How to deliver consistent data quality and insights at scale with MongoDB

With data’s increasingly important role in business decision making, data quality and consistency are essential to ensure the information can be relied upon. 

Organizations without a proactive data quality strategy in place are at greater risk of errors. This can delay projects, increase the need for rework, and put those involved at risk of falling foul of compliance and legislation. It can also lose customers. 

There’s a financial impact too, as a 2021 Gartner report suggests bad data quality costs organizations an average of $12.9 million per year. There are also longer term risks, including reputational damage and missed opportunities that competitors with more accurate data may be able to take advantage of first. 

The benefits of data quality

Those with high-quality data at their fingertips can reap the rewards, providing they are able to correctly manage it and maximize its potential. This includes benefits to:

Productivity and project delivery rates 

Fewer errors in production will prevent bottlenecks and help more projects be completed on time. Validated data also improves confidence that the information meets quality standards, so teams can focus on building functionality rather than fixing data issues.

Customer experience

Accurate, easily accessible data is good news for end users. This can help increase satisfaction and increase the likelihood of them staying with you for the long term. 

Reduced risk 

Manual data handling increases risk of input errors, especially when dealing with large amounts of information. A repeatable data validation process saves time and greatly reduces risk and exposure to vulnerabilities.

The pros and cons of managing data quality with MongoDB 

As a document/NoSQL database, MongoDB is a popular option for organizations that need a flexible, scalable, schema-less data and easily queryable data storage option.

However, those looking to get data quality management with MongoDB right need to overcome issues such as a lack of validation, schema discrepancies, and complex data migration processes.

These can cause data-related errors and the issues mentioned above. To get the most from MongoDB and deliver data that supports strategic decisions, businesses need to design a data strategy framework that addresses these challenges:

Errors during production

Data errors in production environments can occur when data is incorrectly entered, structured, or validated within MongoDB.

Data validation 

While MongoDB’s schema flexibility offers advantages, data structures can become inconsistent over time. No rigid schema makes it difficult to check all data entries align with the expected structure or format, creating potential mismatches in downstream applications that rely on the data’s consistency.

Synchronizing with other systems

Many organizations use MongoDB alongside other systems, such as relational databases or ERP systems. These businesses need to streamline data validation, monitor schema consistency, and keep MongoDB in sync with other systems to reduce the risk of using outdated or inaccurate data for strategic decisions.

Achieving reliable data quality 

To effectively manage data quality in MongoDB it’s important to easily be able to validate data, catch and correct errors and manage multiple connections. 

Visualize data 

The ability to visualize data, using a tool such as a schema explorer is a huge advantage. This allows teams to quickly get an overview of data structures, making it easy for them to find anomalies and data outliers, such as duplicates, misspellings and other unexpected fields, in your MongoDB schema. 

Automate validation

Look to automate the verification of data entries. This reduces the need for manual data validation, speeding up the development process. It also allows you to be more precise about what is going into the database, keeping both the data and code clean.

A single view of all data 

Users need to be able to manage multiple connections and databases at the same time. Visualizing data is critical for success in MongoDB, so select a tool that allows users to quickly compare data across MongoDB and data in external systems. This single source of truth means teams can spot and fix discrepancies with ease. This is invaluable for organizations running large-scale MongoDB deployments. 

How Studio 3T can help

Call us biased, but Studio 3T is the ideal solution for businesses needing to deliver consistent data quality. Our users love Studio 3T as it enables them to gain better visibility, manage and extract data, ensure data accuracy, quality and consistency, while also keeping MongoDB running smoothly. We also believe in listening to solve your MongoDB challenges. As a business leader this means you can confidently make strategic decisions, safe in the knowledge the data used to inform them is accurate.