One of the hottest topics in the world of business these days is data. Many business owners are delighted with the insight and value that their business data can give them. In fact, data is seen as one of the most valuable resources.
However, this does come with a caveat. Data is only a valuable resource if it is high-quality. Low-quality data is at best inconsequential, and at worst can drive businesses to make costly mistakes. The Data Services Insight report from Royal Mail, for example, concludes that bad customer data can cost organisations around 6% of their annual revenue.
These bad data costs tend to comprise the time that employees need to spend correcting bad data, and the impact on customer service.
So, as you can see, improving the quality of your data is vital for businesses. Let’s take a closer look at data quality then and why it is so important.
What is Data Quality?
Data quality is a measure of the condition of data based on factors such as accuracy, completeness, consistency, relevancy, timeliness and validity:
- Accuracy – this refers to how well the data represents the real-world conditions it aims to describe. Inaccurate data will cause businesses problems, as it will lead them to make decisions that might not have the effects they desire.
- Completeness – all the data that is supposed to be collected, needs to be collected, otherwise there will be gaps. If data is incomplete, then businesses may have trouble gathering accurate insights from it.
- Consistency – there should be no difference between multiple versions of a single data item across multiple databases or data sets, otherwise different departments may operate under different assumptions of what is true.
- Relevancy – the data your business collects needs to be useful for the campaigns and initiatives you plan to use it for. If it is not relevant to your business, then it is not useful.
- Timeliness – data should be recorded as soon after the real-world event occurs as possible, as it becomes less accurate and less useful the longer time goes on.
- Validity – this factor refers to the way the data is collected rather than the data itself, as if data is not of the correct type, or doesn’t fall into the right range, then you may have issues with organising and analysing it.
Why is Data Quality important?
More and more businesses these days are relying on data to help them make more informed decisions about finance, marketing, product development and so on, in order to keep up with their competition.
For this data to be beneficial to a company, it needs to be of high quality. The better the quality of the data you collect, the more you can get out of it. If business decisions are based on bad data, then you are likely to fail.
New technologies, such as artificial intelligence (AI) and automation, are also increasing the importance of data and its quality, as they rely heavily on large volumes of accurate data. The more high-quality data a machine learning algorithm has, the faster it can produce results, and the better these results will be.
It is no wonder that data is becoming an increasingly integral part of many business operations, with successful companies integrating it into everything that they do. However, this increased integration of data means that the quality is vital to the success of many aspects of a business – from content creation, and marketing, to sales.
Data quality is also a critical aspect of compliance-related issues. As regulations surrounding data (such as GDPR) continue to develop, it is becoming more and more important for companies to manage their data properly. It is much harder to be able to demonstrate compliance if your data is disorganised and poorly maintained.
What are the benefits of good Data Quality?
As we have seen, good data quality is crucial for businesses to not only take advantage of opportunities but also keep up with (or even overtake) their competition.
High-quality data can also provide the following benefits for businesses:
- Better audience targeting – allowing you to develop content or products that appeal to the right people.
- A competitive advantage – being able to discover potential opportunities before your competitors and therefore beating them to sales.
- Easier implementation of data – due to not having to spend time fixing poor-quality data to make it usable, and also ensuring all of your departments are working on the same data.
- Improved relationship with customers – you will get to know them better and can build a strong, trusting relationship with them.
- Increased profitability – as high-quality data helps you decrease waste and increase sales.
- More informed decision-making – giving you more confidence in your decisions, thanks to a decrease in risk.
- More effective marketing campaigns – as you will know more about your audience and can create adverts and content that appeals to them.
The technologies your business uses, and the team you have in place to provide data to you, play a central role in your data quality strategy. Working with Agile Recruit to strengthen your data quality team can make the task of ensuring data quality much easier.
Want to learn more about how Agile Recruit can help you ensure data quality and make the most of your data? Contact us today!