Data Quality Testing – Expert Strategies to Measure and Determine Data Accuracy

Has a flawed report ever sent your team scrambling? Inaccurate data creates chaos. It leads to bad business decisions, failed projects, and a deep mistrust of your analytics in tools like Power BI. Your team spends more time questioning the numbers than acting on them.

So, how do you fix it? You start with data quality testing.

This is the process of checking your information against clear standards to confirm it is accurate, complete, and ready for business use. It transforms data from a source of risk into a reliable asset. This guide gives you expert strategies to measure your data’s accuracy and build a foundation of solid, trustworthy information.

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Why You Can’t Afford to Ignore Data Quality?

Imagine launching a major marketing campaign. You spend thousands on materials and outreach. The results? Disappointing. You later discover that 20% of your customer addresses were outdated and 15% of the contact records were duplicates. The campaign was doomed from the start.

This isn’t a rare accident. It’s a symptom of poor data quality. Bad data is expensive. It quietly eats away at your budget through wasted work, missed sales, and poor customer experiences. It actively blocks you from making smart business decisions.

What Is The Real Cost of Poor Data?

It’s much more than a single bad number on a spreadsheet. It’s a constant drag on your company’s performance. These problems often fly under the radar.

• Operational Inefficiency: Your team wastes hours manually correcting errors, hunting for missing information, and re-running reports. They become data janitors instead of analysts or strategists.
• Wasted Spend: Marketing campaigns miss their targets. Products are shipped to the wrong addresses. Sales teams chase disconnected leads. Every mistake burns real money.
• Eroded Customer Trust: Sending an invoice to the wrong person or getting a customer’s history wrong damages your reputation. Trust is hard to win back.
• Compliance Risk: Inaccurate records can lead to failed audits and hefty fines. You can’t prove compliance if your data is a mess.

How Inaccurate Data Undermines Your Analytics and Power BI Dashboards?

A flawed BI report is worse than no report at all. It gives you the confidence to make the wrong call. When leaders discover the data is unreliable, they stop using it. Every meeting turns into a debate about whose numbers are correct. Your expensive analytics platform becomes a source of confusion, not clarity.

What good is a beautiful Power BI dashboard if you can’t trust the data powering it?

How Do You Measure Data Quality? The 6 Core Dimensions Explained

How can you tell if your data is “good”? Data quality isn’t a single, simple score. To truly determine data quality, you must break it down and measure it across several key areas, known as dimensions. This framework gives you a precise vocabulary and a clear method to diagnose exactly where your information fails.

Each dimension answers a critical question about your data’s fitness for use.

1. Accuracy: Is the information correct and reliable?

Accuracy means your data correctly reflects real-world facts. It is the bedrock of trust. If your data is not accurate, every other dimension becomes irrelevant.

• What does it prevent? Shipping products to the wrong address, sending invoices to a closed office, or basing financial forecasts on incorrect sales figures.
• Business Example: A customer’s contact record in your CRM system lists their title as “Director of Marketing,” which is verified to be their current role at their company.

2. Completeness: Are there any gaps or missing values?

Completeness checks if all the necessary information is present. A record isn’t useful if critical fields are empty. This is a common problem in systems where some fields are optional.

• What does it prevent? Inability to onboard a new supplier because their tax ID is missing. Failing to contact a sales lead because the phone number field is blank.
• Business Example: A new product record in your Product Information Management (PIM) system has all 25 required attributes filled out before it can be published to the e-commerce site.

3. Consistency: Does the data match across different systems?

Consistency confirms that the same piece of information is identical across all your systems. Data silos are the enemy of consistency, creating multiple versions of the truth.

• What does it prevent? A customer service agent seeing a different order history in the CRM than the accounting team sees in the ERP, leading to confusion and poor service.
• Business Example: A product’s price is listed as $29.99 in your ERP, your e-commerce platform, and your marketing database. There is no conflict.

4. Timeliness: Is the data current enough to be relevant?

Timeliness measures if your information is available when you need it. The value of data decays over time. Yesterday’s inventory count is useless for today’s real-time sales promise.

• What does it prevent? Making business decisions based on last quarter’s market data. Promising a customer an item that was actually sold out ten minutes ago.
• Business Example: Your inventory management system updates stock levels within seconds of a sale, allowing your website to accurately display “Only 3 left in stock!”

5. Uniqueness: Are there duplicate records skewing your view?

Uniqueness ensures that a real-world entity (like a customer or product) is recorded only once in a dataset. Duplicates create waste and dramatically distort analytics.

• What does it prevent? Sending the same promotional email three times to the same person because they exist under “Jon Smith,” “J. Smith,” and “Jonathan Smith.” Inflating your total customer count.
• Business Example: Your customer database is de-duplicated, so “Jane Doe at 555 Pine St.” exists as a single, master record, not three slightly different variations.

6. Validity: Does the data conform to the required format and business rules?

Validity (or conformity) checks if your data is stored in the correct format and follows your organization’s defined rules. It’s about structural integrity.

• What does it prevent? A system crashing because it expects a date in the MM/DD/YYYY format but receives “January 15, 2025.” A US ZIP code being entered with six digits.
• Business Example: All phone numbers in your contact list are stored in the same (XXX) XXX-XXXX format, and every order_date field is a valid, logical date.

What Are the Steps to Test Data Quality?

Where do you even begin a data quality testing initiative? Without a plan, it feels overwhelming. By following a practical, repeatable process, you can get control over your data methodically. This four-step framework turns a big, intimidating problem into a manageable project that delivers real results.

