With the sudden jolt of the global pandemic to the launch of ChatGPT, it seems as if we’re in a more mature Industry 4.0 – with every trade impacted, including human resources.

But what is at the core of Industry 4.0?  Data.  Big Data. Quantitative data. Nominal data. Qualitative data. Discrete data. Continuous data.

Data is extraordinarily helpful in understanding where we are and what needs to be done to get to where we’re going.

However, not all data is clear even with all the Industry 4.0 tools we now have.  Some data is, well, fuzzy. So, how do recruiters and HR professionals plan with data when it’s fuzzy? After all, what are the numbers telling us if they’re not crystal clear?

What Is Fuzzy Data?

Fuzzy data is essentially “[i]mprecise data with uncertainties which indicates that the observed values cannot be considered as the true unique values.” In other words, the data you may be using does not include “precise numbers, or vectors, or categories.”

However, most “real” data is not precise – or fuzzy.

Let’s look at HR and recruiters specifically.  According to a recent study, when examining Big Data, professionals gather insights around a “wide range of tasks solved by the personnel, both organizational, economic and technological.” However, with such large amounts of data – often surrounding people and their tasks and characteristics – we can’t always determine “true and false.” So instead, we look at multiple possible truths (or in other words, degrees of truth for each interaction, resulting in various possible (and reasonable) conclusions.)

How Can We Plan with Fuzzy Data?

Industry 4.0 has directly impacted HR – with some now calling this impact HR 4.0 – allowing the industry to become more automated and focused on high-level strategic strategies as opposed to manual, repetitive activities.

Through the Internet of Things, artificial intelligence, Big Data, technology stacks, and data analytics, recruiters and HR professionals can now build “more efficient and lean teams,” through attracting, retaining, and mobilizing top talent in this continually evolving industrial revolution.

However, not everyone understands how to pull insights from fuzzy data.  For example, if a manager asks specifically about productivity – but the number of successful key performance indicators (KPIs) don’t match up, then the data may not make sense.

That doesn’t mean the data is bad. It just may be fuzzy.  Someone who understands how to read HR data can specifically pull valuable insights from that data as opposed to someone who is only skilled in reading data while making true or false conclusions.

Here are some best practices to keep in mind when analyzing often fuzzy HR data:

• Data often has to be read in “real time,” as the needs of HR change on a seemingly daily basis. So, knowing when to read this data is critical to garner insights that aren’t stale.
• HR is often behind other departments in having the best (and the right) data analytical tools. Leaders need to reprioritize HR when analyzing Big Data, ensuring that organizational budgets align with needs and strategies.
• Leaders must also prioritize reskilling and upskilling recruiters and HR teams, allowing them to garner the necessary skills for a strategic and insightful analysis of fuzzy data.

It’s time for HR to embrace fuzzy data with the right tools and support.  After all, what benefits HR benefits the organization as a whole – and it’s time to recognize that.

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Sometimes hiring leaders aren’t on the same ship deck as recruiters. How are we supposed to pull them into our dimension if they’re cruising in the Death Star while we’re rockin’ the Millennium Falcon?

Well, friend…let me tell you about the force of predictive analytics and big data.

As recruiters, we are experts in the talent field, but being a consultant to hiring teams isn’t easy. We’re often tasked with guiding our leaders without any gravity in the company. But the right data and trends will give you the power you need to make changes.

Data Feels Like Force Powers

UPDATE: Due to an unforeseen conflict, Marc cannot make this session. Instead, we will be joined by PJ LeDorze. You may know him from shows like The Recruiting Animal Podcast, Dueling Sourcers, and SourceCon events. PJ has 14+ years of Corporate Recruiting and 8 years of Agency experience, and his superpowers are sourcing and closing.

He’ll help you understand the massive power of predictive analytics and big data. Let’s learn how to use this information to Strike up your hiring process and educate your hiring leaders!

In this session, you will learn:

• The benefits of merging predictive analytics into your hiring process.
• Examples of how predictive analytics has improved recruitment outcomes.
• Use data to create a solid recruitment strategy and ensure hiring success.
• Identify high-potential candidates with predictive analytics.

