Maintaining high quality, accurate data is a challenge that requires continuous attention. Like fruit in a bowl, data will quickly go bad if it is neglected and not refreshed.
Think about how often names, addresses, phone numbers, job titles and businesses change or become obsolete. If you’re planning to send a marketing communication or create personalised content for your customers, relying on outdated information is a sure-fire way to see your campaigns and creatives misfire – if they even hit the mark at all.
Still, while data becoming obsolete is a significant problem, there is something (or rather someone) who should take far more blame for poor data: humans.
These are the people doing the equivalent of putting half-eaten bananas, too many pineapples and even cucumbers into your fruit bowl.
Employees and customers are bigger culprits for bad data than conversion and migration projects, automated batch processing, database consolidations and natural data degradation. An average from multiple surveys attribute 59% of all data quality issues to human error.
It may not please us to think that we’re the weakest link but the point of data entry is often where it all goes wrong, and can do so in several ways, such as:
Spelling mistakes and typos
These errors are easily and innocently made. This could be a cashier or a telesales person making a mistake the spelling of your name, address or contact details. It could be a customer hastily tapping in their details when making a purchase. You need only read the name on the side of a Starbucks coffee to see how often mistakes can occur.
Incorrectly input information
Even if your proofreading skills are up to scratch, people are still susceptible to ticking the wrong box. Or, putting the right data in the wrong box, leaving fields empty or half completed. The bigger and more complicated an online form is, the greater the likelihood that some crucial piece of data can be incorrect.
Creating multiple records for one customer is a huge problem. Sure, duplicates can be created when consolidating disparate data sources but it can also stem from customers and employees inadvertently creating exact replicas within a system. For example, customers forgetting they already have an account or multiple users within a company creating duplicate records with different variations of a name (e.g. ‘Elizabeth’, ‘Liz’, ‘Lizzie’ and ‘Beth’).
Do you enter dates DD/MM/YY or MM/DD/YY? Feb or February? High Street or High St.? These are just some examples of inconsistencies that can contribute to duplicates and make records difficult to merge and analyse.
What can businesses do to improve data quality?
If you are on a mission to improve your data, then you’re going to need a data management strategy. Data cleansing and data enhancement should of course form a part of this strategy, but you should pay particular attention to mitigating some of the problems that can arise through data entry human error:
Keep online forms simple
Whether for the benefit of customers or staff, the more user-friendly a form is, the better. This means clearly labelled fields, a well-organised structure and removing the need to enter the same information multiple times into the same form.
Drop down menus and check boxes are not infallible. But if you require information to conform to a set format, these are more likely to capture accurate data than free entry fields. Alternatively, data entry input masks or validation rules mean users must enter data (like phone numbers and DOB) in a specific format before the details are accepted.
It’s possible that staff are unaware of the importance of creating highly accurate records. They may not see the harm in creating a duplicate record if they cannot find the original straight away. It could be that multiple departments are creating records but there is a lack of communication between them. Educating employees about the value of data quality can help improve standards and reduce the number of issues.
Establish naming conventions
Another tip that requires self-regulation from staff is to establish naming conventions for fields to ensure consistency. For example, the use of abbreviations in locations and company names. Establishing such rules won’t eliminate mistakes but can encourage good practice.
Understanding who is creating records and at what point to establish a workflow, encouraging more diligent data collecting habits, assessing at your technology for ease of use and minimising errors are relatively easy ways to implement a routine to improve data quality and will form part of an effective data hygiene strategy.
Blue Sheep Data Quality Audits
Thousands of marketers use the Blue Sheep Data Quality Audit to assess and improve the health of their marketing data.
This free service can be used as a one-time assessment of your marketing data quality, or as the first step on a partnership with Blue Sheep towards an insights driven marketing team.