Category: Talent Technology

Data analytics is a valuable tool for talent acquisition professionals, as specific data can be used to predict things like the success of a candidate or the time it will take to fill a position. It can also help build the type of candidate-centered hiring experience that’s necessary to stay competitive in this tight talent market.

However, sophisticated artificial intelligence and massive sets of data can make data analytics seem overwhelming and confusing. In order to understand what this technology can do, you need to have a basic understanding of what goes into the process. We’ve put together some definitions for the terms you need to know to start to take advantage of data analytics in your organization.

Big Data:

Big data is a simple sounding term, but experts say defining it can be surprisingly complicated. Forbes argues there could be as many as 12 different ways to interpret the phrase. Some of these explanations are practical like, “data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges.” Other definitions focus on the forces that drive the use of these large data sets like, “a new attitude by businesses, non-profits, government agencies, and individuals that combining data from multiple sources could lead to better decisions.”

When we’re talking about how data can be used in recruiting, think about big data as the raw material, the large sets of information, that can be used to make predictions and inferences about candidates, employees and staffing needs.

Small Data:

Data doesn’t have to be big to be useful. For many organizations, smaller sets of internal data can provide critical insights. According to the Harvard Business Review, small data is data of a manageable size that is already somewhat organized, and it mostly comes from your own data systems. Most organizations have been using small data for years.

Data Analytics:

Big data and small data are useless unless you have a way to analyze it; that’s where data analytics comes in. An article in the International Journal of Information Management calls data analytics the “efficient processes to turn high volumes of fast-moving and diverse data into meaningful insights.” There are a few types of data analytics – descriptive, predictive and prescriptive.

Descriptive Analytics:

Descriptive analytics describe what happened in the past. Descriptive analytics software combs through large or small data sets and produces useful information about trends and patterns. According to Information Week, the purpose of descriptive analytics is simply to summarize what happened.

Predictive Analytics:

Predictive analytics is the next step. It uses data to find patterns and then uses those models to attempt to predict the future. Predictive analytics can’t tell you what will happen, but it shows what is likely to happen based on past trends.

Another way to look at it, according to PC Magazine, is that predictive analytics is something you can do with AI, machine learning and deep learning. Predictive analytics takes large sets of data and then applies these different forms of technology to see trends and patterns that would be difficult, time-consuming or possibly impossible for humans to accomplish alone.

Prescriptive Analytics:

Prescriptive analytics is a type of predictive analytics that goes a step further. Rather than just predicting what could happen in the future, prescriptive analytics goes so far as to recommend one or more courses of action, as well as the likely outcomes of those decisions, according to Information Week.

Not only can prescriptive analytics predict what is likely to happen, it can also predict the best course of action for a person to take to get a particular outcome. Think about prescriptive analytics as predictive analytics with the ability to make a decision.

AI, Machine Learning and Deep Learning:

Artificial intelligence is an umbrella term. Put most simply, it is a branch of computer science that involves computers doing things normally done by people.

We use AI to perform data analysis because having humans process and analyze all the information would be overwhelming, time-consuming, expensive and in most cases, nearly impossible.

Machine learning and deep learning are the next steps in artificial intelligence, where computers are able to learn how to do something without being specifically programmed how to do that one thing. Machine learning develops algorithms, which are procedures or processes for solving problems. Deep learning is a type of AI that mimics the way the human brain works.

This is especially important to predictive and prescriptive analytics, where the goal is to look beyond what happened in the past and find ways to apply it to what might happen in the future.

What Can Data Analytics Do?

Data analytics technology has applications throughout the entire sourcing and recruiting process. The ability to make predictions and suggest a certain course of action can give employers a better understanding of factors like how long it will take to fill a position or what kind of salary and benefits package will be required to secure a successful candidate. When it comes to candidate success, predictive analytics can use candidate assessments to predict how successful a person will be in a given role and even how long they may stay with the company. Armed with that information, organizations can make better hiring decisions.

If you’re in the business of working with people, at first, data analytics can seem cold, but when applied correctly, it can actually make the hiring process more personal. Candidates leave data behind whenever they go online, log in to a social media profile or make Google search. Marketers are already tapping into that information to personalize and target content. Talent acquisition professionals can make that same data work to customize the sourcing and recruiting process.

We’ll have more on what data analytics can do for the industry and what issues to watch for on the blog in the coming weeks. To learn more about AI, machine learning and deep learning, read our blog post about what they mean to recruiting.