May the power of data (and The Force) be with you.

If you’re unable to attend the live session, that’s okay! Just register and we’ll provide you with all the materials and the recording afterward.

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The Guesswork Era isn’t a real concept — I just invented it right now — so let me try and explain first what it means. In the simplest terms, it’s an executive (“senior decision-maker”) over-relying on their gut instinct when there’s a bunch of information (“big data”) they could base a decision on. Here’s an example:

HR Manager: “Well, sir, we have lots of information on what would make someone successful in this role, and as a result, Joseph is the bes–”

Executive: “No time, Susan! Joseph didn’t impress! Sam! I like the cut of Sam’s jib!”

The situation above could have been “People Analytics,” but instead it became the Guesswork Era. See what I mean?

This happens a lot. It’s not too complicated why it happens either. Most executives don’t understand data that well, and the way it’s often presented to them doesn’t jive with how other things (i.e. balance sheets) are presented to them. For this reason, you could argue two things: (1) is that Big Data might make decision-making slower(odd) and (2) is that for this all to work, we need “data translators” as a job of the future.

Everything I’ve said so far is about the logistics/process of the supposed Big Data era. I haven’t even touched on the psychology. Work is a very psychological place, although we often forget this and try to drown everything in “logical” processes. (Most processes are just invented to make a middle manager feel as if they’ve controlled a situation properly.) Stephen Dubner, who is smarter and more famous than I am, has also noted that a major problem with “data-driven decisions” is that executives want to believe in their gut. In the mind of some of these guys, they arrived at their perch for a specific set of reasons. If “Big Data” removes some of these reasons, how relevant are they anymore? (Not much.) Work is largely a quest for relevance towards self-worth, so who wants 750 rows of information if guesswork makes you feel better?

This is a topic we need to consider more.

The Guesswork Era and the science of selling

Here’s an article from Wharton on “The Science of Good Salesmanship.” I’d classify this as mostly interesting, although at some points the interviewee is like “Data will save us all!” Down near the end, there’s this:

It’s such an exciting time to be in sales and business because this scientific data takes the guesswork out. No longer must we guess our way to success. Now armed with this research, we can make decisions that are accurate and in the best interest of ourselves and those we serve.

True. Or, wait. We want this to be true. It isn’t actually true. The scientific data should take the guesswork out of how we make decisions, but it doesn’t. We’ve created 90 percent of the world’s data in the past 5-10 years, right? At the same time, we have some of the highest levels of decision-making variability in executive history. Shouldn’t all this data be putting our senior leadership teams on the same page, as the data is saying the same stuff to them? Logically, this would happen. But in reality, it doesn’t happen.

Why not?

Some reasons are described above. Other reasons:

• The Silo Effectis pervasive in business, and many executives contextualize decisions relative to their silo — not to the overall company
• A deep belief in data signals to some people that the robots are coming too
• Company-building and market share-stealing is the closest thing some guys get to fun,so damned if an algorithm is going to take that away
• A lot of “big data plays” are really just one company copying another company, and not one company having any kind of strategy around data
• The people companies have in place as their “data team” usually are a bunch of target-whiffers who maybe understand data but don’t understand how to explain that data to anyone else

This all brought us to the Guesswork Era. We want to think this will change with more data, but it probably won’t.

A quick little story on the Guesswork Era concept

Had a job a while back where I sent Analytics reports every Friday. Website, apps, etc. Performance stuff. There’s a concept called “the tyranny of old metrics,” meaning that business changes but people cling to old numbers that used to matter. That idea was big-time in play on these emails. Most people had no clue what I was writing about, so I put some jokes and pop culture references in the emails. Eventually that rubbed a few people the wrong way, my boss snarled at me, and a few months later, I was shit-canned out the door two weeks before Thanksgiving. Hierarchy, baby!

So when I’m dead and buried at this joint, another kid takes over the Friday analytics emails. The first one he sends, he’s got about 700 rows of data in it. Mine were just images and text with some numbers sprinkled in. What happens? People go nuts. They’re forwarding his boss. “What is this? I am swamped! No time to read this!” Of course, most people could have deleted it — but people love to bitch, and bitch they did.

