Boost Your Data Quality with Effective Governance

00:00

00:00

1x

• 0.25
• 0.5
• 0.75
• 1
• 1.25
• 1.5
• 1.75
• 2

Sirojiddin Dushaev Lead Data Engineer & Cloud Solutions Architect

Yana Ni Chief Engineering Officer

Every year businesses lose millions of dollars just because they failed to properly ensure data quality. As a result, they make decisions based on outdated, incomplete, or just plain inaccurate information – and all that comes at a cost. 

Long before the financial damage starts to show up, companies face a lack of clarity about their customers’ image and a creeping lack of confidence in their next steps – all because the data they are working with can’t be fully trusted. 

But it doesn’t have to be this way. Organizations who master their data tend to increase their profits by 9.5% and are 2.9 times more likely to beat the market competition. How do they do it? By working with a reliable data governance framework!

CHI Software, a data engineering company with nearly two decades of experience in the IT industry, is here to demystify the crucial aspects of working with data quality governance at each stage, and the most commonly used frameworks.

Tired of messy and unreliable data? It’s time to clean it up for good.

Fix your data

Article Highlights:

• Metrics that are inconsistent across departments signal poor data quality – and this is one of the top reasons companies misread performance trends;
• Data profiling and cataloging tools OpenRefine and Google Data Catalog help businesses detect and fix insufficient data in minutes, not days;
• CHI Software’s custom data governance framework helped our marketing client receive real-time insights, boosting decision speed by 2x.

Why Data Quality Breaks, and Why Data Governance Framework Is Your Fix

Do you invest in analytics tools, CRM systems, or even artificial intelligence — but still aren’t getting the necessary insights? CHI Software has seen this pattern many times, and the issue often comes down to one thing: a non-existent or poorly constructed data quality framework. 

What Causes Poor Data Quality?

Let’s start with the reasons why poor data quality gets created in the first place: 

• People make mistakes like typos or missing fields;
• There are no data validation rules on how to enter, update, or manage data, so everyone does it their way;
• Data isn‘t always updated, so it becomes irrelevant;
• Different departments use separate systems that don‘t sync;
• No one is in charge of data quality frameworks.

These issues might seem small, but imagine what happens when a company makes all these mistakes and then ignores them for years. 

The True Cost of Data Silos & How to Eliminate Them Keep reading

What Does Poor Data Quality Look Like?

To check the quality of governance data in your company, you don’t need to put all your employees on their toes and conduct rigorous audits. You can spot signs of poor data in your daily tasks:

These are five signs that you should consider using data governance to ensure data quality.

1. Reports from different departments show different numbers for the same metric;
2. A single customer appears five times in your system under slightly different names;
3. Compiling a report takes days (or weeks) because no one trusts the data;
4. Your dashboards show empty fields where essential data should be present; 
5. The information looks different across all the sources available.

The worst part is when every tool you use to make your company more efficient and insightful is impacted. For example, business intelligence (BI) dashboards start showing misleading insights. Or AI models learn from low-quality inputs and deliver faulty predictions.

Data governance is the answer to organizing your data, ensuring its security, and generating error-free insights.

Data governance is a structured way to manage data in a company that can work on its own or be part of a bigger data management system. Using data governance to ensure data quality means setting clear rules, accountabilities, and responsibilities around data management.

Data Governance Framework for High-Quality Data

But data governance doesn’t happen by accident – it requires a clear and consistent framework involving people, processes, and policies. So let us explain each of the data quality and data governance pillars.

Governance Operating Model

Every employee in your organization manages data: some create new information, some update it, and some make use of it. 

Make sure you define the following roles for improving data quality: 

• Data owners: Who will be responsible for data accuracy and quality in a specific area (e.g., customer data, sales data)?
• Data stewards: Assign people who work with data daily and will be responsible for monitoring its quality and reporting problems (analysts or operations managers);
• Data users: Teach your employees the importance of data and how they can use data for business decisions.

Of course, building your data team isn’t always so straightforward a task, so you may need to consider bringing in experienced data governance consultants to guide the process.

