Never has the need for an enhanced human resources experience been so urgent as now. We are still going through the pandemic and have by now explored different work structures to suit it. To top that, Analytics and AI are an industry parading with progress, one after another. This is changing how we hire and keep people in an organization. The right kind of talent is becoming more crucial by the day. Opportunities are endless for the most kickass workforce out there as cloud computing and advancements in AI and analytics traverse the limitations of time and distance and transmute the world into one large but connected space. 

Contrary to the most obvious idea that, in this connected space, AI and data will replace people, people become all the more valuable. But with data analytics, it’s the right kind of people that rule– the most competitive, the most dynamic, the most kickass,  To paraphrase a recent insight in the HR industry, Data and AI are tools and the ones who can operate them the best will certainly win over those that remain oblivious to them. As an HR manager, if you’re not incorporating data into HR decisions already, you’re missing out on the new possibilities our times have to offer.

Though data in hiring is a recent idea, it’s already popular amongst tech giants and startups alike for its ease and efficiency. In today’s climate, data can lead to informed hiring decisions if applied the right way.

But we also understand that it can seem daunting, and unnecessary even to invest in data for hiring decisions. And that’s why we bring you this exhaustive guide to the whys and hows of data-driven hiring decisions.

Here we go: 

Before we get to the thorough manual for hiring using data, let’s explore in detail why it’s a bandwagon you should definitely be riding on: 

• Make Better Decisions: Through you can analyze your history of hiring decisions and understand what led to them. You can use data to know the precise skills that benefit your company and your biases in the process of hiring and the little loopholes that you can easily avoid. This helps you make your footing strong while making these decisions in the future. 

• Less Room for Bias: Data collection and analytics is the product of a sophisticated computerized system filled with only specific inputs, which means that there’s no room for biases. This can lead to higher objectivity in your decisions and diversity in the workforce. 

• Reduced Inefficiency: Data systems can not only analyze available data but can also extrapolate data for the future. Using data, HR managers can create predictive models to forecast a candidate’s potential job performance and the skills they will require to reach their top performance. So along with hiring, HR managers can now plan for training and induction according to their needs. 

• Measure and Track Performance: Using data, you can track the efficiency of the hiring decisions you’ve made by tracking the performance of your teams. You can then identify areas for improvement and implement changes accordingly to optimize operations at the organization. 

• Enhanced Employer Branding: Employers can identify attributes, skills, and post-hiring activities for successful hires and recreate them for a continued culture of right hiring for a longer time. This makes for an overall better candidate experience at the organization, thus enhancing the employer’s reputation in the job market. 

Since now we are in agreement on the utility of data-driven hiring decisions, let’s set the framework for getting out feet wet in the process. 

Here are a few metrics you can use to build a skeleton of a data framework you can further use to make decisions in recruitment:

• Time-to-fill: In the current scenario, time is money. The time it takes to fill a job opening is an expense for the company. So managers should take time to fill as one of their primary considerations while drafting a data system.
• Cost-per-hire: It’s always better to save costs by eliminating unnecessary operations. Aim to knock out redundant or worse, counter-productive costs from the hiring system by actively looking out for them using data.
• Candidate Quality: This is where you directly measure and optimize the efficiency of your system. By measuring the chasm between desired candidates who meet the job qualifications and the ones who show up, companies can further better their frameworks.
• Candidate Experience: This metric is about measuring different components of candidate experience at an organization. One key factor could be the employee retention rate. The amount of time employees stay with an organization can indicate aspects of company culture and the recruitment process.
• Diversity and Inclusion: Fortunately, we live in an era where diversity and inclusion is a conversation but sometimes keeping track of exact numbers can be difficult. Using data, companies can ensure that the decreed percentage of applicants from underrepresented groups is kept in mind while making hiring decisions. 

Take your choice of weapons from the above list depending on the needs of your organization. Identify what metrics indicate the performance of your data system and the decisions it leads to. Once you’re through,  it’s time to amplify the process.