This illustrates (to me) a key point about the implementation of big data. It’s not about collecting the data or having it to show. That’s what most companies think. It’s about (a) empathy for how other people want to interact with it and (b) using it for better decisions. That’s it. Most companies completely miss that, and as a result, the Guesswork Era is likely to live on for another decade or more.

Your take on guesswork vs. data?

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I’ve written before about

Now, I’m a relatively smart person but I’ve never led any HR studies or anything, so let me turn this one over to someone smarter than me.

That would be John Boudreau, who is research director t the University of Southern California’s Center for Effective Organizations. He wrote a recent blog post for the Harvard Business Review about how People Analytics needs to be more user-friendly:

A good case in point is whether HR systems actually educate business leaders about the quality of their human capital decisions. We asked this question in the Lawler-Boudreau survey and consistently found that HR leaders rate this outcome of their HR and analytics systems lowest (about 2.5 on a 5-point scale). Yet higher ratings on this item are consistently associated with a stronger HR role in strategy, greater HR functional effectiveness, and higher organizational performance.

Educating leaders about the quality of their human capital decisions emerges as one of the most potent improvement opportunities in every survey we have conducted over the past 10 years.”

Houston, we seem to have the nutshell problem.

Data analytics: Say it loud and say it proud…

Data is literally meaningless if it’s just collected. That just means you have data, which means people have more work to do trying to scrub/analyze it. Everyone is throwing themselves on the cross about their “added responsibilities.”

Data scientists are being hired at 4.5 times your salary — but it’s all trees falling in forests.

Remember that data is only relevant IF it affects decision-making in some way, and if it’s presented to executives in a way they can understand. This is why we need “data translators.”

Why is this seemingly rocket science?

Executives (aka, “decision-makers”) at companies have been using specific vocabulary (buzzwords and acronyms) for decades to to describe what they do. If HR terms don’t match with those terms, they will probably care less because their incentives and day-to-day schedules are tied to their terms, not whatever vocabulary that HR uses.

Let me give you an example: If you teach an executive to think of talent sourcing as a supply chain, it will have greater business impact. The executive probably knows and can conceptualize a supply chain. He will “get” it. But if you go to him with lots of HR terminology, he likely will not care or dismiss it because it’s not close enough to his “power core” of concepts.

Now, the easiest way around this is to have decision-makers who are adaptable and care to learn more about the business. Unfortunately, I wouldn’t call that normative. When you’ve worked at a place X-time and spent Y-time of that in one silo/division, you get pretty focused up on those terms.

Back to this HR data analytics pull quote above

Here’s your main takeaway, again from John Boudreau’s HBR blog post:

Whatever HR analytics or system you use, it needs to be tied to decision-makers having more info, easier to access info, and making better decisions.”

Otherwise it’s basically being done in a vacuum. The HR data analytics are nearly worthless.

Let’s use a common HR metric example here: employee turnover.

I realize very few companies actually use exit interviews, and the ones that do are usually pretty half-assed, so often the “data” you can glean here isn’t great. But here’s what you need:

• Costs of hiring/recruiting at different salary bands;
• Costs of onboarding at different salary bands;
• Turnover by department per quarter (and annually);
• Turnover by specific manager per quarter (and annually);
• Differences in these costs — i.e. what are specific managers and departments costing the company through turnover?
• Net promoter scores
• Any exit interview insights you have gathered;

None of this data is hiding anywhere. It should all be HR data analytics that people can access relatively easily. We’re not talking brain surgery here.

So now what?

Now put together the data and here’s what it shows:

• People leave at this clip (whatever it is) …
• ... And it costs us this much money when they do …
• … so if 10 percent fewer people left, we’d save.
• And, here are some other solutions/ideas.

Now you’re talking money, and executives probably will care more. You’ll get their attention, and you’re turning HR analytics into their language. Believe me, it’s way more beneficial than trying to jam traditional HR methodology at them.

