While coming to the decision to develop Sutro in-house, we discovered the inherent limitations with 3rd party ATS systems. They’re limited in the data they collect, they remain heavily focused on the resume, they do not effectively aggregate internal and external data, and most importantly, they don’t do anything other than act as a repository of the data for use exclusively by the recruiter.
Sutro puts us on the path to address all of these limitations. We are already using it to aggregate our internally gathered as well as web-based and paid databases for companies and candidates. We are using algorithms to provide insights from that data aggregation, and creating a level of transparency that simply was not available with a 3rd party ATS. All of this allows us to work towards the best possible matches between clients and candidates.
To keep our internal data robust, Sutro captures every single activity and interaction as well as individual user data for all the companies and candidates we interact with. We spend significant time and effort working on how we categorize and classify our internally collected data to ensure it is appropriately factored into our scoring algorithms. And we’re constantly working on making this data as accessible to recruiters as possible to allow them to make the most relevant recommendations possible to our customers.
Like many others, we also collect data from external sources, one of the most well-known being LinkedIn. LinkedIn is a great networking tool, but in terms of recruiting, it is primarily a resume surrogate. There is no doubt that it is becoming the definitive source of employment history, essentially replacing the resume. However, when it comes to employment matching, LinkedIn doesn’t really know that much about anyone, whether it’s the company or the people. It provides a way to make introductions, but it doesn’t have the ability to assess or match people with jobs in a meaningful way. It merely provides the raw data, which requires further analysis to be valuable.
At Rivi, we’re not just using LinkedIn–it’s just one spoke in the wheel to be truly intelligent in search and matching. Everyone has a presence on the web, so we’ve created efficient ways to collect data from both free and paid sources. This is important because each dataset provides different information. For instance, one source may provide data on skillsets or attributes of a candidate, while a company research resource such as CB Insights offers a way for us to assess and make judgments on companies. This is all then meshed with our proprietary data to provide our recruiters with the insights to make great matches.
This aggregation and blending of data from internal input and external sources is an essential factor for us to reach our goal of providing a true end-to-end experience.