Garbage in; garbage out.
In the tech industry, it’s more than an aphorism. It’s a Dantesque warning.
“All of the new services surrounding AI and Machine Learning require accurate data (to properly function),” she says. “Most companies don’t like to talk about dirty data because, until recently, there weren’t very many solutions that were efficient or scalable.”
InsideView’s data integrity functionality monitors CRMs’ databases in real-time, enriching leads with the most up-to-date data as they’re generated. Like a digital Roomba, InsideView’s software cleans a company’s CRM database autonomously.
“It’s a huge benefit to companies to have the peace of mind given by knowing that there’s a trusted application always running in the background to keep their data clean,” says Tucker.
Traditional enterprise data strategies simply aren’t scalable. The more data you throw at them, the more the original data collection, storage, and manipulations’ inconsistencies reveal themselves.
As we integrate AI further into our day-to-day workflows, the risk posed by those inconsistencies begins to stretch inter-departmentally. As reported by the Harvard Business Review, bad data’s ramifications result not only in increased costs, but in lost customers, bad decisions, or reputational damage to your company.
In short: Garbage in; garbage out.