Digital transformation has been talked about for many years, but the pandemic has accelerated the digital transformation journeys for many enterprises. Forced to adapt to changes in the business landscape and customer behavior, businesses have adopted more digital tools and technologies to drive innovation and increase resilience.
The digital race is on. To pull ahead of the pack, a company needs to know what to do with its data. Without a data-driven strategy, you’re bound to lose ground to competitors who apply their data to operational improvements, product development, go-to-market strategies, and the customer experience. It isn’t enough to collect, interpret, and act on the data. You have to do it fast.
Who are my top sellers? What kinds of deals have the highest close rate? How have our sales opportunities changed over time? As a sales leader, these are just some of the questions you ask yourself every day to keep your team on track. And the answers are in your data. Every form fill, cold call, and MQL is another data point you can use to assess the health of your sales organization.
Feature stores have arrived in 2021 as an essential piece of technology for operationalizing AI. Despite the enthusiasm for feature stores in high-tech companies, they are still absent from most legacy ML platforms and can be relatively unknown in many enterprise companies. We discussed how feature stores are critical to the data-first approach of next-gen ML platforms in our previous blog, but they are important enough to get their own treatment in a full article.
