The True Cost of Bad Recruiting Data
Companies lose more than time and energy when they work with bad recruiting data.
Given the speed and scale at which the talent market has changed over the past year, it’s not surprising if talent leaders want to be better prepared for the unexpected. One of the first things they can tackle to improve their preparedness is recruiting data quality. Having access to transparent and high-quality information about recruiting processes, candidate databases, and talent team performance can make the difference between being agile and decisive, and reacting too slowly to a fast-moving market.
But how do you assess the quality of your talent data, and where you are losing the most value? How do you identify bad data, and stop it from impacting your recruiting performance?
The Cost of Bad Recruiting Data is Higher than you Think
Data analytics teams can spend more than half their time processing and cleaning up data, and that applies to anyone working on data in your recruiting teams, even without a dedicated recruiting analytics or recruiting operations function.
With a better data strategy in place, companies can redirect resources to more added-value work. In a recent survey by McKinsey, it was found that leading enterprises usually spend 74% less time on these low-added value, data-related tasks, such as deduplication, cleaning, and manual tasks that could be avoided if the quality of the data was better.
To be compliant with data privacy laws such as the GDPR or CCPA, you need to keep accurate records of your candidate profiles that show who they are, when and how they gave consent, or how you removed them from your systems when they revoked that consent. If those records are inaccurate, even if your teams are actually compliant in their actions, then you cannot prove it to regulators and might be fined heavily.
This also applies to regulation around data processing in certain geographies, like Russia, where you have to prove you are processing all candidate data in Russian territory, or China, where you need to show that you are keeping accurate duplicates of all your data within the Chinese territory. If you’d like to know more about the subject of candidate data compliance, definitely check out our recent webinar: “Candidate Data Compliance: Are you Prepared for the Risks?”
Missed opportunity with better talent strategy
This is definitely where your organization is missing out the most. With data quality, it’s often a case of “you don’t know what you don’t know”. What insights would you be able to glean from the market if you had access to real-time, accurate reporting? What false assumptions is your team operating under because of incomplete data, and what are they costing the business?
You can build a more robust business case for better data quality by diving deeper into a few chosen challenges that your talent team faces, and getting to the root causes behind them—something that your recruiting ops team can help with. Here are some examples of what that root cause analysis might uncover:
Despite putting a lot of effort into budgeting resources and optimizing the time use of your recruiters from the top down, you might be consistently missing core KPIs, and seeing your percentage of open vacancies by the end of the quarter go steadily up. This can easily be caused by recruiters spending too much time chasing inaccuracies or missing pieces in a candidate’s profile.
You are using the same recruitment marketing strategies as your competition, and yet, they seem to be hiring at an accelerated rate compared to you, and they often beat you to the better candidates, as if they know where to find them, or how to create a better experience for them. This could be due to their ability to test campaigns faster, or report on what works and what doesn’t across the whole candidate experience instead of looking at siloed datasets.
How to Recognize and Avoid Bad Recruiting Data
As you can see from the examples above, bad recruiting data is not simply inaccurate data. It’s data that does not deliver all the value that it should, and that can be due to a number of reasons. The way we think about it at Beamery, data quality is driven by two factors:
Is the data collected and processed accurately?
Candidate location, behaviors, or job descriptions change constantly, especially in the choppy recruiting market that we are seeing right now. The candidate data sitting in your database must always be accurate and up to date, to ensure your campaigns have a positive impact, your reporting is accurate, and your recruiters are able to identify the right people quickly even among large numbers of applications.
The aim is to have up-to-date, clean, and unified data across the board. This makes it easier to search, filter, use in personalized campaigns or sourcing activities, as well as automated workflows as we will discuss below.
Is the data centralized into a single source of truth?
Having beautifully curated databases is not enough, if talent teams cannot use them to their full benefit. On one hand, they need the right recruitment analytics skills to even know what to do with it, and on the other, their talent technology ecosystem needs to be set up in a way that enables all talent data to communicate with each other, to be consistent accross the board, and sit in the same single source of truth.
Talent data platforms play that role for the talent team, by being the central database that unifies information across solutions, and surfacing meaningful, reliable data to every other talent tool.
The benefit of having all data live in a single system is that you can access all of it more easily for bulk actions such as filtering, reporting, or even to use it as a learning dataset for a Machine Learning algorithm.
How Talent Teams can Avoid Bad Data
Investing early in the quality of your recruiting data is the best way to avoid unnecessary spend in the future trying to address avoidable issues, or to catch up to the competition. Compared to the 2016 to 2018 3-year period, companies’ spending on data-related costs is increasing by almost 50% from 2019 to 2021. This yet another way in which talent acquisition is inevitably changing, and getting ahead of that change is by far better than “wait and see”.
We think there are three areas to tackle when it comes to bringing your team’s data game up to speed: automation, governance, and training.
Automating recruiting data processing
Replacing human processes with the right automation can not only reduce the chances of human error, it can also expand your team’s scope of impact. There are very parts of the recruiting process that cannot be automated to at least some extent, from sourcing and importing candidate data into your talent tech stack, to building large scale recruitment marketing campaigns, to enforcing data compliance.
A large part of the impact of automation comes from data enrichment. Candidates share huge amounts of data that could be relevant to prospective employers online; the hard part is keeping up with it. If talent teams can give that part of the process to machines, the result will be more complete and up-to-date candidate data to work with.
Instituting Better Data Governance
A better tech stack is not the only improvement needed for a better talent data strategy. A powerful talent data platform, supported with great automation, can only help if the teams using it are working with a cohesive set of conventions and rules, and that is where governance comes in.
Say you have a sophisticated CRM that allows you to centralize candidate data from multiple sources, and then search, filter, and apply bulk actions to it in many useful ways. All this wonderful data will do only half the job if it is not recorded following the same conventions. What if the Events team records “seniority level” differently from the technical sourcing team? What if the Leadership program team doesn’t even have a “seniority level” field for its candidate data? What if every country uses its own reprting standards for engineering candidates, making it impossible to hire from the global candidate pool even when the roles are remote? What if some teams have strong quality checks in place for their data, but others don’t?
Good governance should aim to solve exactly that kind of issue. It doesn’t need to be complex or overreaching, it doesn’t need to add tons of complicated processes on top of recruiters’ usual workload—it just needs to identify potential roadblocks like the ones above, and work with the concerned teams to remove them. You can find excellent suggestions for data governance models here.
Why Talent Teams can Benefit from Data Quality Training
This brings us to the last part of a strong data strategy: training. At beamery, we have noticed that recruiting teams are invariably more successful at maintaining high quality data when the whole team is trained on it, and not just the “data people”.
The goal is not to make everyone into an expert; rather, it’s about linking team members’ behaviors to the impact on data quality in a highly granular way. A sourcing team member might not know exactly why omitting this simple data point, or not resolving a small duplicate warning, means that the reporting team down the line gives the wrong forecast to executive leaders. Having that precise knowledge makes it a lot easier to adopt good data hygiene practices at every level of the talent team.
The underlying data that feeds your talent acquisition team is vitally important to the success of your recruiting strategy, and can impact the wider business more than you expect. It’s only through up-to-date, accurate data that your team can not only stay efficient and competitive, but also reclaim the time and headspace necessary to be innovative and perform at top-level.
Investing in the quality of your recruiting data is not the kind of decision that gets easier the longer you wait on it. Rather, it’s an opportunity that yields the best results when you take it before your competition. Like many other changes happening in the talent market today, it is inevitable, and it needs to happen now.