How Inaccurate Data Impacts Your Business Operations


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Data is one of the driving forces of modern organizations. But what happens if the data being collected is full of errors? Or duplicates of data you already have? Or formatted improperly? Inaccurate data doesn’t just look bad, it has the potential to impact business operations as well. 

Bad data can lead to many operational interruptions, but most concerning is the cost and security risk it brings organizations. According to Gartner, a leading technology research and consulting firm, bad data costs organizations over $12 million on average each year.

Let’s look at all the ways inaccurate data affects business operations and how to address these and other data collection challenges.

The true cost of poor data quality

When considering how much poor data quality costs organizations, it’s easy to picture a monetary loss — like the $12 million found in the research from Gartner. But inaccurate data, while costing millions, creates significant challenges for day-to-day operations. Think about the information bottlenecks and stagnated workflows that happen without clean, usable data. And this is just the beginning. Here are the main ways inaccurate data keeps your organization from operating efficiently and effectively.

1. Missed opportunities

Every department in an organization, from marketing to IT and sales, relies on data. But when this data is outdated, incomplete, or full of duplicates, it is nearly impossible to be effective. For example, if a lead capture form does not have properly formatted phone number or date fields, a new lead could input information incorrectly without getting flagged. Similarly, bad customer or product data makes it difficult for executive teams to make smart, data-centric decisions that help propel their organization forward and increase revenue.

2. Productivity loss

Manually correcting data errors isn’t simply tedious, it often wastes time and can impact a team’s overall productivity. In fact, too much manual work fixing inaccurate data was the leading cause of productivity loss for data teams, according to Datafold’s The State of Data Quality in 2021 report. The report also revealed that almost 60% of those surveyed manually validated data for quality and over 45% had no tools available to ensure data governance or quality. Poor data quality also means the time teams spend doing tasks with inaccurate information is time ultimately wasted.

3. Poor user experiences

Customers expect more personalized experiences, but they’re also wary of how organizations are using their data. It’s no surprise that poor data quality leads to poor user experiences. If inaccurate, duplicate, or error-ridden data makes its way into a CRM, these incorrect details will make their way into prospecting calls, customer communication, and more. This not only makes it difficult to sell to new leads or have effective marketing, but can reduce customer satisfaction and potentially harm an organization’s image.

4. Lack of compliance

Data compliance regulations such as HIPAA, GDPR, and GLBA set high standards for how organizations must govern data while in their care. Under these regulations, organizations must comply with guidelines on data minimization, transparency, security, and accuracy as well as ensure individuals can access, update, or delete their data. Staying compliant with these regulations becomes especially challenging when organizations have inaccurate data that is difficult to govern or manage properly. Noncompliance due to bad data quality limits business operations, but can come with hefty fines and even reputation damage if not corrected.

5. Security risks

Poor data quality can also put your organization at risk for data breaches. These incidents only take minutes, but can have a lasting impact on your organization’s operations and reputation. Poor data governance that increases an organization’s security risk include: no standardized policies or procedures, lack of permission controls, low visibility, data silos, and more. Without consistency or validation, organizations can quickly end up with poor quality data that slow business operations.

How to improve data quality at your organization

If inaccurate data is affecting business operations, there are several ways to improve the quality of this information. High-quality data is not only good for business, but makes it easier to stay compliant with data privacy regulations and maintain better security.

  • Establish a data quality standard: Data will never be consistently good quality without set data governance policies and procedures. Determining the standard for collecting, processing, and managing data will provide guidelines for those who routinely handle this information.
  • Fix current data errors: Maintaining data quality will need to start with correcting the inaccuracies of current data. This will include removing duplicates, correcting errors, updating old entries, and addressing incomplete data.
  • Understand data at the source: While it is necessary to clean up bad data that is already in a CRM, it’s also important to address the quality of data at the point of collection. Setting formatting and verification standards for web form fields will help ensure that data is high quality before it gets imported into a CRM like Salesforce.
  • Minimize or eliminate data silos: Data silos often occur because different teams use different data collection methods or systems. Standardizing and automating these processes across an organization will address data inaccuracies while helping to alleviate time-consuming manual tasks.
  • Audit data processes often: Even with data standards in place, no system or policy is perfect. Conducting regular audits for data quality and processes can help identify errors before they become a major problem that takes significant effort to fix.

Faced with other data collection concerns?

Unfortunately, data quality is only one of the many challenges organizations face when collecting and processing data. Find out how to address other data challenges and how adopting a data stewardship mindset can help in our eBook, 5 Data Collection Concerns of Top IT and Security Execs and How to Address Them.

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