In the sphere of web data, some of the most sought-after datasets are employee and company contacts. That’s understandable—it’s hard to collect millions of emails or phone numbers, let alone update them regularly. However, this data serves the sole purpose of contacting a person or an organization.
That’s why today we want to discuss a different type – employee data. Contacts are rarely included here, and the extra data points greatly expand your options for leveraging such information. But let’s start with the basics.
What is employee data, and how is it sourced?
In short, employee data contains information about professionals. Such databases usually include names, locations, workplaces, positions, and education.
Additionally, you may find out when the person started their current position, how many years they have worked there, and the experience they already have.
Like in most raw datasets, you should expect to find some duplicates, gaps, and inconsistencies. That means some records might be missing country, location, or both. Nevertheless, this won’t stop you from seeing the big picture and developing data-driven insights.
All this employee information is usually collected by scraping publicly available web data, which may include social networks or specialized web platforms. Once more, data freshness should be one of the top priorities unless you’re OK with not knowing where the employee currently works or other information that’s bound to change. That’s why it’s always better to have a supplier that takes care of updates instead of buying a cheaper database compiled two or three years ago.
Main employee data types
Employee data has four main types. Depending on the dataset richness, you may get some or all four. Let’s discuss them in more detail.
1. User info
These are the key details about an employee. Without them, it would be near impossible to effectively use other data points, track them down, or find more information on your own. Here, we’re talking about name, location, current workplace, and so on.
After all, what’s the point if you found the best candidates for your advertised position if it turns out they are in completely opposite time zones and not looking to relocate?
2. Employment details
Employment details focus on the employee's career. Some examples are the starting date of their current job, experience, and roles. Based on this data, you can evaluate a person's progression and its speed.
Here again, we want to stress the importance of data freshness. Let's say you find the right candidate, and it turns out that they worked in their "current position" three years ago. If that wasn't enough, they switched from coding Python to breeding them.
Also, you shouldn't forget that employee datasets contain valuable information about companies, such as URLs of their profiles. Based on that, you can find insights about their size, location, employee count, and other factors. If your business is small, maybe you would prefer someone who hasn't spent their whole career in enterprises.
3. Job details
Next, we have job details. These include more in-depth knowledge about job positions and responsibilities. This can help segment your candidates into junior, mid-level, and senior levels and check their work experience to see if that’s really the case. So, in a way, job details are new information but also a way to qualify employee data.
For example, you may see that a person moved from junior to senior in five years, but their employer and job description remain virtually unchanged. At the same time, you can find juniors with more experience and willingness to work hard.
4. Credentials
While job details are often enough to determine the right candidate, sometimes it comes down to signals that show the person is really into their field of specialization. If two data analysts have finished the same university and worked in renowned companies, you might want to give an advantage to the one who’s been attending Python and SQL workshops for the last few years.
In this context, credentials comprise extra and nice-to-have things like licenses, certificates, and courses. Additional skills like machine learning and data visualization can also be valuable assets. Finally, multi-language companies might be targeting candidates who speak Spanish in addition to English.
Top sources of employee data
Currently, the best employee data source is the largest professional network. It offers 687M+ data records that include employee name, position, experience, employment length, and dozens of other data points, updated daily.
In short, professional network data on employees is the fastest way to data-driven HR tech solutions. However, employee data collection doesn't end here.
Glassdoor is another great source of employee data, specializing in reviews. It allows you to identify employee sentiment by checking how they rate their management or work culture. Glassdoor also provides the author's location and title.
Next, we have Wellfound for enhanced talent sourcing. It contains employee data, such as name, job title, and experience, to name but a few. Wellfound updates all of their records monthly and adds 25K+ more.
Indeed offers jobs and employee data, which is great for talent sourcing and industry trend analysis. They update their dataset each month and also have 40 months of historical data.
Certainly, this is not the final list of top employee data sources. Learn more about Owler, GitHub, and other datasets.
Main employee data use cases
Companies use employee data not only for recruitment. With such an amount of data, you can get exciting insights relevant in various scenarios, such as lead generation or investment. Let’s see some real-life examples.
1. Talent sourcing
The most obvious use case for employee data is talent sourcing. Whether updating your HR platform or building one of your own, an up-to-date database can be a great starting point. It is crucial for sourcing talents on a macro level as well as locating individuals that are hard to find.
All this data will give you an overview of the labor market, predict trends, and always have the top talent at hand.
2. AI-driven recruitment
AI-driven recruitment is inseparable from employee data. As more and more processes become automated, finding the right candidate without moving a finger is no longer a luxury, it’s an inevitable future. For instance, AI can help identify candidates who are likely to switch jobs soon and best fit your current team best.
3. Pairing with other datasets
Let’s not forget that pairing datasets can have a synergistic effect. It’s easily seen when the employee and firmographic worlds collide. This way, you can see the most sought-after candidates and the desirable features of those already working.
Job postings will help you write the best job ad, and employee data will let you find the top talent. Next time, you will only have to repeat this success formula to profit.
4. Lead enrichment
Employee data can enrich lead information, be it B2C or B2B. You can save time by checking whether certain people are the decision-makers or mere mortals.
You can also boost your lead generation engine with extra employee data. Not only can you add information to that which you already have, but you can find new potential clients based on your requirements for location, education, and other data points.
For instance, you find a company that's a perfect buyer of your B2B product. However, you have no idea who their CTO is. Enter employee data. Turns out you're both from Austin and studied at the same university. They have also worked for your competitor before. Now, I feel that growing itch to call this guy and say, "Hey pal, remember me?"
5. Investment intelligence
And now, something for our dear investors. Employee data can say a lot about the company—whether there are new hires, what skill sets each department offers, and so on. Plus, are they even investing in human capital?
This can become clear by checking employee training investments. Many employees like to share their newly acquired certificates and diplomas on social media, so finding nothing like that raises questions.
Additionally, management quality can be determined by the performance track record. If you see a lot of employee turnaround while other departments stay stable, chances are that’s an instance of a fish rotting from the head down.
Employee data collection can help create a more effective hiring process that will be noted by the investor, especially if the plan is to double the company size in one year. This might decide whether there will be another round of investments.
Finally, from a heuristic perspective, an investor may find one good reason to close the deal and go for it. But with more data available, he can switch to a list of pros and cons, where you can win their heart with a bouquet of motives.
Bottom line
Hopefully, we have given you some ideas on how to use employee data for the best results. Some clients fail to see the less obvious ways to get the most out of their dataset. To avoid this, you should start asking more questions.
While employee data records might look mundane, with job titles, experience, and education, think about what these data points could signify and what sort of analysis can be achieved using this data. Could some of them correlate, if not globally, then in your industry?
Try this, and you will see that the actual value of employee data collection is more profound than you anticipated.
