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Wikipedia claims that “Big data is a term applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target currently ranging from a few dozen terabytes to many petabytes of data in a single data set.”
Regardless of definition, the big data concept centers around huge amounts of data that are not only increasing in volume, but also in velocity and variety.
According to MGI and McKinsey’s Business Technology Office, “The amount of data in our world has been exploding and analysing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus…
Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.”
So what is key to keep in mind?
1. “Data have swept into every industry and business function and are now an important factor of production, alongside labor and capital.”
2. Big data “can unlock significant value by making information transparent and usable at much higher frequency.”
3. “As organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance. Leading companies are using data collection and analysis to conduct controlled experiments to make better management decisions; others are using data for basic low-frequency forecasting to high-frequency nowcasting to adjust their business levers just in time.”
4. ” The use of big data will become a key basis of competition and growth for individual firms. From the standpoint of competitiveness and the potential capture of value, all companies need to take big data seriously. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up to real time information. Indeed, we found early examples of such use of data in every sector we examined.”
5. “There will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.
The dig deal about big data is that data can be used to make better decisions.
While McKinsey found that some companies are using data collection and analysis to make better management decisions, I think there is a huge opportunity to collect and analyze human capital data,specifically to make better hiring decisions – to gain a holistic advantage over competitors by finding, identifying, and enabling the recruitment of top talent.
Analytics Based Recruiting
If you’re unfamiliar with Moneyball, the term comes from Moneyball: The Art of Winning an Unfair Game, a book by Michael Lewis about the Oakland Athletics baseball team and its general manager Billy Beane. Its focus is the team’s modernized, analytical, sabermetric approach to assembling a competitive baseball team, despite Oakland’s disadvantaged revenue situation. Simply put, the Oakland A’s didn’t have the money to buy top players, so they had to find another way to be competitive.
The central premise of Moneyball is that the collected wisdom of baseball insiders (including players, managers, coaches, scouts, and the front office) over the past century with regard to player selection is subjective and often flawed.
If you’ve seen the Moneyball trailer (or movie), you will hear one of the quotes that struck a chord with me, which was ”You don’t put a team together with a computer.”
When I first heard that quote when I saw the Moneyball trailer, I immediately thought of all of the people that say things like “recruiting is about people and not about technology” (e.g., sourcing, information retrieval, databases, analytics, etc.). I also thought about all of the great people I’ve hired with a computer by using still “pre-histprical” methods that with the help of some friends and the experiment we are working on, I am about to leave behind, because using disconnected sources of information is simply not going to be as meaningful anymore as it was once given that we now have the ability to combine data points and get the information that will help us find that needle in the haystack, yes, no matter how in depth or targeted a single source may be.
The use of the right applications and mixed sources od data will soon get us close to the use of predictive analytics in recruitment which should initially be used carefully and with discretion; Many are and will be highly dubious of the ability to use text and data to predict who might be a good hire for any particular hiring need.
But how to expect a different response? Much of what is accepted as sourcing, recruiting and hiring best practices today is largely based upon conventional wisdom – ideas or explanations that are generally accepted as true.
However, the problem with any conventional wisdom is though the ideas or explanations are widely held, they are also largely unexamined and untested, and thus not necessarily true.
Conventional wisdom can be a significant obstacle to the acceptance of new information or the introduction of new ideas, theories and explanations, in many cases due to the fact that conventional wisdom is often made of ideas that are convenient, appealing and deeply assumed. At some point, however, these assumptions and beliefs can be be violently shaken when they no longer match reality at all.
Some people would call this violent shaking of conventional wisdom disruptive innovation, and I believe it is coming to talent acquisition in the form of Moneyball recruiting.
What Could Big Data Recruiting Look Like?
Is there an equivalent to Moneyball in recruiting – in challenging conventional HR and recruiting wisdom and identifying and hiring top talent through the use of data, statistics, empirical evidence and objective facts?
Yes – it just hasn’t been fully developed yet, but it is on the making.
Here are just a few ways we could apply the Moneyball concept/analogy to talent acquisition:
1. Moving away from using largely subjective means of assessing talent and making hiring decisions to more objective, fact and empirical data-based means
2. Identifying and acquiring top talent looking for traits, experience, accomplishments and information overlooked by traditional recruiting and assessment methods
3. Challenging conventional wisdom as to what top talent looks like and where it comes from (e.g., Ivy league schools, high G.P.A., certifications, M.B.A’s, experience at certain companies, etc.)
4. Developing objective performance measurements that are relevant across any role, responsibility, company, and industry and that stick with each person as they move through their career, similar to a credit score
5. Individual companies developing “secret sauces” for sourcing, analyzing and evaluating potential hires based on their own data and factual statistical analysis of the makeup of their ideal hire and employee
6. Breaking away from the idea that the only way to hire great people is to “buy” and poach them from competitors or specific companies (look at how incestuous Facebook, