Data is the foundation for every company. If decision-makers do not have timely, relevant and reliable information, they have no choice but to rely on their own intuition. Therefore, data quality is key. Analysts need the right data, collected in the right way and in the right form, in the right place, and at the right time. If some of these requirements are not met, it will be hardly difficult to run a business effectively.
But how to ensure that the data collection process is correct? How can we make sure we are collecting the correct data? What are the ways to determine the validity of the information obtained? Let’s answer all these questions further in this post.
Importance of Data Accuracy
Actively checking and maintaining data quality is a shared responsibility of all employees. Each member of the analytic value chain must monitor the quality of the data. Thus, it will be useful for each participant to understand this issue at a deeper level.
Data quality cannot be reduced to a single feature or requirement. This concept covers a number of aspects. Accordingly, they begin to highlight the levels of quality, at which some aspects turn out to be more important than others. The importance of these aspects depends on the context of the analysis to be performed on this data. So, here are the aspects that the quality of the data is determined by.
The analyst must have access to the data. This implies not only permission to obtain them but also the availability of appropriate tools to ensure that they can be used and analyzed. In this case, using data quality management solutions is key to success.
Data should reflect true values and the current situation. For example, an error in the date of birth, an outdated email address, or a mistake in a phone number of your customer can reduce the effectiveness of your email or marketing campaign. And this information cannot be called accurate or quality.
It should be possible to relate exactly one data to another. For example, a customer’s delivery must be associated with information about the order one has made, with the item or items from the order, with billing information and information about the delivery address, etc. This dataset provides a complete picture of a sales order. The relationship is provided by a set of identification codes or keys that link together information from different parts of the database.
Incomplete data can mean both the absence of some information (for example, the customer’s name is not indicated in the information about the client) and the complete absence of a unit of information (for example, as a result of an error when saving to the database, all information about the customer was lost).
The data must be consistent. For example, the address of a specific customer in one database must match the address of the same customer in another database. If there is a mistake, one of the sources should be considered the main one. Besides that, it is not recommended that you use the questionable data at all until the cause of the disagreement is eliminated.
Each field containing individual data has a specific, unambiguous meaning. Well-named fields, in conjunction with the database dictionary (more on this in a moment), help ensure data quality.
The data depends on the type of the analysis conducted. For example, a historical excursion into stock prices of the American Landowners Association may be interesting, but it has nothing to do with the analysis of futures.
The data must be both complete (that is, contain all the information that you expected to receive) and accurate (that is, reflect correct information).
There is always some time between the collection of data and its availability for use in analytical work. In practice, this means that analysts get the data just in time to complete the analysis on time. With such a delay, the data becomes practically useless (while maintaining the costs of storing and processing them), they can only be used for long-term strategic planning.
In today’s business, those who don’t collect data don’t achieve success. But just collecting information is not enough; data should be checked for quality and completeness. Collecting data is not an end in itself but only fuel for the analytical engine. And if you want this “car” to serve you faithfully for many years, use only proven data “gasoline.”