Garbage in; garbage out.
In the tech industry, it’s more than an aphorism. It’s a Dantesque warning.
IBM estimates that dirty data costs the American economy around USD $3.1 trillion each year.
For Heidi Tucker, InsideView’s Vice-President of Global Alliances, that’s a problem that needs addressing.
“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 has traditionally been known as a trusted data source for B2B companies. Now, Tucker says that InsideView has expanded its offerings to include data integrity solutions.
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.
Data integrity, data quality and data security compliance are top-of-mind for most North American business owners. With an estimated 250,000,000 GB of new data created each day, automated data scrubbing is no longer a convenience.
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.