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Reducing Errors and Improving Accuracy with Data Integration

Photo by Brett Jordan on Unsplash

Your errors aren’t just embarrassing, they’re more costly than you know.

The cost of data inaccuracies can be immense. US businesses are estimated to lose $3 trillion annually through bad data and the resulting poor decisions from using it.

Additionally, while organizations tend to have large volumes of data available to them, they’re typically only using around 50% of what they have for decision-making.

One of the core challenges is that data integration can be complex. When you’ve got massive volumes of data siloed in multiple sources or systems, you need a reliable way to integrate that data or pull it to a central repository. Your software stack needs to connect in such a way that accurate data can effectively flow to whoever needs it in the organization.

It comes down to needing data quality and accuracy so that your organization can reduce errors and make the best decisions based on all of the information that you have available. Data integration aims to give you a 360-degree view, eliminating blind spots.

Common challenges with data integration

It’s a relatively common story among businesses – you find a way to consolidate a range of legacy data sources into a single platform, only to find that large volumes of data exist outside of those sources. You’ve got shared files, desktops, and external data too. Data management can be a headache; you’re still left trying to solve the data integration problem. 

The underlying challenge boils down to many disparate data formats and sources, and large volumes of data to integrate. You might have different teams that access different data sources and use their own processes for inputting and updating data, making it a challenge to compile it as a “single source of truth.”

Another common challenge is that data simply isn’t available for team members where and when they need it. The data is siloed, which can result in the dissemination of inaccurate data or wasted time while people wait for access to the data they need.

The question of data quality is another frequent challenge. There are six dimensions of data quality that are typically accepted as standards for data management, such as accuracy, completeness, and timeliness. However, those data qualities become more difficult to ensure when you have large data volumes from multiple sources. 

Best practices for data integration

The main objective of data integration is for your organization to produce clean, consolidated data sets that meet the needs of information users. In other words, that data should be in such a state that it helps them to devise solutions to achieve their goals accurately and quickly.

Some best practices for data integration include:

  • Define clear goals first. What business objectives will data integration help you to meet? By articulating your goals first, you can develop a clear idea of the sort of technology solution you may need. (A common mistake is to try to prioritize technology before understanding business needs.)
  • Document your integration processes. This should include carefully cataloging integrated data and data sources so any users can find what they need. Documentation will be invaluable for any future data recovery needs.
  • Assemble a cross-functional team and ensure that roles and responsibilities are established. Without clear expectations, the process can become messy and take longer than expected.
  • Understand the data that you’re integrating. You must grasp data formats, structure, and quality to help ensure consistency and reliability during integration.
  • Establish a non-destructive integration process for analytical data sets. Users may need to access the original data at some point, and use cases can change over time.

Get more pointers from our Data Integration and Governance Report here.

Data integration techniques

There’s no such thing as a one-size-fits-all approach to data integration. The basic endgame is that data from different sources come together in a unified view, then are formatted to suit the end user. The data integration technique used can vary how that result is reached.

For example, some data consolidation methods leverage ETL technology to combine data from different sources, clean it up, then aggregate it in a data warehouse. A positive of ETL is that it helps organizations cut down on where data is stored and create a consolidated view. A negative is that ETL tends to focus on IT solutions over business solutions and is managed entirely by IT. This can mean that you lose sight of business goals in the process.

MDM (Master Data Management) is designed to solve business problems that result from inaccurate or incomplete data. MDM consolidates data but takes the focus from being IT-centric to business-centric. Implementation involves cross-functional teams aiming to solve real-world problems in the business. The technology is complementary to ETL, but with a different focus. The result is high-quality data management with more actionable insights to inform decision-making.

Data analytics and visualization

Data analytics and data visualization are key elements for becoming more data-driven. Data integration provides the platform to analyze and create visualizations of data more easily and accurately. They give data users practical insights to help drive business goals. 

Data integration provides the platform to analyze and create visualizations of data more easily and accurately. It ensures that the data fueling those insights is as accurate as possible.

Real-world examples

  • National Student Clearinghouse – NSC wanted to improve their customer support and the accuracy of data on all learners. They needed data integration from multiple legacy sources and achieved that with a MDM platform.
  • Chipotle – Data integration through MDM helped Chipotle to improve data accuracy, customer service, and system reaction time. 


Data integration is essential for any company wanting to reduce errors, improve accuracy, and achieve business goals. A consolidated data management approach helps ensure that users access a “single source of truth,” and avoids data silos.

Company data creation is expected to grow at a rate of 23% in the period to 2025, highlighting the need for stronger data management techniques. Data integration is essential for effective operations. 

Start your data integration journey now.