I hate to break it to you, but most really won’t care much about HR speak.

Bringing it all together on HR data analytics

Here’s what you need to do to make this all happen:

• Collect data that appears relevant (you can test over time if it actually is);
• Organize/sort the data in ways that relate to cost and the bottom line; and,
• Present what you’ve learned in ways, and with vocabulary, that are amenable to the decision-makers.

This is lots of work, but only three (3) big steps. Unfortunately I’m sure most HR/operations guys running HR will break this into 271 process steps, run everyone in circles, present a report that the executives all check their email throughout, and in the end, nothing will happen.

But, it doesn’t have to be that way!

HR data analytics CAN work. It can help you retain more employees and help you build a better workforce. What it requires is just a little big picture thinking — and some come-to-Jesus moments about your workplace data.

The post Hey HR! Data Analytics Doesn’t Need to be Rocket Science appeared first on RecruitingDaily.

That’s how I see employer branding, too. While some might question the metrics that matter and the consequences of when it goes wrong, the bottom line is that when you get it right? It’s a powerful feeling and it translates both on the emotional side and in bottom line  results. That’s of course why everyone is so hungry to create the next Zappos or some other employer brand driven by creative culture in their own company.

That uncertainty has driven a lot of curiosity so my team and I launched an inaugural State of Employer Branding survey in Q3 of this year. The survey tried to dig into recruiters’ and marketers’ thoughts on employer branding as a business function and its interconnectedness with candidate experience. We also dug into challenges today’s companies are facing, what they’re prioritizing, and the bottom line benefits of making an investment in employer branding.

After analyzing nearly 300 responses from a range of companies (20% of which had revenues of greater than $1 billion and a vast majority were from North America), we wanted to share the Cliff’s notes version. If you’re interested in the full report, you can get it here. 

Wax Lyrical: Breaking Down Employer Branding Trends

1. So Happy Together: Employee Happiness and Employer Branding Are Connected

Having a place people actually want to work is a prerequisite for a strong employer brand. So it probably makes sense that almost 9 in 10 professionals felt employee happiness and employer branding are connected. This is an important point. Before pouring budget into improving your outward facing image as an employer, start by fixing chronic issues in your organization. The factors that comprise employee happiness (culture, benefits, work/life balance, career opportunities, etc.) all contribute to employer brand.

2. All The Way Up: Employer Branding Continues to Rise as a Business Function

While many companies have thrown together strategies for employer branding programs over the past few year, 41% of those surveyed said they currently have a formal employer branding program. Note that this number is considerably higher when we look at companies with more than 1,000 employees. 59% of companies think employer branding and corporate branding are different (it is). And 2 in 3 said their employer branding budget increased or stayed the same compared to last year (only 6% said it got smaller).

These indicators combined point to the rise of employer branding as an area of specialization. Yet, a lower number, 17%, reported having any employees at their company with “employer branding” in their job title. With more formalization and budget going into this area, though, we expect to see a sharp rise in employer branding-specific job titles and openings in 2017.

3. Brain Freeze: Creating Employer Branding Content Is a Major Challenge

When asked about their top challenges, the most frequently marked option by recruiters and marketers – about 50% of our group – was “creating employer branding content.”  This makes sense because creating compelling, engaging, shareable content is no easy feat. It’s a challenge not exclusive to recruiting — marketing teams struggle to consistently generate blog posts, infographics, videos, podcasts, and such that people want to read, too. 

4. Gimme Upgrade: Don’t Discount the Impact of Candidate Experience on Employer Branding

Throughout the entire candidate experience from sourcing to onboarding, there are plenty of aspects of candidate experience to control and optimize. But the one thing you can’t control? Candidate reviews. It’s the Achilles heel of employer branding. 

That said, we found that 95% of professionals think the quality of their candidate experience impacts their employer brand. No other yes-or-no question we asked received such an overwhelming majority.

