acceptable data quality. Metrics can include completeness, accuracy, consistency, and timeliness.

Data Quality Training: Train your data users and stakeholders about the importance of data quality and easy way to achieve it without a Data Quality tool. Make them aware of how data quality impacts decision-making and operations. Encourage a culture of data quality within your organization.

Data Quality Issue Log and Reporting: Maintain a centralised Data Quality Issue log for better governance of all the data quality issues across various business data domains. Generate data quality reports that highlight issues and progress in improving data quality. Share these reports with relevant stakeholders to maintain transparency.

While specialized data quality tools can streamline and automate many of these processes, implementing these manual strategies can still lead to good data quality if done consistently and with diligence. The key is to create a culture of data quality within your organization and treat data quality as an ongoing, collaborative effort.