This framework template is your starting point – now let’s tailor it to your business. Customize it with us

Policies and Procedures

Once you have distributed responsibilities among your employees, it’s time to implement data processing guidelines:

• Data entry standards help create consistency in data formats (e.g., name formats, date formats);
• Data update procedures outline how you plan to update existing data, including the frequency of updates and how to track changes;
• Access control defines who has access to specific data, ensuring that sensitive information is only available to authorized users.

Processes

You may  make it smoothly through the first two stages on your own – but where things usually get more complex is when companies come to data governance must-have operations. This is where a skilled data engineering team can make a difference. 

These are the essential processes your data governance should cover:

The key processes that make up effective data quality governance

Understanding the Data

In the early stages of the process, your framework figures out what data you have and where it comes from.

• Data profiling checks missing values, unusual formats, or numbers outside the range. 
• Data lineage shows where your data comes from, how it moves, and how it changes over time. Think of it as a “data map.” 
• Data cataloging makes finding, understanding, and managing data easier, especially for large enterprises that use big data processing. 

Protecting the Data

Once you’ve figured out what data you’re working with, it’s time to ensure that it’s protected.

• Data security: Measures to protect your data from unauthorized access, leakage, or breaches.
• Data compliance: Ensuring that your data practices comply with legal and industry regulations such as GDPR, HIPAA, or CCPA. 
• Data lifecycle: Data management from collection to retention or deletion.

Transforming Data Life Cycle Management with Generative AI Read more

Curating the Data 

With curating, you’ll be able to preserve data usefulness and connectivity.

• Data quality management is a set of rules and measures for fixing all discovered inconsistencies by cleaning up duplicates, correcting inaccurate entries, filling in missing fields, and standardizing formats.

Remember that quality control is not a one-time task: we recommend implementing regular quality control cycles to flag low-quality data. The best option is to check data quality in data governance several times:

• After profiling to identify new errors;
• During quality management to track progress;
• During compliance checks.

Our experience shows that a cyclical method of auditing your data is the best way to maintain high data governance quality and quickly spot any deviations from your standards.

• Data integration combines data from different sources (such as CRM, ERP, and websites) into a single location. If your data is well integrated, your team will have a complete view of your business rather than isolated data pieces. 

Step-by-Step: How to Ensure Data Quality with Governance

The data governance framework might seem overwhelming, but CHI IS here for you to share our experience: through real projects, we’ve built a clear, step-by-step strategy to help you progress toward more valuable data.

Take these essential steps to build a data governance framework and start getting valuable insights.

Step 1. Understand the Current State of Your Data

It may be challenging to start improving data quality if you have no idea what you are working with. Start by scanning your systems for duplicates or incompatible formats.

What to do:

• Identify all data sources, types, and storage locations;
• Use profiling tools to get the whole picture.

Tools you can use:

• OpenMetadata for data discovery and quality checks;
• OpenRefine for exploring and cleaning up datasets.  

Step 2. Standardize What “High-Quality Data” Means for Your Company

Your idea of quality data may differ from another company’s. Establish clear rules that everyone can easily follow.

What to do:

• Define what clean and complete data looks like for your team;
• Document these standards.

Tools you can use:

• OpenMetadata to define and track your own data quality rules;
• Collibra, Alation, or Google Data Catalog to set rules and metadata. 

Data Engineering Strategy: Benefits, Challenges & Best Practices Continue reading

Step 3. Implement Practical Tools and Processes

Once you have set your goals and standards, implement the technology and structure to support them.

What to do:

• Automate everything you can;
• Use tools that effectively clean, store, and visualize your data.

Tools you can use:

• Airflow (or Google Composer) for automating data flows;
• Snowflake or BigQuery for storing structured data;
• Azure Synapse for consolidating data sources;
• DBT to transform and clean data inside your data warehouse;
• AWS Lambda for flexible and cost-effective data processing.

We know this list can seem like a lot, especially if you’re just starting to work with data processes. If you feel lost, a smart move is to team up with a reliable data engineering company like CHI Software, which helps clients in fintech, healthcare, retail, and beyond for years. We know how to choose the right tools for your needs and get them working quickly.

Step 4. Define Data Stewardship Roles Clearly

You need people responsible for your data’s cleanliness and usefulness.