5. Talk Dirty To Me: Social Media Is the Likely Entry Point Into Employer Branding

When asked which employer branding channels they are using, 88% of respondents listed social media. The next closest channel was employer review sites at 55%, followed by content marketing at 46%. This is perhaps a sign of the times, since social media is so deeply a part of our everyday lives. But it’s also worth noting that starting a social media account requires very little effort. Creating an engaging account, on the other hand, is far more challenging.

6. Dolla Dolla Bills, Y’all: Analytics Are Vital to Proving an ROI On Employer Branding

Only 35% of companies were able to establish an ROI on their employer branding efforts in 2015. Among them, there was one thing in common—91% reported using some form of employer branding specifc analytics.

You’ve no doubt heard a variation of the saying “You can’t improve what you don’t measure.” And there’s a good reason for that. Data can guide decisions, and even accelerate performance when it’s effectively measured and analyzed. Without data, you’re playing a dangerous guessing game that can cost you budget and even your reputation in the workplace.

Whether you’ve been executing employer branding campaigns for years or you’re just starting out, analytics are crucial. At the very least, get a grip on your performance by using Google Analytics, Twitter Analytics, LinkedIn Analytics, and others that come at no cost.  

Again, you can get the full eBook here. 

About The Author

Mike Roberts leads digital marketing and demand generation at Jibe, a SaaS startup focused on the recruiting space. He has helped many SaaS companies differentiate themselves in crowded markets. You can connect with him on LinkedIn or on Twitter @mp_roberts.

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I’m of the belief that an ounce of prevention is worth a pound of cure and in this case, recruiting data is prevention for these kinds of stopping points in the world of hiring. We can kill 2 birds with one stone.  If we educate our hiring managers and clients with data so that they have some sense of how rare what they are looking for is, we can help get them to move and better understand our process.  Plus, if we are the only one’s at the table with data, we own the conversation… and that damn table.

I’ve tried very hard to educate my recruiters and sourcers to speak to management with recruiting data because the reality is that it’s the only language everyone at the proverbial table understands. Without data, all management can hear is “wa wha wa wa… 15% wa wah wha waaa 30 days to fill etc,” Charlie Brown style.

Where most struggle, of course, is what data they’re supposed to use.

Cool Story, Bro: Merging BLS To Stop BS

The one data point no one can argue with is scope – it’s a reality of how many people are on the market – and that’s exactly the information I bring to the intake meeting. I lay out the talent pool, then the competitive landscape.

Below is an example of the type of data I will bring to a client and where I get that data from.  In this example I’m working with a pharmaceutical client that is having some challenges filling niche rolls in Madison, Wisconsin.

What I want to do with this information is set the stage with data points that address some of their most common frustrations that quickly become stopping points for us: time to hire and salary.

In this example, I used an aggregator to help me define the talent pool, the BLS to find unemployment information about my target demographic and Glassdoor to give me a baseline for the in-market salary.

From here, I’m able to set expectations about timelines because we’re all on the same page. We both know how few qualified prospects are in the market. I’m able to instill in the hiring managers a sense of urgency when I send them a candidate. I can also provide counter-point data when counter-offers come in far too low. All key information and education to keep a process moving along.

What data do you bring to an intake meeting to move the process along? 

About The Author

Mike Wolford has over 10 years of recruiting experience in staffing agency, contract and in house corporate environments. He has worked with such companies as Allstate, Capital One, and National Public Radio. Mike also published a book titled “Becoming the Silver Bullet: Recruiting Strategies for connecting with Top Talent” and “How to Find and Land your Dream Job: Insider tips from a Recruiter” he also founded Recruit Tampa and Mike currently serves as the Sourcing Manager at Hudson RPO. An active member of the Recruiting community, Mike has spoken publicly in an effort to help elevate the level of professional skill. Follow Mike on Twitter @Mike1178 or connect with him on LinkedIn.

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So often we talk about recruiting as marketing or metaphorically compare it with dating but when I see that image of process and synchronization, I really wish our best practices lent themselves to a manufacturing metaphor. See, manufacturing is all about operationalizing and optimizing to make a better product in the future. Their most core philosophies revolve around making something work more efficiently based on measured inputs and outputs. They look at every widget and cycle to assure timeliness and production efficiencies. It’s a far cry from many of our own models, if we’re being honest.