Data analytics is one of the top seven tech trends impacting the talent acquisition industry. To learn more about some of the possible impacts and the six other trends, download our ebook: Seven Tech Trends Shaping the Talent Landscape.

In popular culture, artificial intelligence is the stuff of science fiction from HAL 9000 to Wall-E. AI has also worked its way into our daily lives with virtual assistants like Siri and Alexa. Technological advancements like driverless cars make headlines. Now, artificial intelligence has the potential to transform the hiring process.

Defining the Terms

If you aren’t entrenched in technology, trying to understand the mechanisms behind AI can be headache-inducing.

Artificial intelligence: Artificial intelligence is the umbrella term. Put most simply, it is a branch of computer science that involves computers doing things normally done by people.

The definition of exactly what we, as a culture, call artificial intelligence changes over time. For example, Microsoft Excel’s ability to complete mathematical calculations doesn’t seem like artificial intelligence in 2017, but when it came out in 1985, that’s what it felt like.

Now, virtual assistant programs like Siri and Alexa feel like artificial intelligence because we can talk to them and they respond in the most human way we can expect from computers. Looking forward though, it’s likely the artificial intelligence of the future will make these current iterations look simple.

Machine Learning: Machine learning is the next step in artificial intelligence, where computers are able to learn how to do something without being specifically programmed how to do that one thing. Machine learning develops algorithms, which are procedures or processes for solving problems.

Examples of machine learning are everywhere. Email spam filters learn how to identify spam depending on context and subject. Facebook’s photo tagging algorithm learns to recognize faces based on previous tags.

Machine learning has a lot of potential. According to the Harvard Business Review, corporate investment in artificial intelligence is expected to triple in 2017, and experts expect the talent acquisition industry to see major impacts from machine learning advancements.

Deep Learning: Deep learning is one step deeper in AI. It’s a subsection of machine learning that uses computers designed to mimic the way the human brain works.

According to Forbes, deep learning is already common in everyday life. It’s used by Google in image and voice recognition. Netflix uses it to recommend shows, and Amazon uses it to predict your purchases.

Predictive analytics: Predictive analytics uses data to find patterns and then uses those patterns to attempt to predict the future. Another way to look at it, according to PC Magazine, is that predictive analytics is something you can do with AI, machine learning and deep learning. Predictive analytics takes large sets of data and then applies these different forms of technology to see trends and patterns that would be difficult, time-consuming or possibly impossible for humans to accomplish alone.

What’s Possible Now?

These technological advancements are already making an impact on the talent acquisition industry. New technology solutions are developed every day on the cutting edge of what AI can accomplish.

One major application for AI identified by Harvard Business Review is sifting through job applications and selecting the best candidates for the next step in the process. Applicant tracking systems already search through resumes, but AI advances can make them better, moving beyond keyword searches. The impact of AI can start even earlier in the hiring process, like using AI to source candidates by searching through social media profiles.

AI helps remove bias in the hiring process. AI structured interviews also help recruiters and hiring managers focus on relevant skills, and AI interview analysis uses data analytics to predict how successful a candidate will be at a position. TLNT further explores the ability of technology to sort through resumes, looking only at relevant information rather than social cues that may sway recruiters or hiring managers.

AI improves the candidate experience as it becomes a part of the entire hiring process. Studies already show that people prefer chatbots over humans for customer relations. That could apply to multiple stages, from applications to initial interviews and scheduling. AI also makes sure candidates don’t get lost in the process, an issue that’s frustrating for candidates and time consuming for recruiters. At PeopleScout, we use AI technology that allows candidates to feel like they’re talking with a real person and lets them to apply through a conversation instead of a long, impersonal process.

The interview process is also seeing the impact of artificial intelligence. Digital interviewing can allow for live or on-demand video interviews and can also use artificial intelligence to provide reports that analyze verbal response, intonation and nonverbal communication.

Where Do We Go Next?

The question of what is possible in the talent acquisition industry through artificial intelligence is the most difficult question to answer because the possibilities are constantly evolving.

On a different level, AI will change the jobs the talent acquisition industry needs to fill. AI skills will become indispensable. Transportation companies are already looking for workers to maintain driverless fleets, rather than drivers to sit behind the wheel. New industries and job titles demanding new skill sets will emerge and demand innovation from talent acquisition.

AI is one of the top trends impacting the talent acquisition industry. To learn more about how it could transform recruiting, check out our AI in Recruiting Handbook for Talent Acquisition Leaders.