Step 1: Define Your Quality Standards and KPIs (What does “good” look like for you?)

You cannot measure what you have not defined. The first step is to decide on your rules and targets. This is not just an IT task; you must work with the business users who depend on the data. They know what “good” looks like for their processes.

• Action: For each critical data element (like customer email or product status), define its quality dimensions. Create specific, measurable Key Performance Indicators (KPIs).
• Example: “For the Customer table, the Email field must achieve 99% validity (formatted correctly) and 95% completeness (not empty).”

Step 2: Profile Your Data to Create a Baseline (What is the current state of your data?)

Before you can improve, you need an honest assessment of where you are now. Profile your data to get a comprehensive snapshot of its health. Data profiling uses tools to automatically scan your datasets and discover their structural characteristics.

• Action: Run profiling tools against your key tables. These tools will identify value ranges (e.g., all order amounts are between $10 and $5,000), count nulls, find frequency of values, and flag format inconsistencies.
• Result: You get a detailed report that shows you the gap between your standards (Step 1) and your current reality. This tells you where to focus your efforts.

Step 3: Design and Execute Specific Data Quality Tests (How will you find the errors?)

With your baseline established, it’s time to find the individual records that fail your standards. Apply specific, automated tests based on the rules you defined. This is the core of the data quality checking process.

• Action: Translate your business rules from Step 1 into technical tests. These can be SQL queries, checks within an ETL tool, or functions in a dedicated data quality platform.
• Example Tests:
  • A validity test that flags any ZIP code that is not five digits.
  • A consistency test that compares product prices between the ERP and the website.
  • A completeness test that lists all customer records missing a phone number.

Step 4: Monitor, Report, and Remediate (How will you fix issues and prevent them from recurring?)

Finding errors is just the beginning. The final, ongoing step is to track quality and drive improvement. Create a feedback loop to fix not only the bad data but also the broken processes that created it.

1. Monitor & Report: Display your data quality KPIs on dashboards (Power BI is perfect for this) so everyone can see the scores over time.
2. Remediate: Establish a clear workflow for correcting flagged errors. Who is responsible for fixing a bad record?
3. Find the Root Cause: Investigate why the bad data is getting into the system. Does a web form need better validation? Does a team need more training? Fix the source to stop the flow of bad data permanently.

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What Methods and Checks Do You Use for Data Accuracy?

You use a set of specific techniques designed to find different kinds of errors. Think of these as the tools in your data quality toolkit. Each method is suited for a particular job, from checking formats to finding statistical oddities.

How do you actually perform a data quality test? Here are four common and powerful methods you can use to check your data.

Rule-Based Validation

This is the most fundamental type of quality data check. You create a set of logical rules that your data must follow. These rules are direct translations of your business policies. Any data that breaks a rule is flagged as an error.

• What it does: Enforces your specific business logic and constraints on data values.
• In-Action Example: You create a rule that the State field for any US address must be one of the 50 official two-letter state abbreviations. A record with “PU” or “Calif” would be flagged.

Reconciliation Checks

Data often moves between systems, like from an ERP to a data warehouse. Reconciliation checks make sure nothing gets lost or changed along the way. You compare datasets at the source and destination to confirm they match.

• What it does: Verifies data consistency across different systems or after a data migration.
• In-Action Example: You run a check to compare the total daily sales revenue recorded in your point-of-sale system against the total loaded into your Power BI sales report. The numbers must be identical.

Anomaly & Outlier Detection

Sometimes data is technically valid but still wrong. Anomaly detection uses statistical analysis to find data points that fall far outside the normal range. These outliers often represent data entry mistakes or system glitches.

• What it does: Identifies unusual or suspicious values that could indicate an error.
• In-Action Example: Your system analyzes product order quantities and flags an order for “1,000” units when 99% of all other orders are for fewer than 10. This could be a typo that should have been “10.00”.

Pattern Matching (Regex)

Many types of data must follow a strict format, like phone numbers, email addresses, or specific ID codes. Pattern matching, often using Regular Expressions (Regex), checks if the data’s structure is correct.

• What it does: Validates that data conforms to a required structural pattern.
• In-Action Example: You apply a pattern check to make sure every email_address field contains an “@” symbol and a valid domain suffix like “.com” or “.org”. An entry like “john.smith@gmail” would fail.

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When to Stop DIY – Scaling Data Quality with an Expert Partner

You now have a solid plan to improve your data. But understanding the ‘what’ and ‘how’ is different from having the time, tools, and specialized skills to do it. At some point, a do-it-yourself approach becomes a bottleneck. It slows you down.

So, when does it make sense to get help? Look for these common triggers:

• You’re facing a major data migration or a new system implementation (like a CRM or ERP).
• Your team lacks deep expertise in data governance, integration, or Microsoft tools like Azure Data Factory and Power BI.
• You need to build trust in your BI reports, fast, but your team is already overloaded with other priorities.
• The scale of the problem is just too big for a part-time effort.

Trying to handle complex data challenges without a dedicated team often leads to slow progress and frustration. An expert partner isn’t just another vendor; they are an extension of your team. A partner like Multishoring brings a dedicated group of specialists with deep experience in data integration audit services and Microsoft technologies. 

We provide the focus and a proven framework to get results quickly. We help you build a reliable data foundation so your team can focus on growing the business, not fighting fires.

Is your data ready for your next big project? Schedule a data health assessment with Multishoring’s experts to identify your biggest risks and opportunities.