David Cherry, Senior Director, International Talent Acquisition, CrowdStrike in an interaction with Janifha Evangeline, Editor, Higher Education Review, shares his opinion on how the digital talent acquisition market is evolving in India, how the emergence of AI has revolutionized candidate sourcing by harnessing the power of machine learning algorithms and natural language processing and more.
The global digital talent acquisition market size is projected to grow at a CAGR of 9.45 percent to reach $ 56.57 billion by 2030. How do you see this market evolving in India?
India is a significant market for many industries, and when it comes to talent acquisition, a plethora of global corporations have centers for everything from shared service centers to Centers of Excellence and every other function that an organization should have.
I have been with CrowdStrike for about six years. We did not have a sales team in India when I started. I traveled to Mumbai and conducted interviews in a hotel. And we currently have over several 100 people. I believe it will continue to grow. Following the pandemic, everyone is more cautious in their hiring and avoiding over-hiring. We are focused on making sure we have the right people at the right places.
In terms of expansion, each organization must finally examine its own growth objectives and scale accordingly, as well as consider if it is in a B2C or B2B environment. So, for us, our scale of growth is about how we are expanding as an organization, and then we will scale the teams accordingly.
While the emergence of AI has revolutionized candidate sourcing by harnessing the power of machine learning algorithms and natural language processing, tell us how these AI-driven tools not only help in expediting the process but also significantly help in reducing costs associated with traditional methods.
I am a technophile and I always love to try new things. However, you must be careful about any third-party plat¬forms that you use. We use a lot of online platforms, such as LinkedIn for instance. And a lot of those platforms are beginning to implement algorithms and machine learning for candidates. So, there is a big part that will help some¬
one who is searching for a job where the hope is to render them the job offers that are more suitable based on what they are looking for.
The opposite is true for organizations and recruiting teams. We can then utilize that information to try to discover those prospects and get a better fit. There are several outstanding advantages. Any organization’s employment processes are often extensive, but they always begin with identifying people to talk to, either through applications, people applying directly, or proactive searches. In such type of screening stages, whether it is automated or manual, AI tools are used to integrate into whichever ATS system that is being used. There are some genuine potential benefits to eliminating processes such as unconscious bias, for example, and it can assist in creating an even more equitable hiring process than you may assume you are already running. While we have several hard-to-fill roles ourselves, there are some niche skills, particularly in the cybersecurity industry, and I believe it can assist potentially discover that talent.
It has the potential to save a significant amount of time, particularly in countries with high volumes of applications. So that, you may utilize AI/ML tools and technologies to screen and filter considerably more efficiently. However, the risk is that AI relies on a massive quantity of data, and as a recruiter, you must know what you are looking for if you use it. Because if you get that criterion wrong, the results will be less useful. The risk is that you must be certain about what you are looking for and why you are using it for it to be effective.
"The importance of data analytics in the talent acquisition process cannot be underestimated"
The candidate experience is another important factor. Although I have been in recruitment for a long time, I would never want to see AI or machine learning completely take over a hiring process because the insight, judgment, and human contact, both in terms of assessing candidates during interviews and in terms of building a relationship with the candidate, are factors that AI is likely to struggle with. Furthermore, there are risks involved, but they can be mitigated if you have a robust hiring process in place. Then there is the ability to move swiftly, which will save you a huge amount of time, ultimately saving you a significant amount of money as well.
Kindly elaborate on how the use of analytics across talent acquisition processes assists in data-driven decision-making and helps provide insights to identify areas of strength and weakness. Also, how does talent analytics help in proving the effectiveness and ROI of recruiting software investments.
When you work with spreadsheets, you should have teams that focus only on working with spreadsheets. We have a great team that completely focuses on our HR data and system. A real-world/practical example would be the hiring software we use daily. We will consider where we can get higher rates and returns on our investments. We also look at information like the amount of hires we have made, the quantity of interviews, and the number of applications we get. Here, data is crucial because it provides a narrative and drives decisions.
The importance of data analytics in the talent acquisition process cannot be underestimated. You can go down even further and take a deep dive into that data to learn and develop. It includes specifics like how many days it took to fill a specific post and the number of candidates we had. Additionally, your HR team can use it globally in all facets of business. It is something that is consistently used, and it includes metrics for a variety of things, from return on investment to job boards to source of hire to examining the effectiveness of referral programs, such as attrition, retention, top performers, and everything else you can think of to do with hiring.
Therefore, you cannot undervalue it, or oversell it. I spend probably 50 percent of my week looking at data either on dashboards or spreadsheets, Google Sheets, or in our ATS system. You use it to try and make the best decisions about predicting the future and learning, what went well, what did not go so well and, how do you continue to improve.
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