In our current digital-facing world of business operations, ensuring optimal data quality has become paramount to a company’s success. While possessing extensive data is a key component of understanding both your users and your own business, verifying that this data is reliable, and trust-worthy, is even more crucial—which is where data quality comes into play.
In this article, we’ll outline how high-quality data can instill companies with the confidence necessary to make strategic business decisions while touching on the various dimensions of data quality that help make this possible. Text
What is data quality and why does it matter?
Every business has some form of data, with key components including customer contact information, sales metrics, and general strategic overviews. But, data is not by default always “quality” or optimized—meaning every company’s level of data quality is bound to look different.
So what is data quality, and why exactly does it matter to your business? In a nutshell, data quality refers to the degree to which data is reliable, effective, and fit for its specified purpose. While having the aforementioned information on hand—such as user data—is necessary for ordinary operations, data quality will play a key role in how well you can leverage this information for decision-making and maximum operational efficiency.
The dimensions of data quality
Before you can begin to use your data to drive insights, reach customers, and ensure overall seamless operational processes, you’ll want to verify that your data is accurate and capable of driving results with the help of data quality dimensions.
Data quality dimensions are attributes that help in assessing the quality of data. These dimensions are critical for ensuring reliability in executing key business decisions and strategies. Let’s take a look at what these dimensions are and how they can be achieved within your business:
1. Accuracy
When assessing the quality of your data, accuracy should be at the top of your checklist. Accuracy ensures that data is correct and represents reality, which is essential for making informed decisions and minimizing financial loss. An example of data accuracy includes a user’s contact information in your database matching their existing phone number, address, email, and more.
2. Completeness
Completeness means ensuring that all necessary data is present. Missing data can lead to incomplete analysis and inaccurate conclusions. This dimension focuses on filling in the gaps and collecting all required data elements.
When it comes to customer data, a single customer view can help obtain this data quality dimension with ease by identifying and connecting data across datasets. This means validating that data is both complete and comprehensive—a capability, which is key to accurate analytics and outreach.
3. Consistency
Consistency assures that data across different databases or systems is uniform. This is crucial as inconsistent data can cause confusion and unreliable results in data analysis. When you find yourself using data stored in various locations, take the time to review these various datasets to guarantee they’re the same despite their location. For example, you may have a full name in one database but only initials in the second.
If there are any inconsistencies in fact present within your user data, consider data cleansing processes that will eliminate duplicate records and provide accurate up-to-date information on your customers.
4. Timeliness
Timeliness focuses on data being up-to-date and available when needed. Stale data can be misleading, and timely data is essential for making decisions that reflect current realities. Make sure you have your data ready when you need it most—whether it’s year-end performance metrics or client emails that will be crucial for your next email marketing campaign.
5. Uniqueness
Uniqueness ensures that data is not duplicated. Since duplicate data can distort analysis and lead to inaccurate reporting, prioritizing data uniqueness helps in maintaining the integrity of data sets.
Gain a more holistic view of your data by leveraging data matching to obtain a single customer view. With a single customer view, you’ll find better efficiency in operations knowing that your team won’t be searching multiple databases or viewing duplicate records. With duplicate datasets removed, achieving unique data becomes much more feasible.
Harnessing the power of high-quality data
Data quality is foundational for business success. With the aforementioned data quality dimensions in mind, your business is much more likely to achieve the true potential of your data assets while mitigating risks and making the most well-informed decisions for your organizations.
At Experian, we’re here to help you get started. Connect with one of our data quality experts today to learn about our data quality software and tools that will help empower your team to make future data-driven decisions with success.
