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What You'll Learn
Data powers every strategic decision you make. But these decisions are only as good as the data behind them. That’s why data quality assessment is critical. This blog will help you evaluate, improve, and operationalize data quality for enterprise decision-making. It will also cover a practical framework and tools you could leverage. Read on.
What is data quality assessment?
A Data Quality Assessment (DQA) is a formal process of measuring the condition of your data against defined data quality standards. It’s the process where you evaluate the key dimensions of your data. You are required to check for the following:
- Accuracy: According to Harvard Business Review, only 3% of a company’s data quality score can be rated as acceptable. And 47% of new data records have at least one critical error. Does your data reflect facts? Are your customer names and addresses correct and valid?
- Completeness: Are all required fields filled in? Do you see any missing data values? All required data must be present.
- Consistency: 58% of business leaders say that key business decisions are based on inaccurate or inconsistent data most of the time. Teams don’t understand the data collected or how to access it. Hence, you must check if data values are the same across your business systems. For instance, is a customer’s state name “NY” in one system and “New York” in another?
- Validity: Does your data follow predefined formats and rules? As per research, 45 million U.S. addresses change every year. Are the email addresses that you have in the correct format? Or phone numbers?
- Timeliness: Is your data updated when it’s supposed to be? Are daily transactions recorded—-
- Uniqueness: Are there duplicates in your dataset? A study shows that around 10-30% of the data in databases is duplicate. Are you keeping a check on your customer records? Do they have different spellings or formats?
Why data quality assessment matters
It is simple! Data quality assessment helps you prevent data quality issues. A structured DQA can help you understand the gaps in the systems you use, like CRM, ERP, and analytics platforms. It is important to conduct a data quality assessment so that you are able to quantify the business impact of bad data. Here’s what happens when you don’t do it. As per McKinsey’s research, errors in customer data can drive 15% higher cart abandonment.
With a practically designed data quality assessment program, you have full control of your data. You are aware of the authorized individuals who have access to your sensitive business data.
Data quality assessment helps you answer one big question: Can you trust your data?
According to Gartner, poor data quality costs companies an average of $12.9 million annually. You don’t want the same to happen for your organization. The next section will help you understand how you can take full control of your data quality assessment process.
Step-by-Step Framework for Data Quality Assessment
Here’s a structured framework your team can use for data quality assessment:
Step 1: Know your data quality priorities
Identify what outcomes your business expects from data. Linking data quality to outcomes is emphasized by DAMA-DMBOK, the globally recognized framework that offers best practices for data management and governance. Raising data completeness from X% to Y% in 6 months is a good example of how you can define your data priorities and business goals.
Step 2: Catalog data sources and metadata
Do you know where all your data is residing currently? We recommend creating a list of all the systems. These systems could be your CRM, ERP, cloud data warehouse, or other third-party integrations. It is also important to catalog your metadata. DAMA-DMBOK includes “metadata management” as a core pillar alongside data quality. Record everything – how your data flows between your systems, who is the owner of such data, and where your data is created, transformed, and consumed. We recommend using a data catalog or metadata tool to automate the discovery and tagging of data assets.
Step 3: Profile your Data
Leverage data profiling tools to scan datasets for null values, outliers, or unexpected data patterns, and duplicate data records. As per ISO 8000-8, there are defined standards for profiling data quality dimensions like nulls and duplicates.
Step 4: Score & prioritize data issues
Assign data quality scores such as 0–100% for each data quality dimension. Accuracy, completeness, timeliness, etc. Use thresholds to define what’s acceptable and what’s not. For example: 95%+ validity for financial transactions.
Step 5: Implement tactical fixes
Apply data validation rules at the point of entry. Cleanse and standardize your legacy data using data quality tools like Informatica Data Quality or Oracle Enterprise Data Quality. Set up deduplication logic using fuzzy matching or master data management techniques. One tip from us is to automate these processes in your ETL/ELT pipelines to maintain data quality over time.
Step 6: Prioritize continuous data quality monitoring & governance
According to COBIT APO14, data quality controls should be implemented and continuously monitored for improvement. Here’s how you can do it. Set up ongoing validation checks that run daily or in real-time. Implement alerts for when data quality drops below thresholds. Share data quality dashboards with business users to build accountability. Establish roles like data stewards, data engineers, and data analysts. All roles should be assigned defined responsibilities. Be proactive! This is one of the best ways you could implement a successful data quality assessment framework.
Top Tools for Data Quality Assessment
Modern businesses choose modern tools for data quality assessment. Below are trusted solutions from LumenData’s strategic technology partners.
Informatica Data Quality
Informatica offers one of the most comprehensive data quality platforms in the market. It provides you with data profiling, data cleansing, rule creation, and data monitoring. Everything is within a highly governed environment. If you are an enterprise handling sensitive or regulated data, Informatica ensures not just clean data, but trustworthy data. With pre-built AI-powered data quality and accelerators, it can be an excellent choice for you, especially if you are looking to embed data quality into MDM, analytics, and compliance processes.
Snowflake + Native Quality Controls
Snowflake’s cloud-native architecture simplifies data quality enforcement through built-in features like constraints, zero-copy cloning, and real-time streams. Please note that Snowflake isn’t a traditional data quality tool! But its ecosystem allows data quality checks to be integrated directly into ingestion and transformation workflows using tasks, stored procedures, and third-party connectors. If you combine it with partner tools like dbt or Informatica, Snowflake becomes a powerful foundation for scalable and governed data quality initiatives.
dbt
dbt (data build tool) is widely used for transforming raw data into analysis-ready datasets. dbt provides you with modern data quality features such as proactive error protection, automated data quality testing and monitoring, and end-to-end data visibility. Around 60,000+ business teams use the dbt platform for their data quality objectives.
Fivetran
Fivetran is a leading data movement platform that ensures data freshness and data integrity during extraction and loading. It has 700+ pre-built connectors that automatically normalize schema, handle schema drift, and offer built-in alerting for pipeline failures. While it doesn’t directly clean or validate data, Fivetran plays a critical role in upstream reliability. You can combine it with Snowflake and dbt to create a strong foundation for clean data.
Choose LumenData for high-impact data quality services
At LumenData, we specialize in helping organizations build enterprise-grade data quality ecosystems that are scalable, sustainable, and business-aligned.
Some of our services include:
- Data quality assessment & roadmapping – We evaluate your current data quality landscape and help identify gaps. We then create phased data quality improvement plans tied to business outcomes.
- Tool selection & implementation – We help you select, configure, and deploy the right mix of data quality technologies to support your data environment, whether cloud-native or enterprise platforms.
- Training & team enablement – We have training programs to train your internal teams to take over operations with confidence.
Contact us today.
About LumenData
LumenData is a leading provider of Enterprise Data Management, Cloud and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
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