Regardless of what source collected the data or where it resides, it cannot contradict a value residing in a different source or collected by a different system. There must be a stable and steady mechanism that collects and stores the data without contradiction or unwarranted variance. Timeliness and Relevance: There must be a valid reason to collect the data to justify the effort required, which also means it has to be collected at the right moment in time.
Data collected too soon or too late could misrepresent a situation and drive inaccurate decisions. Completeness and Comprehensiveness: Incomplete data is as dangerous as inaccurate data. Gaps in data collection lead to a partial view of the overall picture to be displayed. Without a complete picture of how operations are running, uninformed actions will occur. Availability and Accessibility: This characteristic can be tricky at times due to legal and regulatory constraints.
Regardless of the challenge, though, individuals need the right level of access to the data in order to perform their jobs. This presumes that the data exists and is available for access to be granted. Granularity and Uniqueness: The level of detail at which data is collected is important, because confusion and inaccurate decisions can otherwise occur. To determine whether data is accurate or not, ask yourself if the information reflects a real-world situation.
Accuracy is a crucial data quality characteristic because inaccurate information can cause significant problems with severe consequences. Why does completeness matter as a data quality characteristic? If information is incomplete, it might be unusable. Reliability is a vital data quality characteristic. You could make a mistake that could cost your firm money and reputational damage. See what data quality assessment looks like in practice. Review four key metrics organizations can use to measure data quality.
Why does relevance matter as a data quality characteristic? Timeliness, as the name implies, refers to how up to date information is. In turn, that costs organizations time, money, and reputational damage. Precisely provides data quality solutions to improve the accuracy, completeness, reliability, relevance, and timeliness of your data. When you have high-quality data, you can more accurately determine who your target audience should be. You can do so by collecting data about your current audience and then finding potential new customers with similar attributes.
You can use this knowledge to more accurately target advertising campaigns and develop products or content that appeal to the right people. In addition to improving targeting, data quality can also help to improve your content and marketing campaigns themselves.
The more you know about your audience, the more reliably you can create content or ads that appeal to them. If you discover that golf is one of your most popular categories, you can direct your content team to create more golf-related articles and videos. If you find that golf is especially popular among visitors to your site who are men between the ages of 45 and 64, you can show golf content to users in this age range when they visit your site.
A similar technique can be applied to content used as part of a marketing campaign. High-quality data can also help you improve your relationships with customers , which is crucial for success in any industry. Gathering data about your customers helps you get to know them better.
This can help you build strong relationships with them. Proper data management also helps prevent you from delivering duplicate content to customers, which can become annoying to your audience and damage your reputation. High-quality data is also much easier to use than poor-quality data. If your information is not complete or consistent, you have to spend significant amounts of time fixing that data to make it useable.
This takes time away from other activities and means it takes longer for you to implement the insights your data uncovered. If you have better quality data than your competitors or use your data more effectively than they do, you gain a competitive advantage.
Better data quality means that you can discover opportunities before your competitors do. A lack of good data means missed opportunities and falling behind the competition. Ultimately, high-quality data can lead to increased profitability. It can help you to craft more effective marketing campaigns and increase sales numbers. It also decreases ad waste, making your marketing campaigns more cost-effective. Having this information enables you to focus more of your time and resources on these kinds of content.
Collecting high-quality data can be challenging. Problems with data quality may occur when a company is attempting to integrate data systems across different departments or applications, implementing new software or manually entering data. They may also occur because a company does not have the proper tools or processes in place. There are things that companies can do, however, to help improve data quality. Taking the following steps can help ensure the collection of quality data.
Having data management technologies in place can make ensuring data quality much more efficient and straightforward. The Lotame data solutions platform , for example, can pull all of your data into one place, making it easier to organize it and ensure quality. This allows you to integrate your data so that you can get a full picture of your audience, more quickly analyze your data and use it more effectively.
When it comes to data you collect directly from your customers, ensuring data quality is, in some ways, relatively straightforward. Because you gather it yourself and it comes right from the source, you can be reasonably confident in its accuracy. You just need to make sure you have a collection plan in place that ensures validity, consistency, relevancy and the other aspects of data quality.
This data that you collect directly from your customers is known as first-party data. There are also two other types of data — second-party data and third-party data.
You purchase it directly from the organization that collects it. The kind of data is usually considered high-quality. Second-party data is useful for increasing the size of your audience and expanding into new markets. Third-party data is data that you purchase from sources that are not its original collectors.
You typically buy it from aggregators who collect large amounts of data from various sources into one data set.
You can find this data for sale on exchanges such as the Lotame Data Exchange. One of the main advantages of third-party data is its scale. Because of its volume, you can uncover more valuable trends and insights. There are some ways to ensure quality, however. Choosing a provider with a good reputation goes a long way. Also, find out everything you can about the source of the data before you purchase it. See if you can determine whether it was modeled or validated.
Purchasing third-party data through Lotame can help ensure that your data is accurate. We offer Precision Audiences , which are data segments that exceed industry benchmarks for accuracy and precision. This means you can be sure they are accurate, precise and will help you to reach your intended audiences.
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