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A Guide to Visualizing Your Recruitment Data

A guide on how to turn your raw data into charts and insights that drive better hiring decisions and business outcomes.

Data is foundational for performance, decision-making, and storytelling and is a critical pillar in any high-performing recruitment team.

The only problem is that data can be overwhelming and challenging to work with if it's not organized correctly, trusted, or visualized in a way people understand. We've created this article to help you visualize your recruitment data, decide which metrics to track, how to find your sources of truth, and how to pick the right visualization tools.

By the end, you'll better understand how to turn your raw data into insights that drive better hiring decisions and business outcomes.

But before we dig into the step-by-step, let's clarify a few things:

  1. Data is not only numbers.
  2. Data is just raw information, and you often need to combine several pieces of data to get to your final metric.
  3. Your output is only as good as your input.
  4. More data is not always better.
  5. Data does not always tell the whole story. Sometimes, you need context to capture the nuances of a situation or result.

Okay, now that that's out of the way, let's get into how your TA team can make the most of its data.

Step 1: Decide what data you are interested in

As mentioned, it's important to understand that data comes in many forms and is not always about numbers and charts. Although charts are excellent for visualizing and making sense of a message (more on that later), the input that goes into the graph can be qualitative and quantitative.

We have an entire article on modern recruitment metrics you should be measuring, so we suggest you start there to really go deep on this topic. But if you're crunched for time, here is the tl;dr version of what metrics you might consider tracking.

  • Time-to-hire
  • Cost-per-hire
  • Applicant-to-hire Ratio
  • Offer Acceptance Rate
  • Source of Hire
  • Delivery Forecast
  • Candidate Conversion Rate
  • Pipeline Diversity
  • Reasons for Offer Lost

A lot of these are "hard," measurable metrics. But it's important to remember that the secret weapon of data-driven teams lies in their ability to analyze and act on both quantitative and qualitative insights. For example, common phrases that hiring managers use to reject candidates or words that repeatedly appear in candidate feedback surveys. This is especially important given that people are the focal point of any recruitment process, and peoples' sentiments and behavior are complex and challenging to capture in quantitative metrics alone.

Your list could get long once you sit down and start carving out the quantitative and qualitative metrics you want to use. However, be critical of yourself and those requesting the data by asking why you are measuring this data in the first place and what value the measurements bring to your team. Is it actionable? Is it representative? Is it impactful on company-wide metrics? It can be easy to lose yourself in everything you can measure, but the more noise you let in, the easier it is to lose focus. So be critical and narrow your list as much as possible so that your dashboard creates value, not chaos.

Step 2: Find your source(s) of truth

Once you know what type of data you are interested in, you need to figure out where to find it. Finding it doesn't mean that "time-to-hire" is lost in the ether somewhere. However, it might be that the project start and end date (i.e., the two raw components that make up time-to-hire) might be. So you may need to work backward and put different data points to paint the dashboard you want to see.

Your ATS is likely an excellent place to start since most applicant tracking systems have at least a basic pipeline and candidate metrics. Or maybe you have a spreadsheet where you keep track of more granular metrics that aren't available in your ATS, or perhaps you store candidate feedback in the survey tool you used to collect it in the first place.

It's okay to have data in separate systems since it's unlikely that a single spreadsheet or ATS will service all of your data needs. But the most important thing is having a single source of truth (SSOT) regarding each data point. For example, if you store candidate feedback data in a survey tool, you don't need to report it "live" in a spreadsheet (unless the spreadsheet can pull from the original source in real-time).

Why? Because there will always be mistakes and delays when you manually transfer data from one source to another. So it's better to keep data in a single location and then pull it together in a slide deck or spreadsheet for reporting purposes rather than trying to keep up with multiple sources in real time.

Collecting the same data in multiple locations can lead to conflicting results and confusion around which data is "correct."

At Amby, we use Notion databases as our source of truth. We track all project-related data in a Notion database for each recruitment process we work on (i.e., project start and end dates, type of role, number of sourced candidates, etc.).

Step 3: Understand your stakeholders

As your recruitment data matures and your company grows, so will the complexity around what data to show and to whom. This is why visualizing your recruitment data doesn't end at displaying information on a chart. It also doesn't stop at knowing which data to show to which stakeholders. Instead, it involves how to show it, when, and how to handle ad-hoc data requests.

