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How do you check data quality?

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Data quality is a crucial aspect of any analytical project, as it affects the accuracy, reliability, and validity of the results. Poor data quality can lead to erroneous conclusions, wasted resources, and missed opportunities. Therefore, checking data quality is a vital skill for any analyst who wants to deliver high-quality insights and recommendations. In this article, we will discuss some common methods and best practices for checking data quality, and how to address some of the data quality issues that may arise.

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Huda Anwar

Data-First Visionary & Leader: Guiding Teams to Success Through Data & AI Driven Strategies
Gaurav Rawat

15 Yrs of Transforming Businesses: Digital Transformation & Marketing-Product & AI | Alembic Pharma | HDFC ERGO |…
James C.

Senior Business & Quality Assurance Analyst at Logic Group NZ

1 Define data quality criteria

Before you can check data quality, you need to define what data quality means for your specific project and context. Data quality criteria are the standards and expectations that you set for your data, based on the purpose and scope of your analysis. Some common data quality dimensions are accuracy, completeness, consistency, timeliness, relevance, and uniqueness. Depending on your project, you may also consider other aspects, such as security, usability, or compliance. You should document your data quality criteria clearly and communicate them to your stakeholders, so that everyone is on the same page about what constitutes good data quality.

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Gaurav Rawat

15 Yrs of Transforming Businesses: Digital Transformation & Marketing-Product & AI | Alembic Pharma | HDFC ERGO | Maruti Suzuki HQl TataMotors | Infosys | 40 under 40 BM | AI Product Development(Stealth & Hustle mode)??
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In addition to this Data Validity, Precision and consistency is also very important- Data Validity refers to the extent to which data accurately represents the concept or phenomenon it is intended to measure or describe. Precision relates to the level of detail or granularity of the data. It reflects the degree of exactness and specificity with which the data values are recorded. Precise data helps in achieving more accurate and reliable results. While Consistency is about the absence of Data discrepancies and unavailability of data points which can lead to impact the analysis. Data Quality criteria setup is quite important for a reliable analytical result and it is required to be regularly monitored and updated during the project.

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2 Assess data sources and formats

The next step is to assess the data sources and formats that you will use for your analysis. Data sources are the origins of your data, such as databases, files, surveys, or APIs. Data formats are the ways that your data is structured and stored, such as CSV, JSON, XML, or Excel. You should evaluate the reliability, availability, and accessibility of your data sources, and check if they meet your data quality criteria. You should also check if the data formats are compatible with your analytical tools and methods, and if they require any preprocessing or transformation.

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James C.

Senior Business & Quality Assurance Analyst at Logic Group NZ
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To ensure reliable data for meaningful analysis, evaluate sources and formats for credibility, availability, and accessibility. Verify adherence to rigorous collection practices. Consider compatibility with analytical tools. Establish criteria: accuracy, completeness, timeliness, consistency. Evaluate using checklists. Investigate data provenance for biases. Leverage automation for validation. Detect anomalies and outliers. Combine manual and automated checks. Maintain a proactive approach, regularly revisit assessments, adapt methods for emerging sources. Meticulous assessment enhances accuracy, credibility, and value. Strive for high data quality, empower informed decision-making, drive meaningful insights, benefit organisation's success.

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3 Perform data profiling and exploration

Data profiling and exploration are techniques that help you understand the characteristics and distribution of your data, such as the number of records, columns, values, types, ranges, frequencies, outliers, and patterns. Data profiling and exploration can help you identify and quantify data quality issues, such as missing values, duplicates, errors, inconsistencies, or anomalies. You can use various tools and methods to perform data profiling and exploration, such as descriptive statistics, summary tables, charts, histograms, box plots, or scatter plots. You can also use code or queries to extract and inspect specific subsets or samples of your data.

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Huda Anwar

Data-First Visionary & Leader: Guiding Teams to Success Through Data & AI Driven Strategies
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Always helpful to automate data profiling and build dashboards to alert if any quality issues such as incompleteness if data.

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4 Apply data quality rules and checks

Data quality rules and checks are the methods that you use to verify and validate your data against your data quality criteria. Data quality rules and checks can be applied at different levels and stages of your data pipeline, such as during data collection, ingestion, integration, or analysis. Some examples of data quality rules and checks are data validation rules, data cleansing rules, data integrity rules, data quality metrics, data quality reports, or data quality audits. You can use various tools and methods to apply data quality rules and checks, such as code, queries, functions, formulas, or software.

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5 Resolve data quality issues

Once you have identified and measured the data quality issues in your data, you need to decide how to resolve them. There are different strategies and techniques that you can use to resolve data quality issues, depending on the type and severity of the issue, the impact on your analysis, and the availability of resources. Some examples of data quality resolution techniques are data imputation, data deletion, data correction, data standardization, data normalization, data enrichment, or data reconciliation. You should document your data quality resolution decisions and actions, and track their effects on your data quality.

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6 Monitor and maintain data quality

The final step is to monitor and maintain your data quality throughout the lifecycle of your project. Data quality is not a one-time activity, but a continuous process that requires regular review and improvement. You should establish a data quality management framework that defines the roles, responsibilities, processes, tools, and standards for ensuring and enhancing your data quality. You should also implement a data quality feedback mechanism that allows you to collect and address data quality issues from your users, clients, or stakeholders.

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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?

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Dimple T. Shah, Esq., CIPP/US

Privacy Professional
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Also consider how data quality plays a fundamental role in security and compliance frameworks. Poor data quality can lead to false positives and security "alert fatigue." False negatives can lead to missing sensitive data that needs protective controls. High-quality data pioneers compliance initiatives. Standardized metadata informs remediating compliance risks [cue drumroll] automatically. Strong data quality processes mean reports are based on trusted data that is not only accurate, but may very well deliver on regulatory expectations! So the next time business wants to tackle data quality... invite your pals in privacy, compliance and cybersecurity to join the game (for the win) :)

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How do you check data quality?

1

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3

4

5

6

7

1 Define data quality criteria

2 Assess data sources and formats

3 Perform data profiling and exploration

4 Apply data quality rules and checks

5 Resolve data quality issues

6 Monitor and maintain data quality

7 Here’s what else to consider

Analytical Skills

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How do you check data quality?

1

2

3

4

5

6

7

1 Define data quality criteria

2 Assess data sources and formats

3 Perform data profiling and exploration

4 Apply data quality rules and checks

5 Resolve data quality issues

6 Monitor and maintain data quality

7 Here’s what else to consider

Analytical Skills

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