Informatica, the company for which I work, deals in big data challenges every day. It’s what we do — help customers turn their data into actionable business insights. When I took the helm as VP of global talent acquisition I was surprised to learn that the data within the talent acquisition function was not up to the standards Informatica lives by. Clearly, talent acquisition was not seeing the huge competitive advantage that data could bring — at least not the way sales, marketing, and research were viewing it. And that, to me, seemed like a major problem, but also a terrific opportunity!
This is the story of how Informatica Talent Acquisition became data-centric and used that centricity to our advantage to fix the problem.
Go to the Source
No matter how big or small your company, the data related to talent comes from varied and diverse roles within the talent acquisition function. The role may be named Researcher, Sourcer, Talent Lead Generator, or even Recruiter. Putting the name aside, the data comes from the first person to connect with a potential candidate. Usually that person, or in Informatica’s case, that team, is the one who finds the data and captures it. Because talent acquisition in the past was largely about making a single hire, our data was captured haphazardly and stored with …. let’s say, less than best practices. In addition, we didn’t know big data was about to hit us square in the face with more social data points than yesteryear’s Talent Sourcer could believe. I went to our sourcing team as well as our research department to begin assessing how we were acquiring, storing, and streamlining our data.
Data is at the heart of so many recruiting conversations today. But it’s not just about the data, it’s the access to the right data at the right time by the right person, which is paramount to making good business or hiring decisions. This led me to Dave Mendoza, a talent acquisition strategy consultant, who had developed a process called “talent mapping” which we applied to help us identity, retrieve, and categorize our talent data. From that point he was able to create our Talent Knowledge Library. This library allows us to store, access, and finally develop a talent data methodology aptly named, Future-casting. This methodology defines a process wherein Informatica can use its talent acquisition data for competitive intelligence, workforce planning, and candidate nurturing.
The most valuable part of our transformation process was the implementation of our Talent Knowledge Library. The weakest point with this new solution was not the capturing or categorizing of our data; it was that we had no central repository that would allow for unstructured data to be housed, amended, and retrieved by multiple talent sourcers. To solve this issue we implemented a candidate relationship management application — Avature. This tool allowed us to build a talent library — a single source repository of our global talent pools, which could then be accessed by all the roles within the talent acquisition organization. Having a centralized database has improved our hiring efficiencies such as decreasing the time- and cost-to-fill requisitions.
Because Informatica is a global company, it doesn’t make sense for us to house all of our data in a proprietary system. While the new social sourcing platforms are fast and powerful, the data doesn’t belong to the company once entered. That didn’t work for us, especially given we had teams all over the world working with different tools. With a practical approach to data capture and retrieval, we now have a central databank of very specific competitive intelligence that has the ability to withstand time because the tool can capture social and mobile data and thus is built for future-proofing. Because the data is ours, we retain our competitive advantage, even during talent acquisition transition periods.
One truth became very clear as we took on this data-centric approach to talent acquisition: if you don’t set standards for processes and protocols around your data, you may as well use a bucket, as no repository will be of much use without accurate and useable data that can be accessed consistently by everyone. Being able to search the data according to company-wide standards was both obvious and mind-blowing. These four standards are what we put into place when creating our talent library: 1) Data must be usable and searchable; 2) Extraction and leverage of data must be easy; 3) Data can be migrated from multiple lead generation platforms; 4) Data can be categorized, tagged, and mapped to talent for ease of segmentation.
In today’s globalized world, people frequently change their physical address, their employer, and their email addresses, but they rarely change their Twitter handle or Facebook name. This is why “people data” quickly turns outdated and social data is the new commodity within the enterprise. People who use social networks are leaving a living, always-fresh data shadow making it easy for us to capture their most relevant contact data. It sounds a bit like we’ve become on-line stalkers, but marketers and business development professionals have been doing it for years. And just as we move toward predictive modeling on these pieces of personal data, so too are our competitors for talent.
By configuring our CRM systems to accurately capture and search these social data points, our sourcing team is more efficient and effective. It has reduced duplicate entries which caused candidate fatigue in our recruiting processes.
I think Dave says it perfectly in his recent white paper “Future-casting: How the rise of Big Social Data API is set to Transform the Business of Recruiting”: “Future-casting has the ability to review the career progression of both internal employees and external candidates. This stems directly from the ability to track candidates more accurately via their social data. Now, more than ever before, corporations and the talent acquisition professionals within them can keep fresh data on every candidate in their system, with a few simple tweaks. This new philosophy of future-casting puts dynamic data into the hands of the organization, reducing dependency on job boards and even social platforms so they can create their own convergent model that combines all three.”
Results Will Come
At Informatica we saw results very quickly because we had an expert dedicated to addressing the challenges, and we were committed to making our data work for us. But if you don’t have a global sourcing team or a full-time consultant, you can still begin at the top of this list. Talk to your CRM or ATS vendors about how you can tweak your tracking systems. Assess and map your current talent process. Begin using products that allow you to own your own data. Finally, set standards such as the ones I mentioned previously and make sure everyone adheres to them.