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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%.
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.
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.
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 can be considered duplicative, missing key elements, or data that hasn’t been standardized.
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.
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 pill to swallow when you realize 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 cashflow problems that are caused by data errors. Poor quality can cause a handful of headaches — some more severe than others.
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.
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.
Storing data incorrectly or collecting it in a questionable way can lead to hefty fines. Ensuring your data collection, storage, and organization methods are above water is crucial for sticking to data governance and avoiding any unnecessary fines that can impact your bottom line (and reputation).
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.
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.
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.
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 each year.
Now you know how detrimental poor-quality data can be for your business. Here are a few ways you can minimize the number of errors in your system.
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.
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.
It’s easy for data to get diluted if different team members or different departments are dealing with it in different ways. Create standardized 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.
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.
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:
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 standardized blueprint for your entire team, you’ll reduce the number of errors and ensure you’re using information from a single source of truth.
Learn more about building the right data structure for your business and how to streamline and optimize your operations with “The Brand CIO’s Guide to Product Data Architecture.”DOWNLOAD GUIDE
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