In this video, I’ll break down the differences between data and insights. Data points are static pieces of information that represent a past state of a business. They are rarely refreshed and are highly commoditized, meaning there are hundreds of vendors all vying to give you the best data available.
Insights are derived from massive amounts of data by applying machine learning. Data sets are ingested by a machine to find patterns that suggest potential outcomes. For example, if a company has recently taken a round of funding, has been hiring sales and marketing professionals and has been adding customers at a rapid clip, they are most likely in growth mode.
Sure, a single person could come to the same conclusion, but are limited by the number of companies they can process. A machine can extract insights from a massive dataset and push those insights directly into a CRM at scale. These insights can then be used by sales and marketing professionals to achieve far more personalization and relevancy in their messaging.
“Hey everybody, Matt Amundson here with another Modern Data Tip.
So today we’re gonna talk about the difference between data and insights.
Data only gives you half the picture of an account. Sure, it tells you the number of employees, the revenue, or the industry that it’s in.
But with data science and machine learning to extract insights, now you can have an understanding of maybe that account looks like two or three of your current customers, maybe that account has a high propensity to grow. Or if you’re an insurance business, if that account’s a high- or low-risk business. So with these types of insights, you become a much more effective sales or marketing professional.”
Related: Learn how EverString approaches account matching in our Enterprise Guide to the B2B Data Revolution.