What are Data Quality Assessments?

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: data quality management frameworks have: ensure that the data is of good quality. However, unlike data quality management programs, DQAs are often required when working with government agencies such as USAID, environmental authorities such as the EPA, or health organizations such as 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 ensuring that the type, quantity and quality of the data presented has been assessed before a decision is made.

TO SEE: Data management checklist for your organization (Tech Republic Premium)

Like other approaches to data quality management, DQAs offer many benefits to data-driven businesses. They provide better data, which leads to better performance and decisions; they help organizations meet compliance and governance requirements; and they provide scientific evidence that the data used is of the highest standards. The rest of this guide provides an in-depth dive into data quality assessments, how they work, and how your organization can implement one.

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What is a data quality assessment?

A data quality assessment involves creating a self-contained report that includes evidence of the processes, observations, and recommendations found during data profiling.

Data quality assessments look at where the data comes from, how it flows within an organization, whether the data is of good quality and how it is used. In addition, the assessment identifies gaps in data quality, what types of errors the data has, why they have that level of quality, and how they can be resolved.

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

The main challenges of these assessments today are related to the significant amounts of data that organizations generate from various sources on a daily basis. Misconfigured, inaccurate, duplicate, hidden, ambiguous, obsolete, or incomplete data are common data quality issues. Companies are also struggling to define the standards for what constitutes good data quality and find skilled data experts who can operate the right technologies to move the process forward.

How do you assess data quality?

There are many different methods for assessing data quality, including data profiling, normalization, preprocessing, and/or visualization. DQAs are performed to ensure data meets five quality standards, according to USAID:

Data quality standards that DQAs must meet

• Validity: Data must clearly and adequately reflect the intended result.
• Integrity: Data should have safeguards to minimize the risk of bias, transcription errors, or data manipulation.
• Precision: The data must be sufficiently detailed to enable informed decision-making by management.
• Reliability: Data should reflect stable and consistent data collection processes.
• Timeliness: Data must be available with useful frequency, current and appropriate for use in management decision-making.

Data teams must follow a clear process to ensure that data lives up to these values. Data profiling is a good place to start with identifying and categorizing all types of data within a system, network, or dataset. Data errors are also identified during profiling. Data normalization is an approach used to convert all data into the same format. This makes it possible to process data by data teams and AI and machine learning tools.

Cleaning data is an important step in cleaning up erroneous or duplicate data. Data visualization then makes it possible data engineers and data scientists to get the big picture of data. Data visualizations are especially useful when using real-time data.

Steps to perform data quality assessments

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

• Data profiling: A scan to identify data and any critical issues.
• Data cleaning: Measures 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 subdata 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.

In addition to the processes mentioned above, which are similar to those used in data quality management frameworks, organizations often follow step-by-step checklists to ensure that their DQAs meet the standards of specific organizations such as USAID and EPA.

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These comprehensive checklists cover data observability and other data-related factors. Accel data provides extremely useful data checklists and data pipelines for organizations looking to strengthen their DQAs.

Data Checklists

• Data Detection: Develop a unified inventory of data assets across all environments. Inventories must 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 make sure it looks correct and complies with your data reconciliation policy.
• Data drift detection: Continuously monitor for any content changes that indicate how much data is floating and impacting your AI/ML workloads.
• Schedule drift detection: Look for structural changes to schemas and tables that could harm pipelines or downstream applications.

Data Pipeline Checklist

• End-to-end visibility: Track data flow and accumulated costs as data moves through different systems.
• Performance analyses: Optimize data pipeline performance based on historical data, current bottlenecks, and processing issues.
• Pipeline monitoring: See how data transactions and other events take place in SLAs/SLOs, data schemas, and distributions.
• Cost-benefit analysis: Consider the costs and ROI associated 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.
• Integration API: Integrate existing infrastructure, datasets and data processes via 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 recommend that you adhere to the processes and guidelines established by the party that requires it. While each authority or organization may have different specificities – for example, clinical trial-related DQAs must comply with health data regulations – the general processes are the same for all DQAs.