How can you use automated data quality assessment methods effectively?

Data quality is crucial for any data analysis project, but checking and improving it manually can be time-consuming and error-prone. That's why automated data quality assessment methods can be a valuable tool for data analysts, as they can help you identify and fix common data quality issues faster and more accurately. In this article, you will learn how to use automated data quality assessment methods effectively, and what are some of the benefits and challenges of using them.

1 What are automated data quality assessment methods?

Automated data quality assessment methods are techniques that use algorithms, rules, or models to evaluate the quality of data without human intervention. They can measure various aspects of data quality, such as accuracy, completeness, consistency, validity, timeliness, or uniqueness. For example, you can use automated methods to detect missing values, outliers, duplicates, inconsistencies, or errors in your data, and generate reports or alerts based on the results.

Add your perspective

Dillan T.

Data Analyst | CEO
Report contribution
I would include an example here to engage readers and to demonstrate the impact that bad data can have on reports or business decisions.

Like
David Arnaud Ngam A Kibeng

Cornell Alumni - MEAL Expert - Community Development practitioner - Social Scientist - Public Health Specialist
Report contribution
What I found useful in this article were the couple examples provided like detecting missing values, outliers, duplicates, inconsistencies, or errors in data. Those examples are very generic in data quality assessment. An automated model is led by rules, an algorithm, a program. That means that the perception of the programmer affects the keys, the focus, and the procedures to assess the said data. I want to put in perspective that each data has it's own sociocultural context, so it's own accuracy, completeness, consistency, validity... Automated data quality assessment methods are unavoidable while working with large data sets and help preparing data for a thorough examination.

Like
Srinivasa Gopalan K

Risk Quant | Goldman Sachs | IIT Madras '22 | Brewing ideas to vision
Report contribution
Use ydata-profiler to profile and generate an automated report on any dataframe as an extremely time saving starting point! :)

Like

2 Why use automated data quality assessment methods?

Automated data quality assessment methods can offer several advantages over manual methods, such as saving time and resources, improving accuracy and reliability, and enhancing data governance and compliance. You can run automated checks on large or complex datasets quickly and efficiently, without spending hours or days on manual inspection or correction. Additionally, you can reduce the risk of human errors or biases, and ensure that the same standards and criteria are applied consistently across your data sources and analysis processes. Moreover, you can track and document the data quality issues and actions taken to demonstrate that your data meets the required quality levels and regulations.

Add your perspective

David Arnaud Ngam A Kibeng

Cornell Alumni - MEAL Expert - Community Development practitioner - Social Scientist - Public Health Specialist
Report contribution
What you might consider in replicating a model to different data is to standardize it. We should make sure that the data has been collected with the same protocols. A human eye can prepare the data and make sure that it is ready to use with automated methods.

Like

3 How to use automated data quality assessment methods effectively?

To use automated data quality assessment methods effectively, there are some best practices to consider. You need to define your data quality goals and metrics, choosing the right tools and methods for your data type, format, structure, and quality dimensions. Additionally, you should implement and monitor the automated checks at appropriate stages and intervals in your data analysis pipeline. This will help you visualize and communicate the data quality status and trends, as well as identify any issues or anomalies through dashboards, reports, or notifications.

Add your perspective

Kamal Pant

Principal Endur/RightAngle/Zema I ETRM/CTRM I Product Management I Digital Transformation I LNG I Bunkering I Crude I Mogas I Diesel I Aviation Fuel I LPG I Asphalt I Energy I Oil & Gas I Coffee I Petroleum Mgt & Engg
Report contribution
Using statistical techniques and benchmarking the data to trusted source, enable us to ensure data quality. Scalability and flexibility of solution to cater to specific data validation needs.

Like
Volha S.
Report contribution
With any automation from VBA to python cleansing code and ML, it will be only as good as quality rules you set up and automated fixes for errors coded. It may not work well with different type of data or new uknown errors. You will have to go and check new errors, right a new code to fix them. I believe the best quality will come from good data culture in organisation, robust workflow and asociated data flows, enabled traceability and data lineage to speedly spot at what step fix is required.

