Ensuring the data your business collects is quality data is crucial, as it affects whether it can serve the purpose you want it to in a particular context, such as data analysis.
So, how do you go about determining data quality? Well, there are five key data-quality traits that you need to be aware of:
- Accuracy – is every detail of the information correct?
- Completeness – is the information comprehensive?
- Reliability – is the information contradictory to other trusted resources?
- Relevance – is this information important to your business?
- Timeliness – is this information up-to-date and could it be used for real-time reporting?
This trait of data quality ensures that every detail of the information is correct and that it reflects a real-world situation. For example, in the US, dates follow the MM/DD/YYYY format, but in the rest of the world, they follow the DD/MM/YYYY format. So 11/10/2009 could be the 10th of November or the 11th of October.
This is one of the most crucial data-quality traits, as basing your business strategy on inaccurate information can have severe consequences.
This trait is concerned with how comprehensive the data is and whether all of the information you need is available. For example, you need a customer’s name and email address, but when the data is imported into your business the associated email address is missed.
If information like this is incomplete, then it renders the rest of the associated data useless – especially if you are wanting to send an email newsletter to the customer. You need the email to ensure the newsletter gets to them – so without it, the data is incomplete.
When we talk about reliability in the context of data-quality traits, we are talking about the need for the data collected to not contradict another piece of data from a different system or source. For example, if one system has your customer’s name as Mrs B A Williams, and the other has it as Miss B A Jones, then the information is unreliable.
Data reliability is key to data quality. If pieces of information contradict each other, then the data cannot be trusted – and it could lead you to make mistakes that will cost your company reputational and financial damage.
Relevance comes into play when thinking about why you are collecting the data in the first place. Do you really need the information you are collecting, or are you collecting it just for the sake of it?
If you are gathering irrelevant information, then you are wasting your time as well as your money – and your analyses won’t be as valuable.
This trait refers to how up to date the information is. If it was gathered in the last hour, for example, then it will surely be timely data – unless new information has come in since then that renders this previous information useless.
Timeliness is considered an important data-quality characteristic because if data isn’t timely, it can lead to people making wrong decisions that could cost them time and money, as well as potentially damage their reputation.
As you can see, knowing the five key traits of data quality will ensure you get the most out of your data. If your data does not meet these criteria, it has no value.
If you want to learn about how Agile Recruit can help you shore up your data quality team, or are looking for your next data quality role, contact us today.