Here’s a List of the Next Steps to Make a Data-Driven Hiring Decision:

• Collect Data: This is where you define the source of data. You can use a range of sources like applicant tracking systems, recruitment analytics software, and candidate feedback surveys among others to collect initial data about the applicants.
• Create a Data-Driven Hiring Process: Congratulations! You have made it through the initial stage-building of creating the process and this is where the real work begins. Now you first organize and clean data by streamlining the flow of data. Check for redundancies and omissions. Once the data is clean, you begin analyzing it to gain insights. The goal is to turn data into information and information into useful insights. Visualizing and organizing data in a way that makes it easy to read may help in identifying patterns and insights.
• Making Data-Driven Hiring Decisions: All the work you’ve put in to collect the right kind of data in the right way so far will now culminate into decisions you make. You might implement new recruitment channels to attract a more diverse pool of candidates based on your understanding so far, you might adjust the job description to attract candidates with a specific set of skills or experience. While you continue to put insights into action, don’t forget to monitor the progress you make regularly. This will help you gauge the accuracy of your decisions and also make timely corrections. As you continue to iron out the kinks and make the hiring process to your satisfaction, you can continue to modify your data and analytics tools depending on your needs. 

As more and more companies reach their hiring goals, data in recruitment is gaining speedy popularity. Here are a few examples of how managers used data to solve the most complex problems in hiring to reach their goals: 

• At the pace our ways of life are changing, early identification of upcoming trends is a huge advantage to businesses. To illustrate this, let’s look at Deutsche Telekom’s example. They began by collecting macro-economic data pertinent to the job industry and through the use of precise data and intricate analytics, they were able to convert trends into relevant skill sets unique to their company. Not only that, they were able to spot and remove white spots in their day-to-day processes as well.
• At Psychometrics Canada, they are using their in-house intelligence of personalities and collected data to arrive at personalities that suit different levels in the organization. They take top performers and analyze individual personality profiles to arrive at insights that ease hiring across boards for them. The good news is that they also make some of these data and insights available for others to refer to.
• Dell has been using data and insights on its employees in a unique way. The computer giant has been using data to find out what their existing employees are like. It, then, uses these preferences to engage with its own employees on social media and also to push the company’s core culture online. Through these practices, Dell is creating a culture of community that contributes to its high retention rate. 

As beneficial as data-driven hiring decisions are, there are considerable challenges in managing and collecting data and then using it further to make informed decisions. The first step to overcoming these challenges is knowing them.

Here are a few common challenges that tend to arise: 

• Collecting Data: As ubiquitous the use of data is in our world, collecting the most accurate data is a bit of a tall order. The initial stumbling point is ensuring the accuracy of data. It can be very difficult to access data from reliable sources. It’s expensive and cumbersome to collect only relevant and accurate data and most managers often throw in the towel at this stage but it’s also the most crucial step in the process.
• Integrating Data: Consolidating data from several sources to make the most efficient decision is not a cakewalk either. All the collected data has to be sifted now to find the most useful insight. This is a towering job. Managers have to deal with their own biases while screening the data they have. They also need to be mindful of their own prejudices that can come up while interpreting data. Even after they have done due intelligence so far, the job keeps getting more demanding as they integrate data as insights and decisions in the workplace.
• Maintaining Data Security: Data is the most valuable jewel of our times. Careful use of data can turn tables in no time and our news portals or even Instagram scrolls are rife with examples of data use. And that is why handling data in a safe way becomes even more crucial. Managers need to ensure that data is collected in a way that agrees with the participants and after that also they need to be meticulous in ensuring that the data is protected from breach or cyber-attacks. Companies need to invest in advanced data protection systems before they venture into collecting and handling data. 

Conclusion

Human resources are the bedrock of any company’s achievements. People are not just a resource, but the lifeblood of an organization. Talented, motivated, and engaged employees have been found to be the consistent factor in all successful businesses. 

Finding this human resource can be made simple and precise with data. In the current dynamic business environment, it is all the more crucial to make informed and objective hiring decisions based on measurable and relevant data. 

Moreover, data-driven hiring decisions reduce the possibility of errors caused by bias and heretics. Data can buttress your hypotheses on what makes for a growth-oriented people culture. With the application of data, a company can begin to look out for exactly what it’s looking for. We hope that with the help of the manual above you can make more data-driven decisions in hiring and build a well-equipped workforce that can overcome the challenges of the 21st century. 

As you continue to innovate in the HR world, don’t forget to check the most kickass coders and developers out there: workfall.com. (Here’s a secret: We found them after taking a series of data-driven hiring decisions.)