Data quality assessment overview

Data quality assessments help you avoid introducing errors into your database. Learn how they work and why you need them.

Data quality assessments have the same goal that data quality management frameworks have: to ensure data is of good quality. However, unlike data quality management programs, DQAs are often required when working with government authorities like USAID, environmental authorities like the EPA or health organizations like the WHO.

While processes certainly overlap, each organization has its own processes for developing DQAs. The main purpose of these assessments is to support decision-makers by assuring that the type, quantity and quality of data presented have been assessed before making a decision.

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Like other approaches to data quality management, DQAs offer many benefits to data-driven companies. They provide better data, which leads to better performances and decisions; they help organizations meet compliance and governance requirements; and they offer scientific evidence that the data being used is of the highest standards. The rest of this guide offers a deep dive into data quality assessments, how they work, and how your organization can perform one.

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What is a data quality assessment?

A data quality assessment involves creating a stand-alone report that contains evidence of the processes, observations and recommendations found during data profiling.

Data quality assessments look at where the data is coming from, how it flows within an organization, if the data is of good quality and how it is used. Additionally, the assessment identifies gaps in data quality, what type of errors the data has, why it has that level of quality and how to fix it.

Data quality assessments serve as a blueprint for data teams and leaders. Data quality checklists and processes set clear roles and steps for organizations to take control of their data with visualization and tools. Data sets, subsets, workflows and data access are all evaluated.

The main challenges of these assessments today are related to the significant amounts of data organizations generate daily from different sources. Misconfigured, inaccurate, duplicate, hidden, ambiguous, obsolete or incomplete data are common data quality problems. Companies are also struggling with defining the standards for what good data quality is and finding skilled data experts that can operate the right technologies to drive the process forward.

How do you assess data quality?

There are many different methods to assess data quality that include data profiling, normalization, pre-processing and/or visualization. DQAs are conducted to make sure data meets five quality standards, according to USAID:

Data quality standards DQAs need to meet

• Validity: Data should represent the intended result clearly and adequately.
• Integrity: Data should have safeguards to minimize the risk of bias, transcription error or data manipulation.
• Precision: Data should have a sufficient level of detail to permit informed management decision-making.
• Reliability: Data should reflect stable and consistent data collection processes.
• Timeliness: Data should be available at a useful frequency, should be current and should be fit for use in management decision-making.

Data teams must follow a clear process to ensure data meets these values. Data profiling is a good place to start identifying and categorizing all types of data within a system, network or data set. During profiling, data errors are also identified. Data normalization is an approach used to transform all data into the same format. This makes it possible for data to be processed by data teams and AI and machine learning tools.

Data cleaning is an important step for cleaning up any erroneous or duplicate data. Data visualization, then, allows data engineers and data scientists to get the big picture of data. Data visualizations are particularly helpful when using real-time data.

Steps to perform data quality assessments

Data quality assessments have their own particular processes and standards that must be followed for a DQA to be effective. These are some of the most important data quality management steps for a DQA:

• Data profiling: A scan to identify data and any critical problems.
• Data cleansing: Actions taken to correct errors in data and processes.
• Data validation: Data is double-checked for standard and format.
• Data mapping: Data that is connected is mapped.
• Data integration: Databases and sub-data are unified and integrated into one system for analysis.
• Data visualization: Charts, graphs and single-source-of-truth dashboards are created for accessibility and visualization benefits.

Besides the processes listed above, which are similar to those used in data quality management frameworks, organizations often follow step-by-step checklists to ensure their DQAs meet the standards of specific organizations like USAID and EPA.

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These exhaustive checklists cover data observability and other data-related factors. Acceldata offers particularly helpful data and data pipeline checklists for organizations that want to strengthen their DQAs.

Data checklists

• Data discovery: Develop a unified data asset inventory across all environments. Inventories should be searchable and accessible.
• Data quality rules: Use AI/ML-driven recommendations to improve data quality and reliability.
• Data reconciliation rules: Check your data to ensure it looks correct and aligns with your data reconciliation policies.
• Data drift detection: Continuously monitor for any content changes that indicate how much data is drifting and affecting your AI/ML workloads.
• Schema drift detection: Look for structural changes to schemas and tables that can harm either pipelines or downstream applications.

Data pipelines checklist

• End-to-end visibility: Track the flow of data and accumulated costs as data moves across systems.
• Performance analytics: Optimize data pipeline performance based on historical data, current bottlenecks and processing issues.
• Pipeline monitoring: Watch how data transactions and other events happen across SLAs/SLOs, data schemas and distributions.
• Cost-benefit analysis: Consider costs and ROI that come with scaling your data quality efforts over time.
• ETL integration: Invest in ETL integrations to reduce complexity and unnecessary tactical work for trained data professionals.
• API for integration: Integrate existing infrastructure, data sets and data processes through API connectors.

Conclusion

While data quality management frameworks and data quality assessments share many common elements, DQAs are considered more concrete evidence of data quality performance. DQAs are also often required to do business with specific organizations.

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If your organization needs to create a DQA, experts suggest you should adhere to the processes and guidelines set by the party that requires it. While each authority or organization may have different specifics — for example, clinical trial-related DQAs must comply with health data regulations — the general processes for all DQAs are the same.