Bad data leads to bad decisions and it cannot be overstated the problems it can cause. Not just with losses of time and money, but the impact it can have on critical business decisions. Poor quality data prevents accurate customer profiling, hinders lead generation, damages brand reputation and could even lead to violations of data protection laws. According to Ovum research, it could be costing businesses at least 30% of revenues.
While there is no magic bullet for removing bad data, beginning the process of data cleansing and better data management will help ensure that your customer database is up to date, accurate and packed with valuable information.
What is bad data?
Asides from those intentionally falsifying their details to avoid receiving marketing material, creating bad data is easily done – particularly if you have millions of records in your database, collected from numerous channels.
Bad data tends to fall under at least one of the following categories:
Irrelevant – Just because organisations can collect vast volumes of data, it doesn’t necessarily mean that all of it is useful to your business. Not only is keeping hold of irrelevant data costly to store and process, it could also increase your risk of non-compliance. A report in 2015 titled Databerg revealed that mid-sized UK companies are spending around £435,000 per year on redundant, obsolete or trivial data.
Duplicated – Also adding to the storage burden, duplicate data is a main cause for inaccuracy and keeps many marketers awake at night. This is often created when data is kept in multiple repositories, but duplicates can be generated whenever data is purchased or gathered. Most often, duplicate data is created as a result of human error. Whether volunteered by a customer or input at another point of data entry, variations in the way that names, addresses and other contact details are recorded can lead to the inadvertent creation of duplicate records. Several case studies put the duplication rate at up to 30% for untouched records.
Decayed – Your databases require constant upkeep. In fact, Hubspot calculates that marketing databases degrade by about 22.5% every year. At Blue Sheep we have observed a decay rate of nearly 40% over 12 months. This is natural – people die, change their name, address and jobs details – and this highlights a need for regular data management.
What is involved in the data cleansing process?
In general, the data cleansing process encompasses several stages that monitor and purify data:
Analysis – this stage identifies the types of errors and inconsistencies that are to be removed in a detailed report. Most often, this will include screenings related mortality or bankruptcy, user suppressions, residency verifications and salacious word screening.
De-duplication – This compression technique is very difficult to get right and moves data towards what every business is looking for: one record per customer. Creating a consolidated record may require using existing records from disparate systems, then matching and merging them. At the merging stage, the best-fit records (more recently accessed records, fields with more complete data or external reference numbers) can help create a higher quality record.
Standardisation – This step helps to establish a process of standardisation during data entry, creating a consistent format to help identify and prevent duplicates. Common standardisations include standardising dates, removing punctuation and widening acronyms.
Enhancement – although not a requirement to cleansing, data enhancement can be very useful to add extra information to your records once they have been cleaned. For example, data can be appended with demographic characteristics, behavioural data, financial characteristics or property characteristics to enrich existing customer records and help marketers derive valuable insight.
Many organisations spend a great deal of time and money setting up a system to collect data but fail to invest the same amount of effort into the maintenance of their customer records. For one reason or another it is often the task that marketers put to the bottom of the list. Why? Because it’s difficult, time-consuming and fraught with error.
Despite the tendency to put data cleansing projects to one side, getting it right is potentially the most effective way of improving campaign results and the ROI of future campaigns.
More trustworthy data improves customer insight, ensures your marketing and business decisions are made on good facts and improves the lead time of campaigns by reducing the data preparation phase.
It may be an ongoing process, but the expense of preserving quality data is a drop in the ocean compared to the improvements it can make to revenue.
How clean is your data? Book a free Data Quality Audit to find out today or fill out the form below to get in touch: