Last year, Gartner shared luxembourg phone number list
a startling statistic. “Every year, poor data quality costs organizations an average of $12.9 million.”
We can expect bad data’s impact to only worsen for companies. Poor data quality loses you access to opportunities, ruins strategic planning, and wastes your time and energy.
Business trends in the past decade focused heavily on having access to data. For the first time, businesses could have in-depth details found, logged, and analyzed about their prospects, customers, and own work processes. But once the data floodgates were opened, data and metrics were collected constantly without concern for accuracy and usefulness.
Let’s review how poor quality B2B data impacts your business, why bad data is so common, and what you can do about it.
How Does Poor Data Quality Cost You Millions Annually?
Poor data quality hurts telemarketing as a sales enablement tool
you in the big picture and while doing daily activities.
First, disorganized data culture stops everyone from being on the same page and causes mistakes.
Does your company have different definitions for a “prospect” vs. a “lead”? Does everyone know those differences or are some using those terms interchangeably? When a contact has a phone number listed in the database, is that number the company HQ phone line, their office desk number, or their cell phone number? If those three options are listed, are you sure everyone and every integrated app know and understand those differences?
Second, if your data is being mismanaged, you’re unable to make intelligent decisions.
Let’s say you have data saying your anti-competitor campaign from last year made the most revenue, so you decide to invest more this year, going after even more competitors. But, if customers weren’t accurately tagged with the outreach campaigns they experienced before buying, you could be investing heavily in marketing that won’t make an impact.
Why is Data Health Frequently Poor?
For data to be valuable, europe email
it needs to fulfill several objectives and be:
- Accurate
- Consistent
- Accessible
- Timely
- Complete
According to Talend’s 2022 State of Data survey, companies are struggling in all five separate areas. And, it only takes errors in one section to cause revenue loss.
Let’s consider timely data. In the above example of deciding to launch a competitor-focused campaign, maybe your company does track and collect all the necessary data to have made a better decision. But, the data isn’t put together and shared until someone has already been a customer for at least six months (maybe the data collection and evaluation is part of your customer success plan). All your decisions are now based on data at a minimum of six months old.
So, your data collection process could take too long, could introduce too many human errors, could be siloed and inaccessible.