Put another way: garbage in, garbage out
As information environments become more complex and businesses accelerate their digital efforts in a post-COVID world, data quality is more important than ever.
Because poor data quality has very real costs. According to Gartner, poor data quality costs companies an average of $15 million per year. When organizations make decisions based on poor data quality (or lack of data), they pay more to fix the mistake.
One business area where this becomes particularly evident is in the relationship between marketing and sales.
I was once part of an organization where marketing and sales were constantly at odds. Sales blamed marketing for not providing better leads, and marketing blamed sales for not closing prospects.
There were probably multiple factors at play here, but one question that clearly emerges is the question of data quality. Sales felt like their leads were based on outdated information, and it was unclear how they were qualified or connected to the company. Marketing was spending so much time managing and formatting data, analytics and data quality wasn’t always at the forefront.
On both sides of the equation it was evident that poor data quality was leading to poor leads.
Put another way, we were seeing first-hand that garbage in = garbage out.
Another part of the challenge with lead generation for marketing and sales teams—particularly in the digital era—is that there is so much information available and so much that you can do with this data. Data preparation (and ensuring data quality) is time consuming by itself, let alone processing and connecting analytics to business value.
In fact, Gartner’s 2020 Marketing Data and Analytics Survey notes that the three most-cited obstacles to marketing analytics teams’ success are:
- manual data preparation,
- connecting analytics to business value,
- and connecting analysis to insight.
How the right tools, practices, and team can be a game-changer
So how do we address the challenge of poor data quality?
For SMBs, it can feel overwhelming. What’s the right data to support your desired business outcomes? Where can you find this data? And what data can you trust?
Data quality is a challenge for any organization. But with the right tools, practices, and teams in place, it can be a game-changer in lead generation.
Consider these examples of how trusted data and models can support business outcomes.
- By identifying potential market opportunities so that you can target new customers based on your unique sweet spot. Example: you want a detailed picture of the supply chain for wind turbines in Canada to gauge how your company could position itself. A solution like our Diligence platform can provide that data and insight.
- By connecting prospects to chambers of commerce so that you can get better ROI on your membership expenditure. Example: our partnership with the Momentum Chamberizer.
- By uncovering who global clients partner with, so that you can identify how they’re a right fit for your channel technology. Example: our IDC Channel partnership.
Many of our solutions are tailored to support the unique business outcomes of SMBs. Explore some of our other solutions and see more possibilities.
Whether you’re currently invested in fine-tuning data quality in your organization, or you’re looking to explore data possibilities for new insights – investing in the right tools, practices, and teams can make all the difference.
Better data (and knowing how to leverage it) ultimately means better leads.
This post was written by Malisa Kurtz.