Often times when it comes to recruiting, the over-stated, under utilized philosophy goes something like this: understanding the success of how we hire means measuring our progress every step of the way, much like an operations expert might look at a machine. Most recruiters (and recruiting listicles) can agree that measurement is the key to making better decisions down the line and ultimately hiring exceptional candidates.

With that philosophy in mind, we’ve adopted a standard of metrics that we continually leverage to make our hiring processes more intelligent and our businesses more successful: cost-per-hire; time-to-hire; number of applicants; applicants per position; the list goes on. No doubt these metrics are effective at keeping our companies streamlined, our budgets under control, and our teams fully staffed — but there’s a change brewing in the industry that might surprise you.

Diagnosing Culture: The New Big Data

According to Jobvite’s annual Recruiter Nation Report 2016, recruiters now care more about what happens after the hire, instead of before. While traditional metrics do still matter, recruiters know they’re successful today when talent ramps quickly and performs at high levels. Almost 40 percent of recruiters rate performance of a new hire as the most important metric for evaluating their success, followed by almost 25 percent who report retention rate is most essential. From there, it drops all the way down to just 13 percent of recruiters who state that time-to-hire is most important, while 11 percent answer cost-per-hire.

And it makes sense right? This is a metric we should have been focused on all along. It’s the most rational measure of recruiting success and the importance of fast ramp times and higher performance is only compounded by an alarming trend in decreasing employee tenures. With millennials switching jobs every 3-4 years and a shortage of STEM workers, recruiters must constantly compete to hold onto the best quality talent. It’s simple math – with these short tenures, you’re going to turn 25-35% of your millennial workforce every year. Yes, you read that right – minimum 30% turnover year over year, coming soon to a hiring department near you.

The why here is pretty simple. To keep up with the Joneses (in this case, other recruiters), it’s time to reevaluate how we measure success. The how, though, is where it gets a bit tougher. Guaranteeing that a candidate is going to be a perfect fit and immediately produce results has never been possible — and it’s an equation recruiters have always tried to solve. Based on our data and experiences, we believe there are three areas where companies can start to measure and hire better people.

Who Do You Serve? Variables for Employee Success

We know “culture” is a huge part of the hiring decision. In fact, it’s now one of the top concerns for both job seekers and recruiters when it comes to finding a good fit. According to the same Recruiter Nation Report, 60 percent of recruiters rate culture fit of high importance when making a hiring decision — topped only by previous job experience (67 percent) —  trumping other factors like cover letters (26 percent), prestige of college (21 percent), and GPA (19 percent). Moreover, 51 percent of recruiters plan to increase efforts in branding their employee culture in the coming year.

Where many companies struggle on culture is establishing what culture even means to your organization without creating some mirror image of Zappos or some other big brand that conferences point to as a case study; a true, authentic definition that accurately captures your team’s motivations and approach to problems.

Look around – do you value team bonding, and host game nights every Friday? Are you into furthering employee education, therefore helping your team to invest in learning new skills? Perhaps collaboration is key and is supplemented by an open floor plan and a flat structure. Whatever it may be, knowing in the first step. You have to find what makes your company most unique and use that as a primary value proposition in the hiring equation from day 1. Be honest. Persuading people with an inauthentic employer brand is the same as not know what yours is at all, just with a prettier cover story and will drive your turnover rate even higher.

Culture feeds the two areas where we believe recruiting can make the largest impact on hiring.

Focus on the Onboarding Process

Let’s not forget that how employees enter your company will set the tone for the rest of their stay. Unfortunately, most recruiters aren’t spending enough time onboarding employees. Only 27 percent of hiring professionals have a dedicated onboarding solution, while 41 percent just use spreadsheets and email. And 42 percent of them spend 8 or fewer hours training new employees.

