Poor-quality data costs the U.S. economy around $3 trillion a year and impacts the bottom line of 88% of American companies. That's why data reliability is so critical. It prevents inaccurate, inconsistent, or just plain bad data from moving through your pipelines

Five things to know about data reliability for business success:

  • Data reliability is the process of ensuring data in your organization is complete, accurate, consistent, and compliant.
  • Human error, data degradation, failure to update systems, and other factors can lead to unreliable data.
  • Using reliable data sources, data profiling, and data testing can improve data reliability.
  • Updating data regularly and training employees on how to manage data can also improve reliability.
  • The right data pipeline tools will ensure data is reliable when it moves from one location to another.

This guide explores why data reliability is crucial for business success and how you can collect, process, share, and analyze better-quality data. 

Table of Contents

Why is the reliability of data important?

When data is unreliable, it can impede data management and analysis, making it difficult to generate value from all the information in your organization. Businesses need reliable data to make informed decisions, stay competitive and maximize profits. Data reliability also helps create excellent customer experiences by providing the most current information for products and services. 

What are the business benefits of reliable data?

High-quality reliable data can prevent wasted resources, improve compliance, enhance business workflows, and lead to more accurate data analysis and decision-making. Having reliable data can also help businesses identify areas needing improvement and opportunities for growth. Ultimately, reliable data allows businesses to gain a better understanding of their customers, operations, and financial performance. 

Why Is Reliable Data Important in Business?

Every data-driven organization requires reliable data that is valid, precise, and consistent. That helps them understand their business processes and make more informed decisions. When data is unreliable, your business can experience the following problems:

Poor Decision-Making

Unreliable data that's inconsistent or incorrect can skew your business decisions, resulting in unsuccessful outcomes. For example, you won't be able to draw conclusions, identify patterns and trends in big data sets, or get any value from business intelligence tools that require reliable data to generate valuable insights. 

Inability to Comply with Data Governance Frameworks

Data governance frameworks in your region or industry might forbid collecting, processing, and sharing inaccurate data. Failure to adhere to this rule could result in expensive fines from the government. GDPR, for example, stipulates that data collectors must take reasonable steps to erase or rectify inaccurate data. A no-code data pipeline platform like Integrate.io can help you improve data accuracy when moving personally identifiable data between different locations. 

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Breakdown in Business Workflows

Good data is required to execute day-to-day workflows and make data-driven decisions in your business. Say your marketing team has inaccurate insights about potential customers, such as out-of-date contact details. They might waste time targeting the wrong leads with marketing materials, resulting in decreased revenue.

Inability to Identify New Opportunities

Unreliable data makes it challenging to find and capitalize on opportunities that might take your business to the next level. For example, if data is incomplete or incorrect, you won't be able to identify cost-cutting measures in your organization.  

Where Does Unreliable Data Come From?

Unreliable data can come from almost any data source in your organization. There are various reasons why data might be outdated, inaccurate, inconsistent, incompatible, or incorrect:

Data Degradation

Data can degrade over time when kept in hardware like data storage devices, flash memory sticks, and solid-state drives. Hardware stores data using electrical charges, which can break down because of a lack of insulation. Moving data to the cloud can improve data reliability — but only if data hasn't already been degraded. 

Failure to Update Data Systems

Data reliability issues can occur when team members fail to update systems with the correct data. For example, incomplete customer data profiles can make it difficult for sales and marketing teams to learn about leads because of a lack of information. 

Human Error

Team members who make mistakes when updating systems can also cause data unreliability. Data sets might contain spelling errors, incorrect information, missing fields, and other oversights that jeopardize data quality. 

Using Data from Legacy Systems

Businesses often make the mistake of moving data from legacy systems through their data pipelines, even though that data no longer has value. When combined with current data sets, legacy system data can negatively influence analysis, produce inaccurate insights, and cause data quality issues. 

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How To Ensure Data Reliability

There's no such thing as guaranteeing data reliability, as no data sets will contain 100% accurate information. People make mistakes, data can degrade, and data bias can impact the quality of information in an organization. However, you can ensure data reliability as much as possible in the following ways: 

Data Profiling

Data profiling or data validation involves reviewing existing data to ensure it's accurate and consistent and has the correct structure. The right tools can catch errors that might impact data reliability and send alerts to users when poor-quality data exists in systems. 

Data Testing 

While data profiling concerns existing data, data testing ensures new data sets meet internal and industry standards. Again, the right digital tools, such as Integrate.io, can automate this process and improve data reliability. 

Ensuring Reliable Data Sources

You might keep third-party data you collect from external sources in your systems. Some data sets are more reliable than others, depending on where you got them. For example, government statistics or marketing data from a reputable company are more trustworthy than unsourced news articles or unverified scientific papers. Incorporating data from reliable sources into your systems will improve data reliability.

Training Employees on How To Use Data

Human error is one of the top reasons for unreliable data. When team members input incorrect or inconsistent information into a system, it can impact an entire data set. Training employees on how to use data properly can improve data reliability and result in high-quality data. This process might involve teaching team members how to properly collect and verify the information before entering it into a database and adhering to data governance principles like GDPR and CCPA. 

Updating Data

One of the best ways to ensure data reliability is to update data sets as soon as possible. For example, when customer service teams receive new information about customers, they should enter it into a customer relationship management (CRM) system immediately rather than writing it down on paper. Keeping your records up-to-date will ensure the information in your organization is accurate.

How Integrate.io Can Help With Data Reliability

Poor data quality can impact your bottom line and render it difficult to generate value from the information in your business, making data reliability more important than ever. Integrate.io's data observability feature solves reliability issues by detecting data-related problems as you move information from one location to another for data analysis. You'll even receive alerts when the no-code data pipeline platform identifies threats that might impact your business intelligence initiatives. This way, you can prevent unreliable data from jeopardizing your business operations. 

The great thing about Integrate.io is that it automatically identifies data-related issues during the data integration process. That means you don't need to know lots of code or hire data engineers to improve data reliability. 

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Other Integrate.io features include:

  • Move data between locations with no-code ETL (extract, transform, load) and Reverse ETL pipelines in minutes using a simple drag-and-drop interface. Set up an ETL trial meeting
  • Perform ELT (extract, load, transform) and CDC (change data capture/data replication). Integrate.io is the industry's fastest ELT platform, allowing you to unify data every 60 seconds in a single source of truth. Set up an ELT trial
  • Create secure and self-hosted read/write REST APIs for your data products with Integrate.io's API management.

Integrate.io is the no-code data pipeline platform that helps you reach your data reliability goals. Why not sign up for a 14-day free trial? Or schedule an intro call with one of our experts to identify your data integration pain points and discuss solutions one-on-one.