Why is Data Quality Important?
Bad data is costly for organisations. Inaccurate information, outdated contacts, and incomplete and duplicate records result in everything from undeliverable shipments to returned mail and lost customers. Data quality creates a foundation for businesses to work more effectively with their customers. By addressing data quality issues, businesses can leverage their data for analysis and better decision-making to improve the efficiency of their operations. In the long run, it is extremely costly for businesses not to have a solid, comprehensive data quality approach or solution in place that will guarantee their customer contact information is valid. Solutions that correct and verify data at the point of entry go a step further by making sure bad data never gets into an organisation’s systems in the first place - preventing data quality issues down the road.
What Steps Can Be Taken to Improve Data Quality?
The CRM database is an essential and valuable asset for many companies. But if the data isn’t accurate, it’s useless. Companies need to acknowledge that a customer database goes stale at a rate of 2% each month. That’s over 20% in a year.
The following four steps can be taken to improve data quality and consistency to achieve clean data:
- Check data before it enters your system - Verifying contact data at the point of entry will ensure data is clean, verified, and accurate. This includes making sure an address exists and is formatted to the right country format, and if an email address or phone number is real for correspondence.
- Fill in the gaps and enrich - Data enrichment & append adds missing contact information for more complete customer records. You can also enrich your data by adding demographic or firmographic insights so you can do more with it, like segmenting and gaining extra insight.
- Remove duplicate records - As businesses open up more touch points for customers, data may come into their systems from multiple channels. Overtime, duplicate data from the same individuals may start to build up, so it's important to identify, match and merge multiple or duplicate records into one “golden record” for each customer.
- Keep your records current - By batch cleansing your data from time to time or having a data cleansing routine, you will keep your database fresh, your customer records up to date and your data GDPR compliant.
Learn more about data cleansing in our What is Data Cleansing article
Taking into consideration the 1-10-100 rule, it costs £1 to verify data at the point of entry, £10 for your data to be cleansed intermittently to correct up, update and deduplicate records in batch and £100+ if nothing is done, reflecting the cost of wasted materials, resources, poor reputation, and lost customers.
Taking the steps to improve customer data leads to better response rates, greater customer satisfaction and, ultimately, better ROI.
We have included our data quality explainer video below to give you more insight into the data quality lifecycle and how you can implement a solid data quality strategy.
Why Melissa?
Melissa has been helping businesses improve their data quality for over 35 years with smart solutions that correct, verify, update, and enrich customer data. Our full spectrum of data quality solutions gives businesses the tools they need to maintain clean, current, and consistent data for more efficient operations and improved marketing and sales efforts. Melissa’s Data Quality solutions instantly verify contact data at the point of entry for over 240 countries and territories, with flexible tools available as on-premises APIs or Web services to meet your specific needs.