Poor data quality is an issue that comes up daily in both our internal marketing team conversations and with the hundreds of demand marketing and marketing ops teams we closely work with.
We all know it’s a big challenge, yet many of us still struggle to fix it. Why? Because it’s a never-ending quest to get it right and maintain high levels of data accuracy and integrity.
The good news is there are automation solutions and very clear practices you can deploy to address dirty data challenges that negatively impact customer experience, undermine sales partners’ trust and hamstring your ability to deliver optimal results from your marketing programs.
This past week I reviewed a few key articles from data experts for a kick in the butt to get out in front of the dirty data issues. It’s a call to arms to proactively implement measures to prevent bad data where possible and manage the health of our prospect and customer data on an ongoing basis.
Here are three solid reads – from Forrester Research, Sirius Decisions, and our own Integrate Data Quality Index – that I hope will spark some ideas and provide inspiration to tackle the poor data quality issue in your organization.
Most of us have invested big dollars in and make daily use of the proverbial customer data “system of record” (or multiple systems :) ). Get data right in the system of record and you’re a hero. But the demands on data in the “Big Data” world are exposing the warts of bad quality.
This Forrester report, by analysts Michele Goetz and Nasry Angel, is a useful guide to eliminating data issues from your business to improve quality, relevancy and business results. The analysts of this report recommend addressing data quality governance as close to the point of customer interaction as possible so that the marketers and data end users will trust and use the data. This shift in thinking requires you take a service-oriented approach within your enterprise architecture.
Email validation is critical to assure a positive customer experience and maintain your reputation score. Sirius Decisions analyst Jay Famico outlines how to automate this important process for your nurturing and prospect/customer outreach programs. Specifically, he highlights ways to apply automated validation for known competitor domains, role-based email addresses, mailing lists, malformed email addresses, spam detection and blacklist services and wireless device domains (a very important step as your customers go mobile).
This proactive approach can significantly reduce the time and resources you put into these processes, so you can focus on the quality of the communications and targeting versus spending your time manually scrubbing out bad addresses.
Leveraging Integrate’s platform, and specifically its data governance software, our team analyzed more than 778,000 B2B tech industry leads and compiled the findings in Integrate’s first research report, titled “Integrate Indices: Data Quality – B2B Tech Industry.” The Index’s findings fully validate the need and call for better data and proactive, automated efforts. This apathy is literally costing you budget by paying for incomplete or inaccurate prospect data and leads.
The Index found, on average across enterprise and SMB tech industry businesses as well as the media companies that generate leads for them, 40% of leads generated were determined to be of poor quality. Specifically, 311K plus leads out of 778.5K contained an invalid email address, missing fields, duplicate data, incorrect formatting or invalid values. Today, this kind of data governance can be automated so you can stop spending time scrubbing lead files. Most importantly, it enables you to stretch lead gen and media budget much further.
This report covers both the WHY and HOW to prevent poor lead data quality. You can download the full report here.
If you have other resources, best practices or just plain inspiration your data team is using, please pass it on. It’s time we put a big dent in the dirty data problem plaguing our marketing and customer communications efforts.