Businesses – big and small – contain a range of data from all over the world, and with globalisation being as prominent as it is, you would be hard pushed to find a retail database that contained address data from just one country. This, of course, is a great thing for retailers as it means their company is recognised internationally. However, it can also lead to issues relating to address formatting – after all, there are over 120 address formats around the world, as well as a wide variety of character sets and languages.
But, what exactly is data quality? Well, put simply, it refers to a dataset’s ability to serve a specific purpose in a particular context. For example, if you have a database full of valid and accurate customer addresses, you have good data quality that means you can communicate effectively with these customers. Conversely, poor data quality is where your data is inaccurate and means that you have issues delivering packages and communications to your customers. This can be a consequence of errors being entered during the address entry part of the checkout and can be extremely costly for retailers in many ways.
According to our research Fixing Failed Deliveries: Improving Data Quality in Retail, failed deliveries cost British retailers an average of £183,132 per year – costly business that could easily be avoided.
In order to overcome data quality issues, there are several steps you can take. We recommend following these seven to get you started.
Show cultural sensitivity
When it comes to international data, it is essential to ensure you understand the character sets and data norms of the countries you are gathering data from. Alter your data processes to fit cultural and linguistic norms – not the other way around. For example, the way that an address is written in the UK is different to the way an address is formatted in Italy. In the UK, the house number comes before the street name, but in Italy, it’s the other way around. It’s essential to understand this to avoid confusion, potential delivery issues and even offense.
Don’t cut corners
Though cutting corners and spending less money may be tempting, it will only lead to issues further down the line. Instead, be prepared to spend at the point of collection.
Make data collection simple
Improve accuracy as well as making data entry easier by ensuring your data collection system closely matches the different data formats as possible.
Implement type-ahead at the start
When collecting data, type-ahead technology is a huge help. Not only does it reduce customer frustration as well as the amount of work required of them, but it also greatly improves data quality.
Don’t compromise on data quality
Regular data audits should be a regular process. Identifying and resolving discrepancies at the source is necessary so your overall data quality is not negatively affected.
Don’t over-complicate it
As simple as it sounds, use the tools you have rather than causing yourself more work.
Common sense rules
Common sense is a tool that is often overlooked, but is the generally the most effective tool in the box when it comes to making efficient business decisions.
While managing international data can seem complicated, it is vital to understand the consequences that poor data can have on your business. This is true regardless of whether the data you are collecting is national or international, though global data can often be deemed more complex due to the different formats and cultural factors involved. Arming yourself with the right tools to deal with poor data quality is vital and can save your business a lot of money and poor business decisions.
Find out more about the implications of poor data quality in our report Fixing Failed Deliveries.