We’ve all been there – it’s a scary thing walking into a completely new company on the first day. Building and communicating a strong (and accurate) employment brand can go a long way to take the mystery out of “the first day.” And once they’re at their desk, you need a simple and collaborative process for getting employees ramped quickly because the reality is that the top performers aren’t going to stay as long as you’d like. So every day counts. According to a blog from O.C. Tanner, 69 percent of employees are more likely to stay with a company for three years if they experienced great onboarding.

Invest in Employees Long Term

The last piece of the puzzle is a seemingly obvious one — but one that is worth repeating. If you want an employee to feel compelled to stay with your company for the long term and perform at a high level, make sure you show them how much you believe in their potential. No employee wants to stay at a company where they can’t visualize their future, so it’s essential to invest in them for the long term.

What does that look like? Whether it’s professional development, salary negotiation, education opportunities, or simply support via mentorship, investing in employees just means helping them live up to their full potential in your company and ensuring them that you’re always thinking of their future. However it makes sense for you, the key to this is simply being genuine and caring about your employees. A little bit of investment goes a long way, I can promise you — so long, in fact, that you might have some staying for longer than four years.

About The Author

Matt Singer is Jobvite’s fearless marketing leader. He’s officially been in marketing and sales for the past 15 years, but informally for 30+ years starting with cookie, lemonade, and lawn mowing businesses in his neighborhood at the age of 8.

Outside of work, Matt is a proud husband, father, and “manphibian.” He tries to spend as much time as possible in the water abalone diving, fishing, and surfing.

A self-proclaimed data geek, Matt has spent his career channeling that data obsession into building great brands and scalable marketing machines. His career in B2B has focused primarily on the world of HR software, but recruiting is his biggest professional passion.

Follow him on Twitter @matthewdsinger or connect with him on LinkedIn.

The post Rational Recruiting: Big Data Myths Debunked appeared first on RecruitingDaily.

Take the election season, for example. As a data scientist, I realize that who we choose in the first place is the consequence of a thousand different inputs from media, relationships and demographics. We’re reminded of the power in our decision simply by logging into our social media channels every day. Despite common sense and political courtesy, you’ll find a lot of people trying to convince each other of one thing or another but even more around election time. I’m not sure why people insist on talking about their views on policy and ideology, but nevertheless it’s prime season for political wars.

Media conglomerates and candidates with too much money are even creating quizzes that help you see how your beliefs and values align with different politicians and their voting records. They make a match based simply on your answers to questions. While there are some obvious indicators for one party or another, actually deciding the right decision based on outcome isn’t so easy. Even with all of those dots of data on certain policies, the bottom line is that we make our decisions based on how we feel about the person, not based on the metrics and inputs. Just like we do about people we hire.

Voter Fraud: Trusting The Unpredictable

The unpredictability of making the right choice in hiring and in elections is a data point too. When we’re uncertain of the outcome, we incrementally become less trusting of the system as a whole. When we can’t justify why a series of points add up to a concept we disagree with, we inevitably point to the unpredictable. The outliers.

But the numbers don’t lie. They can’t. Numbers are inputs for an output and the people creating the formula are the ones that maneuver and twist the information to make the outcome say what they want. However, people are highly unpredictable and “always” isn’t safe territory, even for numbers. Sure, if you’re predicting weather patterns and likelihood of winning the lottery – the numbers are pretty straightforward. Not so much for people.

That’s probably why teams show such disdain for the metrics behind people because they always have an outlier, someone they can point to that doesn’t fit the mold, that’s not fitting into the equation. And when it comes to a data point, it’s easy to cut. When it comes to people? Not so much.

There’s so much more here than a decimal and a digit – it’s a life, a personality, a relationship. Even though hiring with an algorithm has shown promising results for increasing the diversity and quality of your candidate pool while simultaneously decreasing your hiring costs, like any new recruitment technique, it’s been met with skepticism. And any potential benefits of hiring with an algorithm are going to be moot if you can’t convince people of its value in the first place.

Voter Registration: Getting Buy-In On A Big Bet

Hell, we still haven’t convinced everyone in hiring that they need to be measuring anything so it’s probably safe to assume it will take more than proof to build trust in the value of data driven decisions. People just don’t trust the numbers – almost as much as they don’t trust most politicians. In my experience, it takes a little more than a simple post or persistence. Here are 4 ways I’ve used to get buy-­in for hiring with an algorithm from a reluctant team.

