LinkedIn steps into business intelligence with the launch of Talent Insights [TechCrunch]

LinkedIn may be best known as a place where people and organizations keep public pages of their professional profiles, using that as a starting point for networking, recruitment and more — a service that today that has racked up more than 575 million users, 20 million companies and 15 million active job listings. But now under the ownership of Microsoft, the company has increasingly started to building a number of other services, and today comes the latest of these, the launch of a new feature called Talent Insights.

Talent Insights is significant in part because it is LinkedIn’s first foray into business intelligence, that branch of enterprise analytics aimed at helping execs and other corporate end users make more informed business decisions.

Talent Insights is also notable because it’s part of a trend, where LinkedIn has been launching a number of other services that take it beyond being a straight social network, and more of an IT productivity tool. They have included a way for users to look at and plan commutes to potential jobs (or other businesses); several integrations with Microsoft software including resume building in Word and Outlook integrations; and adding in more CRM tools to its Sales Navigator product.

Interestingly, it has been nearly a year between LinkedIn first announcing Talent Insights and actually launching it today. The company says part of the reason for the gap is because it has been tinkering with it to get the product right: it’s been testing it with a number of customers — there are now 100 using Talent Insights — with employees in departments like human resources, recruitment and marketing using it.

The product that’s launching today is largely similar to what the company previewed a year ago: there are two parts to it, one focused on people at a company, called “Talent Pool,” and another focused on data about a company, “Company Report.”

The first will let businesses run searches across the LinkedIn database to discover talent with characteristics similar to those what a business might already be hiring, and figure out where they are at the moment (in terms of location and company affiliation), and where they are moving, what skills they might have in common, and how to better spot those who might be on the way up based on all of this.

The second set of data tools provides a similar analytics profile but about your organisation and those that you would like to compare against it in areas like relative education levels and schools of the respective workforces; which skills employees have or don’t have; and so on.

Dan Francis, a senior product manager running Talent Insights, said in an interview that for now the majority of the data that’s being used to power Talent Insights is primarily coming from LinkedIn itself, although there are other data sources also added into it, such as material from the Bureau of Labor Statistics. (And indeed, even some of LinkedIn’s other data troves, for example in its recruitment listings, or even in its news/content play, the material that populates both comes from third parties.)

He also added that letting companies feed in their own data to use that in number crunching — either for their own reports or those of other companies — “is on our roadmap,” an indication that LinkedIn sees some mileage in this product.

Adding in more data sources could also help the company appear more impartial and accurate: although LinkedIn is huge and the biggest repository of information of its kind when it comes to professional profiles, it’s not always accurate and in some cases can be completely out of date or intentionally misleading. (Related: LinkedIn has yet to launch any “verified”-style profiles for people, such as you get on Facebook or Twitter, to prove they are who they say they are and that they work where they claim to work.)

“We’re pretty transparent about this,” said Francis. “We don’t position this as a product as comprehensive, but as a representative sample. Ensuring data quality is good is something that we are careful about. We know sometimes data is not perfect. In some cases it is directional.”