When good data turns bad

When good data turns bad


Accurate customer data is essential for successful business operations. It also underpins centralisation and automation of customer communications processes.

Companies collect and process data all the time and care must be taken to assure high quality data management standards. Inaccurate, incomplete or wrong information can result in bad customer experiences and even lost sales. If prospect data isn’t of a high quality, companies can waste valuable time and money on marketing campaigns that have a limited chance of success. Despite this, many companies still manage data manually through a range of tools such as spreadsheets and standalone databases that cannot provide a single ‘one truth’ view of each customer or record.

Maintaining Manual Records is a Challenge

If data isn’t verified, it may be entered incorrectly to begin with. If there’s no data management strategy, records won’t be kept up-to-date and over time they will become inaccurate. Change happens all the time – customers upgrade to newer products, change premises, get new email addresses and end contracts. In this way, good data can turn bad and when that happens, the impact is felt on the bottom line. The cost to the US economy of poor data quality has been estimated at a staggering $3.1 trillion while in the UK it could be up to six per cent of companies’ annual revenues.

Hidden costs include staff time spent grappling with lists of data and ‘cleaning’ multiple databases. Upfront costs include wasted spend on ineffectively targeted marketing campaigns. According to Canada Post, a colossal 140 million pieces of mail a year don’t reach their recipients due to inaccurate addressing. That’s a lot of money wasted and opportunities missed.

3 Steps to Successful Data Management

Three ways that companies can help ensure high quality data supports business operations are:

  1. Verify new data – auto-check addresses and standardise data fields for more accurate records creation. A consistent format makes it simpler for automated processes to access and use the information
  2. Set a clear data policy and strategy – this isn’t just good business practice, it’s also important for compliance with data regulations. It should be clear who has responsibility for keeping data secure and up-to-date
  3. Centralise records – a single ‘one truth’ view of each customer can support richer interactions for sales, marketing and other departments. Centralised data also helps minimise inconsistencies and incomplete records and supports digital communications tools that automate the creation of marketing materials across print and electronic channels.