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A Red Sign Saying Wrong Way on a Busy Street Bad Data 101 Salsify-1;

Bad Data 101: How Errors Affect Your Business Performance

11 minute read

Data is a major part of business. It tells you who’s buying your products or services, highlights how your business is doing, and offers a potential glimpse into the future.

But if the data you have is incorrect, duplicated – or simply not relevant – it can do your business more harm than good. This is especially true for product data, as “2023 Salsify Consumer Research” found that 55% of global consumers wouldn’t purchase a product online due to bad product content.

Additionally, according to research from tech consulting firm Gartner, 60% of businesses report they don’t measure the annual financial cost of poor data quality. Global consulting firm McKinsey & Company also notes that investing in data architecture, which is the process of managing data, can yield short-term savings (about six months) of 5-10%.

What Is Bad Data?

Bad data refers to any data that’s wrong or not relevant to your business. This data might miss critical information, be considered duplicate, not be compiled correctly, or simply not be helpful. For product data, this could be incomplete or inconsistent product information – or product content that lacks answers to shoppers’ biggest questions about your products.

While it might feel like the more data you have, the better, it’s not so cut and dry. If the data can’t be used for the purposes you need it for, or you’re just storing it for the sake of it, it’s still considered bad.

These are the most common causes of bad data.

Human Error

Manually entering data is rife for error (and it’s likely not as simple as the inputter skipped their morning cup of coffee). Especially if your business operates globally, there might be a disconnect between the data your European team is inputting versus the data your U.S. team is inputting.

Old Data

What was once a source of truth soon becomes old news, and this can dilute the findings you get. For example, the majority of customers might have discovered your product via ads two years ago, but today they might find you via organic traffic. If you’re still keeping the old data in circulation, you’ll get a false reading about your most popular acquisition source.

Inaccurate Data

Inaccurate data can be considered duplicative, missing key elements, or data that hasn’t been standardised.

Manual, Time-Consuming Processes

Collecting product content often requires manual, time-consuming processes to gather the most up-to-date product data, including navigating back-and-forth emails and endless spreadsheets. These are prone to error due to the sheer length of time required to collect all of the necessary information. 

The Potential Costs of Bad Data on Your Business

Bad data can be detrimental. If you’ve been making decisions based on what you thought was a single source of truth, it can be a hard pull to swallow when you realise you’ve been leaving money on the table.

Product data errors are particularly irksome because they can have a huge impact on your business’s bottom line. But it’s not just cash flow problems that are caused by data errors. Poor quality can cause a handful of headaches – some more severe than others.

Bad Data Can Drive Away Shoppers

As more shoppers rely on omnichannel shopping experiences, including using their smartphones inside of brick-and-mortar stores, bad product data can impact their shopping experience and cause confusion. This incomplete or outdated product data can lead to lost sales.

Bad Data Can Increase Product Recalls

If your data is good and accurate, it can help you identify customers who might be affected by a recall and reduce the risk of a mass recall – something that ultimately will cost you a lot of time and money.

Bad Data Can Lead to Fines

Storing data incorrectly or collecting it in a questionable way can lead to hefty fines. Ensuring your data collection, storage and organisation methods are above water is crucial for sticking to data governance and avoiding any unnecessary fines that can impact your bottom line (and reputation).

Bad Data Can Impact Productivity

It becomes increasingly hard to maintain momentum when teams aren’t able to effectively collaborate across departments. As a result, productivity levels can plummet, particularly if teams don’t have the correct data they need to move forward and make decisions.

Bad Data Can Lead to Bad Decisions

It’s impossible to make accurate decisions when you only have poor data to reference. When you’re basing your next move on inaccurate, patchy information, you’re inevitably going to make decisions that don’t benefit your business or its customers in the best way.

Bad Data Can Disrupt Critical Business Processes

One single piece of poor-quality data can affect multiple different business processes. For example, incorrect manufacturing data can affect the entire production line, while inaccurate product labels can cause an entire shipment to be returned.

Bad Data Can Increase Costs

Using wrong information can leave money on the table, but it can also increase your overall costs. Dealing with inaccurate data can be expensive – especially if you have a lot of it. You’ll need to assign several resources to fix the discrepancies, which can take precious time away from money-making activities. According to Gartner, poor data quality can cost businesses an average of $9.7 – $14.2 million USD each year.

How To Mitigate Bad Data and Data Errors

Now you know how detrimental poor-quality data can be for your business. Here are a few ways you can minimise the number of errors in your system.

Check Your Sources

The only way you can be sure your data isn’t bad is to go back to the start. You might be sourcing information from the wrong places or focusing on redundant metrics. Take the time to identify the sources that collect good, useful data, and figure out the end goal of your data collection.

Ensure Your Data Collection Techniques Are Accurate and Relevant

There’s no point in collecting qualitative data if you want to get a percentage figure of people who have bought more than three products. Examine your data collection techniques to see if they align with your goals or the type of data you want and need to collect.

Create Streamlined, Standardised Processes

It’s easy for data to get diluted if different team members or different departments are dealing with it in different ways. Create standardised processes that everyone can follow to ensure the same data is being collected each and every time. This will include things like whether monetary values should be in U.S. dollars or Euros, and which information is a must-have versus a nice-to-have.

Eliminate Redundancies

If you’ve got a lot of poor-quality data, there’s a good chance you’re collecting the same information from multiple different sources that could be contradicting each other. Identify where each piece of data is coming from and make sure it’s the sole way you’re getting your hands on that specific data. At the same time, comb through existing data and get rid of any duplicates or redundancies.

Use Technology To Automate Data Entry Tasks

Human error is one of the biggest causes of poor-quality data. All it takes is for someone to enter the wrong number or skip a section on the intake form for things to go wrong. Instead, incorporate automated processes into your data collection systems to reduce the chance of human error and streamline the process. There are plenty of tools that will automatically eliminate duplicates or flag incomplete data.

Integrating an ecommerce technology solution like a product information management (PXM) platform into your data collection and management processes can drastically reduce the number of errors and help you drive business performance. 

Ecommerce technology can help your team:

  • Ensure high data quality and security: You can choose what kind of data you want to collect, provide parameters for the tech to adhere to, and make sure you’re storing data securely to avoid any unnecessary fines.
  • Leverage tools to succeed: Arm your staff with tools they can use to collect, store, and analyse data in one central place.
  • Identify errors quickly: It can take a while for the naked eye to find a data error, but technology can do it in milliseconds.

Say Bye To Bad Data Forever

Data errors can be a tiny crack in your business’s foundation that expands over time if not remedied correctly.

Successful data architecture can diffuse this by helping you plot the management, collection, distribution, and consumption of the data you have available.

By setting up a standardised blueprint for your entire team, you’ll reduce the number of errors and ensure you’re using information from a single source of truth.

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