If we were to break down the typical stakeholders that a talent acquisition team deals with on a daily, it might fall into these five categories: C-Suite, Functional Leads, Finance, Hiring Managers, and finally, the Talent Team itself.

It's a given that all of these stakeholders need different information at different times and in various formats, but let's dig into some more concrete examples.

  1. C-Suite. A group of C-Suites might want general high-level data like time-to-hire and cost-per-hire, as well as any interesting stories or key takeaways from the more nitty-gritty data (i.e., a 50% increase in "salary" being the reason for offer lost this quarter compared to last quarter). But generally speaking, keep it high-level and focus on your team's actions based on the data presented.
  2. Functional Leads. Similar to C-Suites, Functional Leads are likely to keep their focus on more high-level metrics; however, unlike C-Suites, they might be more interested in how their department is benchmarking against the company as a whole, or even other organizations, on these metrics, which adds a layer of benchmarking complexity.
  3. Finance. In most organizations, people teams control upwards of 50% of costs: headcount. This means recruiting, retention, and everything in between is critical for the finance team. It also means that when speaking to them, you will want to get into the weeds around churn, compensation and leveling, hiring volume, and project timelines so they can forecast accordingly.
  4. Hiring Managers (might overlap with Function Leads). Hiring managers (who also might be Functional Leads) might be looking for more nitty-gritty data around how efficient their team is able to bring on new employees (i.e., interview-hours-per-hire), what their department-specific drop-off rates are, and what type of feedback they are getting from candidates throughout the interview process.
  5. The People and Talent team. Finally, the Talent Team needs to dig into the micro details (i.e., reading this month's candidate feedback one by one) to understand the full story of what's really going on behind the numbers. Understanding what the data is trying to tell you means you need time to analyze it as well as easy access to the data itself. That's why we suggest having an open-access dashboard that everyone on the TA team has bookmarked and focus time slots in their calendar to dig into the numbers regularly.

Step 4: Pick your visualization method

While having all project data in standardized rows and columns seems structured, it's not ideal when interpreting data and seeing trends over time. So, to get the most out of your raw data, you will need to visualize it in a chart or graph. There are so many ways you can visualize your metrics, but here is a general guide for what type of data to use.

  • Bar charts work best for displaying categorical or discrete data. This includes data that can be divided into categories or groups, such as job positions, recruitment sources, or candidate demographics. Bar charts effectively compare values across these categories and identify relative differences.
  • Line charts are ideal for showcasing trends and changes in numerical data over time. This includes continuous or sequential data, such as the number of applicants, interviews, or hires over a specific period. Line charts help visualize patterns, fluctuations, or growth trends in recruitment metrics.
  • Pie charts or donut charts are suitable for displaying data representing parts of a whole or proportions. This includes data that can be categorized into segments, such as the percentage breakdown of candidates by gender, ethnicity, or department. Pie or donut charts effectively communicate the distribution or composition of recruitment data.
  • Tables are versatile and can be used to present various types of recruitment data, but they work well to display comprehensive datasets that include multiple metrics or attributes. Tables allow for an organized and detailed representation of recruitment data, facilitating in-depth comparison and analysis.

Various data visualization tools, such as Tableau, Power BI, or Google Data Studio, can help you bring your recruitment data to life through interactive and visually appealing charts. They provide more advanced visualization capabilities and allow for in-depth exploration of data insights.

At Amby, we use Tableau to turn our recruitment data into a series of interactive charts that include a little bit of everything (i.e., pie, bar, line, etc.). We also have internal documentation outlining how the numbers are calculated and where each data point comes from. This is just as important as any other step in the visualization process because it ensures transparency and consistency in our reporting and allows stakeholders to understand the meaning behind the data easily.

Step 5: Getting Started

The four steps above are really all you need to get started. However, we've also compiled a few resources to help kickstart your data journey. You can think of it as a data starter pack, and the rest is up to you!

Setting KPIs

Data Sources and Visualization

We hope this list can help you get started on your data journey, and if you have any more questions about recruitment data, talent acquisition, or anything in between, feel free to drop us a line!


Author profile Meagan Leber

Growth Marketing Manager at Amby, who loves writing about the tech, venture capital, and people space.