When it comes to data, quality matters, and low-quality data can cost you.
It could mean a loss in revenue, inaccurate analysis for your customers, compliance fines, or a damaged brand reputation. Your data shouldn’t cost you money.
Poor data quality has been said to cost organizations up to $2 trillion per year. So what would non-quality data cost you?
To answer that question, let’s analyze the difference between good and poor data, the dimensions to prioritize, and how to improve your data with Digital Experience Intelligence (DXI).
The low down on data quality
Before we talk about data as a whole, it’s important to clarify its importance.
Essentially, data is of high quality if the data correctly represents what it describes. It’s an essential part of data governance that ensures your data is fit for its intended purpose.
Companies know they have high-quality data when they’re able to put it to use—communicating effectively with their customers, finding new ways to serve them, and more. It refers to the overall utility of a dataset and its ability to easily process and analyze it for other uses.
A rule of thumb: quality data is actionable data.
On the flip side, low (or poor) quality data hides valuable opportunities and cab leave a company struggling to identify shortcomings. As it is innately less trustworthy, it has to be verified repeatedly for accuracy—a timely process for busy teams.
In essence, reliance on data you’re not sure about to make business decisions yields an inadequate standpoint for teams to grow.
The core dimensions of data quality
Depending on what industry you’re in, prioritizing your core dimensions of data quality is important to not only support use cases but to obtain high-quality data. Here are five core dimensions of data quality to consider prioritizing today:
Accuracy: The certainty of data that reflects the information or event it’s intended to represent. This data is measured by how the values agree with the known information source.
Validity: The data must comply with business rules and fall within the data parameters when those rules are applied.
Completeness: The data meets all required values and records available.
Timeliness: Data is updated in real-time, to ensure that it meets user requirements for accessibility, accuracy, and availability.
Consistency: Data values are the same across all instances of an application.
One way to help prioritize your data is to use a Digital Experience Intelligence tool such as FullStory. With a DXI platform, you can access complete customer experience data, and definitively learn how users interact with your site or app.
Want to take your digital experience to the next level? Let’s chat.
Improve and enhance data quality with DXI
Instead of anecdotal, low-quality data gathered from NPS surveys, for instance, DXI allows product, UX, and engineering teams to zoom in on how users really interact with your website or app via Session Replay.
Real, logged interactions are the ultimate in quality data.
And, when you improve your data quality with DXI, good things happen:
Increased revenue with better conversions
With high-quality data, identify revenue-impacting issues, and understand how customers are affected by your product. How? Contextualize data with real user Session Replay.
Improved organizational efficiency
Empower engineers with the data they need to identify, troubleshoot, and remediate bugs efficiently and effectively.
Scalable customer growth and improved retention rates
Use data to fuel positive customer experiences and confidently iterate to improve your digital intelligence—keep customers coming back.
FullStory’s Digital Experience Intelligence platform proactively surfaces where users experience confusion or frustration.
Request your custom demo and start building a more delightful user experience today.