Welcome to a new series of blogs we’re calling ‘back to basics’. I’ll be aiming to unpick the nitty gritty of data quality and, in simple terms, explain why it’s so critical to organisations, both large and small if they want to stay ahead of the curve.
That’s not to say that data quality as a practice is ‘basic’. Far from it. It’s one of the most business-critical activities that organisations can do if they want to ensure the accuracy of one of their most precious business assets. We know that 83% of organisations see data as an integral part of forming a business strategy, yet we also know that they believe an average of 30% of their data is inaccurate. So why is that? It’s clear that the digital and regulatory landscape is becoming increasingly complex; more data, more channels, more regulation. And all of this puts pressure on the processes that organisations have in place to maintain the integrity of their data.
Our latest global data management research shows this trend very clearly and the downstream effect it’s having on levels of data quality in organisations. Having worked for both Experian as a Data Strategy Manager, as well as at a large retail organisation, I’ve seen both sides of the coin and how bad data effects business. I’m therefore a huge advocate for making sure data quality is always first on the agenda, as the most important foundation for data management.
So, without further ado, let’s kick off with Part One.
Whatever your organisation does, you will be capturing data on individuals – whether they are customers, clients, citizens, patients, supporters, suppliers or staff.
This data powers your business. As a retailer, it’s critical for getting purchases to customers. As a charity, you’ll be using it to let donors know about the great work you do. As a hospital, you’ll be using it to ensure patients can make appointments. In fact, any organisation is going to be hard-pressed to do what they do without their data.
So in a world where our interactions with data are becoming ever more complex, the most basic details such as address, email and phone number should be totally accurate. Right?
Well surprisingly, this is still an issue. Organisations believe an average of 30% of their data is inaccurate, often because there aren’t the right checks in place when information is entered for the first time. No doubt we can all recall instances of websites that require you to enter your address manually or your email address twice to ‘validate’ it. Frustrating is just one of several ways I would describe it!
In fact, with all the effort from User Experience and Product Management professionals going into making retail checkouts, quotation forms and even your tax return as hassle free as possible, using manual, un-validated contact data entry fields doesn’t make sense.
With so many reasons to make data entry for your customers and colleagues as simple as possible - whether it be reducing duplication, meeting regulatory requirements or reducing checkout drop-offs – it’s worth all organisations looking again at how they capture data across all channels including websites, call centres and in stores. Let’s break it down into the different types of contact information you probably capture.
If you’re using manual address entry fields, think about the number of keystrokes required to enter an address. Consider the time this takes, the risk of manual error and the need (or not) for ‘required fields’. What could happen if the address is wrong? Is this easy on a mobile device?
My home address requires 40 keystrokes (without entering a county) and takes me around 10 seconds to enter (longer if I need to move around multiple fields or if I’m on a mobile device). Even though I’m quick on a keyboard, I will still make mistakes. If I have to say the address over the phone to someone else, the chances of human error are even higher – especially as I have letters in my postcode that can easily be misheard without using the phonetic alphabet.
Much of this can be solved using an address capture tool like our Global Intuitive API. For a user on your website, they can either enter their postcode and then select their address from a list, or they can start typing their address with our tool ‘searching ahead’ as you would see on a search engine. This latter feature is even more useful in your call centre, as it limits the risk of errors with misheard postcodes.
Email has become an increasingly critical channel for contacting individuals quickly. Service messages from local authorities to remind you to pay your council tax, to marketing messages from your favourite brands. These are all being delivered via email to cut costs, improve impact and reduce the time it takes to deliver information.
However, many forms still require emails to be entered twice – a time-consuming task with many more forms not validating that an email address exists, is deliverable or is not a ‘throwaway’ address that could, for example, be used to claim multiple special offers by the same individual.
Live email validation can prevent both the poor customer experience and the risks posed by bad quality data and 'throwaway' email addresses. These tools (such as our own Email Validate) ‘ping’ the email server to verify that the address exists and can receive email. It also checks the address against 'throwaway' domains to reduce the risks I mentioned above.
Mobile has become an increasingly important channel for service messages in the logistics and retail arenas. Other verticals are increasingly using it to confirm appointments, validate bank transfers and seek feedback on customer experiences.
As with address and email, capturing an accurate mobile number is easier said than done. Manual entry errors can make this critical service message channel useless.
The quality of this field of data may also be impacted by the purpose – if its intended use is for marketing via SMS then this may well influence the completion rate.
Using our mobile validation service, you can ‘live ping’ a mobile number to understand whether it is valid, able to receive calls/messages (i.e. is it live) and whether it is roaming.
This means that only useful data enters your data lake and by considering carefully how you intend to use this data element (along with the appropriate message to the consumer at the point of entry) you could boost the quality and value of the data you hold.
Simply put, if you capture poor quality data you will see poor quality results. Customer service, marketing and ultimately the bottom line will suffer.
Access to simple validation tools is an increasingly simple and cost-effective solution to poor data quality. They can be implemented quickly without significant technical overhead, and when combined with wider customer experience and data strategies, are truly transformational to organisations of all shapes and sizes.