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Data Quality: The Bedrock Every Engineer Must Build On

Posted On: September 2025

By: Dinesh L

As aspiring data engineers, you're on a thrilling journey to design the next big data pipeline or data warehouse. But let's pause for a second. Once you've built that pipeline, how confident are you that the data coming out of it is any good? Is it trustworthy enough to fuel those shiny dashboards and reports your company relies on?Now, imagine this: Your company's dashboard is showing inflated sales figures. Or worse, it's missing half of your customer base. Decisions are being made based on this data, and before long, your company is making bad calls. There goes your credibility and a lot of lost opportunities. This is where Data Quality becomes your secret weapon. High-quality data isn't just a bonus; it's the foundation for everything you build. Accuracy, consistency, and reliability are the pillars. But how do you evaluate whether your data meets these standards? Well, get ready, because we've got six key dimensions of data quality that every data engineer should be thinking about.

Accuracy: Is the Data Correct?Imagine you're working on a dataset that tracks employees and their details. But suddenly, you notice some discrepancies. Let's take a look:

EID	Name	City	State	Country	Pincode	Work Email
101	Emp1	Mumbai	MH	India	400001	Emp1@email.com
102	Emp2	Bangalore	KA	India		Emp2@email.com
103	Emp3	New York	NY	USA	1001	Emp3@email.com
104	Emp4	Delhi	DL	India	500001	Emp4@email.com
101	Emp5	Chennai	TN	India	600001	Emp5@email.com

Rule: For India, the Pincode should be 6 digits long, while for the USA, it should be 5 digits.

What do we spot?: The Pincode for Emp3 is 1001, which doesn't follow the 5-digit rule for the USA. Is this data accurate? Nope.Completeness: Is Any Data Missing?You're analyzing employee data, and suddenly you realize some information is missing. Check out the following table:

EID	Name	City	State	Country	Pincode	Work Email
101	Emp1	Mumbai	MH	India	400001	Emp1@email.com
102	Emp2	Bangalore	KA	India		Emp2@email.com
103	Emp3	New York	NY	USA	1001	Emp3@email.com
104	Emp4	Delhi	DL	India	500001	Emp4@email.com
101	Emp5	Chennai	TN	India	600001	Emp5@email.com

Rule: Pincode should never be missing.

What do we spot?: Emp2 is missing a Pincode. Does this affect the completeness of your dataset? Absolutely.

Consistency: Is the Data Stored in a Uniform Way?Consistency means that data across different tables should match up. Let's say you have two tables: one with employee details and another with contact info.

EID	Name	Contact	Work Email
101	Emp1	+91-9999999999	Emp1@email.com
102	Emp2	+91-98888888888	Emp2@email.com
103	Emp3	+1-222-555-0123	Emp3usa@email.com
104	Emp4	+91-97777777777	Emp4@email.com

Rule: The email in the contact table should match the one in the employee table.

What do we spot?: Emp3 has a different email in the contact table: Emp3usa@email.com vs. Emp3@email.com. Is your data consistent? Nope.

Timeliness: Is the Data Up-to-Date?You've got a table that tracks employee status and last update date. You want to make sure that your data reflects the most recent updates, especially for active employees.

Emp_ID	Name	Last_Updated_Date	Emp_Status	Department
101	Emp1	2025-09-01	Active	IT
102	Emp2	2025-08-01	Active	HR
103	Emp3	2025-09-01	Active	Finance
104	Emp4	2025-08-01	Resigned	Sales

Rule: Monthly reviews should be performed for active employees.

What do we spot?: Emp2 and Emp4 have outdated records. Timeliness is key!

Uniqueness: Are There Duplicates?Duplication can seriously mess with your data. Check this out:

EID	Name	City	State	Country	PinCode	Work_Email
101	Emp1	Mumbai	MH	India	400001	Emp1@email.com
102	Emp2	Bangalore	KA	India		Emp2@email.com
103	Emp3	New York	NY	USA	1001	Emp3@email.com
104	Emp4	Delhi	DL	India	500001	Emp4@email.com
101	Emp5	Chennai	TN	India	600001	Emp5@email.com

Rule: Employee ID (EID) cannot be duplicated.

What do we spot?: Emp1 is listed twice with different data! Duplicate records? Big problem.

Validity: Does Data Follow the Required Format? This one's about ensuring that data is in the right format. Let's take a look at the states in this dataset:

EID	Name	City	State	Country	PinCode	Work_Email
101	Emp1	Mumbai	MH	India	400001	Emp1@email.com
102	Emp2	Bangalore	KA	India		Emp2@email.com
103	Emp3	New York	NY	USA	1001	Emp3@email.com
104	Emp4	Delhi	DL	India	500001	Emp4@email.com

Rule: State should only have two characters (e.g., MH, KA).

What do we spot?: All state codes here follow the rule, so this part checks out!

Wrapping It UpWhen you're building data pipelines, don't just think about the structure of the data, think about the quality. Accurate, complete, consistent, timely, unique, and valid data is what will empower your organization to make smart, reliable decisions. Think of these 6 dimensions as the checklist that ensures your pipeline isn't just functioning; it's functioning with data that actually works.Once you've calculated your data quality scores, you can use Power BI or other visualization tools to visualize and track your progress. Imagine monitoring the health of your data with interactive dashboards and getting real-time insights into which dimensions need attention.PowerBI Report

Pro Tip! Keep track of these dimensions while building your data pipelines and you'll go from a good engineer to a great one! Your future self and your company will thank you.

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