1. Use statistics to get buy-­in for hiring with data

For the analytical, facts­ and ­figures members of your team, hit them with the stats. Research confirms that this works. Presenting information in a graph decreases people’s initial resistance. Here’s a good stat to convince them: a survey of 100 recruiters found that 91% of them said adopting technology has made their jobs easier by reducing their cost­ per ­hire. Hiring with an algorithm is basically adopting technology to reduce cost ­of­ hire and improve quality­ of ­hire.

Here are some more stats for you: An analysis of more than 300,000 hires found that employees who were hired using an algorithm based on a test that assessed personality, intelligence, and job fit stayed on the job approximately 8% longer ­ and performed just as well as ­ employees hired by human recruiters.

2. Use storytelling to get buy-­in for hiring with an algorithm

A statistic (ironically) that often gets quoted is, “After a presentation, 63% of attendees remember stories. Only 5% remember statistics.” So for the team members unconvinced by the numbers, tell a compelling story about hiring with an algorithm. Make your storytelling even more persuasive by using social proof: it’s easier to convince someone to change their mind when you provide an example of how someone similar to them benefited.

For example, SAP started using an algorithm to recruit sales people, which saved their recruiters hundreds of hours in manual resume pre­screening and saved them more than $370,000 in annual costs. If SAP doesn’t resonate with your team members, find a testimonial or case study that does.

3. Start with a small test to get buy­-in for hiring with an algorithm

Some members on your team might be particularly risk­ averse. Or maybe they’re skeptical that hiring with an algorithm is just the latest fad. So start with a small test . This makes it both relatively low ­risk and low ­investment. For example, perhaps you test by adopting the technology for a single role during a specific time frame. It’s ok to insist that partners offer time for testing before you put strain on your HR or IT departments by implementing a large scale tool that doesn’t give the outputs you need.

Your team can assess the quality of the candidates, the time taken to hire, and other recruiting metrics using an algorithm versus more traditional methods of hiring after, for example, 45 days. Once you can quantify and demonstrate the ROI of hiring with an algorithm, even the most skeptical member should be won over.

4. Talk to your team to get buy-­in for hiring with an algorithm

Probably seems pretty straight-forward but a lot of Type A, data people forget to just talk to the team. Sometimes your team is resistant to change simply because it implies that what we’re currently doing is wrong, not because what you’re doing isn’t right. Listen to your team’s concerns and objections. What is it that they’re worried about with using an algorithm to hire? When you’re able to communicate that you understand the problem your team member is trying to solve, he or she becomes open to hearing your ideas for a solution.

Here’s a case. Maybe the biggest pain point for your team member’s job is time wasted pre­screening unqualified candidates. Replacing this inefficiency with an algorithm should be welcomed with open arms. Or maybe their top priority is increasing the diversity of the candidate pipeline. A study of more than 150 companies found that companies that hired using an algorithm based on a personality assessment had more diverse workforces. Once you figure out what problem your team member is dealing with, you can tailor your messaging on how hiring with an algorithm fits their needs.

You Choose: The Bottom Line

Data can’t work without the right inputs and buy-in from people who believe in the cause. No matter how convincing you think you are, ego leads people to think they’re always right.

It doesn’t help that traditionally, hiring has been considered an art involving human judgment. When you ask someone to remove their idea from the process, we also have to consider the conversation and justify why we’re making the change.

The bottom line is that hiring with an algorithm is more of a science that has the potential to increase the quality of your candidate pipeline while decreasing hiring costs. If that doesn’t hook a recruiting team, I don’t know what will. But I’d say it starts with a better conversation.

Ji ­-A Min is the Head Data Scientist at Ideal, a talent marketplace for sales professionals that helps recruiters automate candidate sourcing, pre­screening, and shortlisting. Using her Master’s in Industrial­Organizational Psychology, she conducts research on how to best source, recruit, and hire salespeople. You can connect with her on LinkedIn here.

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