recently announced the launch of a new service called The Predictive Cloud that provides API access to its powerful predictive engine. InsideSales made its name, not surprisingly, by adding predictive capabilities to sales prospects. By aggregating very granular prospect data from its customer base (over 100 billion sales interactions), InsideSales can not only predict who is a top sales prospect, but even what day of the week is best to make contact. The Predictive Cloud throws this impressive analytical and predictive capability open to anyone who wants to use it, even if they want to use it to predict their own top prospects, though what really excites InsideSales is the belief that other companies with lots of data will find non-sales applications for its predictive engine, such as logistics, marketing and even human resources.

While The Predictive Cloud has obvious applicability to many commercial data products, it’s representative of an important trend: the ability for data providers to tap into cloud-based plug-and-play datasets and analytical tools to enhance their own products. It’s a hugely positive development for data companies, because these new tools and datasets allow us to access Big Data in a useful and powerful way without having to become Big Data experts. Similarly, we can now start to tap into analytical toolkits without the expense and complexity or having to build them … or even run them.

I’ve been saying it for several years now: Big Data doesn’t imperil commercial data producers, most of which produce what can be called “Little Data.” Indeed, Big Data can be used to enhance Little Data and make it deeper and more powerful. The analytic tools and capabilities that have come out of the rush to Big Data can also now be profitably employed by Little Data producers as well.

There’s a lot going on out there, but everything I see still tells me that those who control a valuable dataset are still in the driver’s seat, especially if they take full advantage of these plug-and-play opportunities to make their datasets smarter, deeper and ever more useful to their customers.