Poor quality data can seriously harm your business. It can lead to bad analysis and even worse, bad business decisions. These bad business decisions can then have adverse effects on how your business performs, often leading to financial losses. Here we will take a look at some causes, consequences and preventative measures that can be taken to stop the growth of poor data.
What Causes Poor Data Quality?
Reliance on multiple systems
Where companies require multiple inputs across systems and forms that aren’t integrated, it is likely that human error will occur. Re keying information is a long, arduous task that can easily result in multiple versions of the truth, especially when there is no form of data validation.
Mixed entry from different users
Breakdown in established procedures and ways in which data should be formatted and entered can affect data quality. For example, customer names should have a certain format (ABC Ltd or ABC Limited) and open text fields can be used and interpreted in different ways by different users.
Poor data migration and integration
Migrating data to a new system or consolidating systems via integration carries inherent risks to your data. Data values can be irregular, missing or misplaced, and even simple spreadsheets can cause inconsistency problems. If your data isn’t clean you’ll likely need rules implemented to change that.
Data is always changing, it isn’t static. Do you still use the same email address, or have the same job title you did maybe two years ago? You should look at keeping your data up to date, otherwise it’ll become outdated and you’ll be wasting time and resources trying to get in contact with it. Last year Hubspot found business databases naturally decay at 22.5% every year.
What are the consequences of poor data quality?
Loss in revenue
You may lose client and prospect interest and revenue if you don’t keep them in the know of your latest products and activity. If you have incorrect contact information in your systems then how can you contact them with marketing information to upsell your products? Gartner recently found that for most companies thousands, and for large corporates millions of pounds is lost each year down to lost productivity from poor quality data.
If you’re running data analysis or predictive analytics with incomplete and incorrect data you could not only be wasting your time, but also risk being led down the wrong path. With duplications and missing fields in your data you’ll have inefficient resource management, wasting resources trying to follow bad analysis as well as trying to fix your errors.
Damaged reputation and fines
If you contact the same customer multiple times unnecessarily, or are sending emails to a large number of dead addresses because of bad data management you will likely build a bad reputation both within the physical and digital world. Along with the implementation of GDPR in May 2018, if you don’t manage your marketing data accurately you could be in for a hefty fine from the ICO.
What can I do to prevent poor data quality?
Update or upgrade your software
Whether you’re using disparate systems or using excel spreadsheets, upgrading your internal software can be a great way to increase your data quality. A database, or having your systems integrated will make your process “proactive” instead of “reactive”, reducing inconsistencies not only within your current data set but eliminating them from future prospects.
Implement import rules
Utilise a pre check system for any manual data that is collected and train colleagues to ensure they’re consistent with it. Using a consistent format for names, job titles etc (Director of Marketing & Marketing Director are two different roles to a system!) is key to keeping your data clean and reducing duplications. Where possible, implement pre-populated online forms, whereby the data from these are guaranteed to delivered in the format you’re after.
Develop a data cleansing routine
With decaying data, it’s important to keep as much of your data as possible up to date. Whether it’s keeping in contact with your customer base regularly or using profile enrichment and audience insight software, developing a routine way to ensure your data is accurate is crucial to the effectiveness of your marketing campaigns.
Trusted, high-quality data is a core requirement for enabling digital business. This means unless CIOs, chief data officers and information leaders get this right by pragmatically improving their data quality, they will be unable to take full advantage of new big data opportunities such as data insights and marketing analytics.
Note: This post was originally posted on 9th August 2016; however it has since been updated to keep the data and accuracy of the content relevant.