What to do:

• Create roles such as “data manager” or “data owner”.
• Build role-based dashboards to help stewards and owners track missing values, anomalies, or update frequency. 
• Define what happens when data stewards discover a data issue. What are their next actions? How fast should they react? 
• Educate stewards and owners on why data quality matters and what impact it has on business, compliance, and analytics accuracy.

Tools you can use:

• Jira or Asana for tracking tasks and responsibilities;
• Alation or Collibra to assign data owners and stewards.

This step may seem purely formal, but skipping it can have a tangible impact in real life. One of our clients experienced the consequences firsthand: a leading mobile technology company was struggling with inefficient data management, which was causing constant confusion and slowing down development.

CHI Software immediately recognized a familiar pattern and implemented OpenMetadata to track data provenance and assign responsibility. Now that effective data governance is in place, every department knows who is responsible for what – making the reporting process go smoother, and decisions made faster. 

Step 5. Measure and Monitor Data Quality

If you want your data to be trustworthy over time, you need to track its quality continuously. If you’re not measuring data quality, you will never know if your efforts are taking you in the right direction. 

What to do:

• Regularly check the status of your data;
• Set up error notifications (e.g., missing values or unusual spikes);
• Choose business KPIs.

Tools you can use:

• Monte Carlo or IBM Databand for monitoring pipelines and detecting anomalies;
• Google Sheets + bots for notifications with minimal coding.

Here’s another example of how it works in real life: one of CHI Software’s advertising clients couldn’t connect their ad engagement metrics to real business results quickly enough, and it was slowing every decision down.

Our team built a bot that tracked performance changes in real time and sent hourly updates. We also created a custom-tailored Google Sheets dashboard where the marketing team could easily compare ad metrics (clicks and conversions) with internal KPIs (purchases).

This combination brought the benefit of cutting evaluation time in half, smarter budget utilization and, ultimately, a noticeable boost in profits.

Good governance shows in the numbers. Let’s make sure yours are telling the right story. Validate your data

Step 6. Make Data Governance and Quality a Habit

You wouldn’t ignore your operations or finances for weeks – so why treat your data any differently? High-quality data governance should be part of your company’s daily routine. 

What to do:

• Include quality checks in your data processing workflow;
• Create dashboards that people will use every day.

Tools you can use:

• Airflow, DBT, and ETL pipelines for automating repetitive tasks;
• Power BI, Looker, or Superset for turning numbers into stories.

Step 7. Train Teams to Prioritize Data Quality

Data routine only works when every team member understands why data quality matters to the business. You should not explain why meeting deadlines or serving customers is crucial, so do everything you can to integrate data awareness in your company culture as well.

What to do:

• Hold short workshops or training sessions;
• Keep your data clear and accessible;
• Celebrate data quality improvements as a team.

Tools you can use:

• DataCamp or Coursera for deeper learning;
• Confluence or Notion for simple guides;
• Power BI to turn the data into visual dashboards.

Conclusion 

Data quality and data governance are inseparable: one cannot thrive without the other. While many companies still struggle with managing and using data, this guide should help you understand how a data governance framework works, its key elements and processes, and how to build them properly.

To turn all this knowledge into real results, you’ll need the right partner – preferably, one with over ten years of experience, 70% senior certified data engineers, and more than 30 successful data projects – which is exactly what you get when you partner with CHI Software! 

Join the companies who already trust CHI Software with their data. Let’s work together

About the author

Sirojiddin Dushaev Lead Data Engineer & Cloud Solutions Architect

Sirojiddin is a seasoned Data Engineer and Cloud Specialist who’s worked across different industries and all major cloud platforms. Always keeping up with the latest IT trends, he’s passionate about building efficient and scalable data solutions. With a solid background in pre-sales and project leadership, he knows how to make data work for business.

Yana Ni Chief Engineering Officer

Yana oversees relationships between departments and defines strategies to achieve company goals. She focuses on project planning, coordinating the IT project lifecycle, and leading the development process. In their role, she ensures accurate risk assessment and management, with business analysis playing a key part in proposals and contract negotiations.