How can Dirty Data Impact Your Bottom Line?

What is Dirty Data, and How Can it Impact Your Bottom Line?

Our everyday lives rely on confidence in quality data. We trust that our bank accounts are accurate, our social security numbers are secure, and our insurance policies are up to date.

Dirty data is what happens when this goes wrong. Put simply, dirty data is information that’s unusable or untrustworthy for any reason. It’s the source of many everyday problems in business as well as life.

If we hand an outdated car registration to a highway patrol officer, we’ve shared outdated information and may incur a fee. If we tell a friend to meet us at the wrong address, we’ve shared incorrect information and cost them time and money for gas.

Our businesses are no different. When we sketch a prototype, set revenue goals, or sign up a new subscriber, we need to be able to trust that the information will be stored accurately. 

However, the vast majority of data leaders confess that poor data quality has cost their company revenue. If information is so crucial, how can we ensure we have high-quality data? 

Types of Dirty Data

While bad data comes in a variety of forms, it is almost always due to either technical problems or user input. To understand how to clean it, we first need to understand the ways it can get dirty.

Here are some of the most common forms of dirty data:

• Outdated: Useless information, like customer addresses from 10 years ago.
• Duplicated: Multiple copies of the same info, like two customer profiles in the same data set
• Incomplete: Some data is missing, either accidentally deleted or never collected.
• Inaccurate: Data that merges incorrectly, is not aligned or conformed to standards; Like entering in phone numbers (xxx) xxx-xxxx vs 1-xxx-xxx-xxxx,
• Irrelevant:Data that is collected and unused might answer someone’s questions, but it doesn’t provide value to your business goals.
• Corrupted: Completely unreadable data, like product image files on an external hard drive that has been damaged or data items merged with the wrong formatting.
• Siloed: Information that isn’t shared between departments causing inconsistencies in data points, like sales data that never reaches the marketing department or lacks a common field like a date, customer ID, or name.
• Missing:  Data that was never collected or is lost in the data supply chain when a company changes platforms or physical locations.
• Inaccessible: Information that’s not easy to find or cannot be accessed altogether, like financial data on old software that former employees used.
• Unsecured: Unprotected data that can lead to compliance issues, especially for healthcare companies who must comply with HIPAA regulations.

Even worse, a given bit of data can have more than one of these problems. Imagine if important customer records are both incorrect and inaccessible to a customer service agent. Next time that person calls, there’s a good chance you’ll lose them due to poor customer data management.

The Cost of Using Bad Data

The lower the quality of data, the more expensive it is to fix. Gartner estimates that, on average, organizations lose $12.9 million per year to low-quality data. 

Where does all this money go?

Expensive Data Errors

Stories abound of nightmarish data errors. 

In one case, an incorrect figure in a product dimension contract caused a mobile phone’s packaging to show up 10 times larger than planned. The remanufacturing cost the European company $25 million Euros ($26 million USD) to fix. 

Human error can affect even the most prestigious institutions, as NASA learned when a conversion issue from metric to imperial measurements cost them a probe – and $327 million.

Bad Business and Marketing Decisions

A study conducted by Forrester found that 21 cents of every dollar spent on marketing is wasted due to data quality issues. Even worse, this is a widespread phenomenon – 85% of data leaders concede that their company has lost revenue due to dirty data. 

Even the most innocuous errors can cost a company, like accidentally swapping billing codes that cause discrepancies in invoices. 

Major Delays

According to a 2021 study on data usage in the construction industry, up to 14% of all rework is caused by dirty data. While construction companies are quite literally rebuilding infrastructure, other companies are spending too much time wrangling bad data. 

According to Inc.com, one company admits to having battled computer crashes and memory errors due to spreadsheets simply being too large. This cost their employees extra time and effort.

Slowed or Stalled Growth

If bad data can delay projects and turn brilliant business decisions into blunders, then it’s not surprising that it can also slow company growth. 

In a 2019 study by Dun and Bradstreet, almost 20% of companies surveyed said they lost a customer due to dirty data. But losing customers is only half the problem of stalled growth. Another 15% of those surveyed shared that they lost a contract due to “incomplete or inaccurate” information about the potential client.

Compliance Issues

Sometimes, the problem is that the right people can’t access your data. Other times, it’s that the wrong people can. Exposed customer addresses, unprotected healthcare information, or security breaches are all examples of improperly secured data. 

Large companies have had to pay millions for bad data practices. If a business isn’t identifying security risks in their data management, they’re vulnerable to leaks and breaches, as well as the resulting fines.

Master Data Management = Confidence in Quality Data

The pitfalls of poor data can be averted with a Master Data Management (MDM) system. MDM creates a single source of truth for a business, where all business information can be polished, secured, sorted, shared, and used to provide overarching insights. 

A good MDM solution like Reltio can hold unlimited points of information and help build alignment between departments of an organization. It can standardize thousands of records, cleanse them, remove duplicates, and efficiently resolve errors. MDMs use machine learning and data matching to identify and merge similar customer and client profiles, and to constantly seek out issues and offer solutions. 

Managing data effectively can save a business millions, while costing much less. Reltio’s Connected Data Platform gives business leaders a birds-eye view, making it easy to identify opportunities to reduce costs and raise revenue.