Like
David Arnaud Ngam A Kibeng

Cornell Alumni - MEAL Expert - Community Development practitioner - Social Scientist - Public Health Specialist
Report contribution
A good point is to monitor automated checks during the data analysis phase. Sometimes we can identify errors in our model later in the exploitation. It is very important to keep an eye on data quality status and trends because we might refine or change the initial model.

Like

4 What are the benefits of using automated data quality assessment methods?

Using automated data quality assessment methods can bring several benefits for your data analysis project, such as improved outcomes and insights, increased efficiency and productivity, and boosted confidence and trust. You can make sure that your data is accurate, complete, consistent, and relevant for your analysis objectives, streamline and automate your data quality processes, and increase the credibility and reliability of your data and analysis. This will help you avoid misleading or inaccurate results or conclusions, focus more on the core analysis tasks and value-added activities, and build trust and confidence among your stakeholders, customers, or users.

Add your perspective

David Arnaud Ngam A Kibeng

Cornell Alumni - MEAL Expert - Community Development practitioner - Social Scientist - Public Health Specialist
Report contribution
As these automated models are faster the productivity in projects is way better. Those models can also be used by non experts. However, it should be monitored by a trained person to make sure that it is used the right way.

Like

5 What are the challenges of using automated data quality assessment methods?

Using automated data quality assessment methods can also pose some challenges or limitations. Having a good understanding of the concepts, standards, and methods is essential for selecting, implementing, and evaluating the automated checks appropriately and effectively. Moreover, the context and scope of the data quality assessment should be considered and adjusted accordingly. Additionally, you need to balance the benefits and costs of using automated data quality assessment methods, making trade-offs based on your data quality goals and constraints. This may involve compromising on some data quality dimensions or aspects, or investing in more resources or tools.

Add your perspective

Kamal Pant

Principal Endur/RightAngle/Zema I ETRM/CTRM I Product Management I Digital Transformation I LNG I Bunkering I Crude I Mogas I Diesel I Aviation Fuel I LPG I Asphalt I Energy I Oil & Gas I Coffee I Petroleum Mgt & Engg
Report contribution
I would like mention data latency or in another words tool's responsiveness, when data flow is continuous and time criticality is of paramount importance.

Like

6 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?

Add your perspective

Kamal Pant

Principal Endur/RightAngle/Zema I ETRM/CTRM I Product Management I Digital Transformation I LNG I Bunkering I Crude I Mogas I Diesel I Aviation Fuel I LPG I Asphalt I Energy I Oil & Gas I Coffee I Petroleum Mgt & Engg
Report contribution
Technologies that can be employed like ML/AI, Big Data, Blockchain etc. Data Management standards and organizations like: 1. DAMA 2. ISO 8000 3. ISO/IEC 27001 4. ISO/IEC 38505 5. GDPR 6. HIPAA 7. COBIT 8. TOGAF 9. MMI 10. FAIR

Like
Joshua Kashitala

Managing Partner
Report contribution
One thing I have found useful is when it's linked to indicator for quality improvement project to show a dashboard for users to track changes

Like

How can you use automated data quality assessment methods effectively?

1

2

3

4

5

6

1 What are automated data quality assessment methods?

2 Why use automated data quality assessment methods?

3 How to use automated data quality assessment methods effectively?

4 What are the benefits of using automated data quality assessment methods?

5 What are the challenges of using automated data quality assessment methods?

6 Here’s what else to consider

Data Analysis

Rate this article

Thanks for your feedback

More articles on Data Analysis

More relevant reading

How can you use automated data quality assessment methods effectively?

1

2

3

4

5

6

1 What are automated data quality assessment methods?

2 Why use automated data quality assessment methods?

3 How to use automated data quality assessment methods effectively?

4 What are the benefits of using automated data quality assessment methods?

5 What are the challenges of using automated data quality assessment methods?

6 Here’s what else to consider

Data Analysis

Rate this article

Thanks for your feedback

Explore Other Skills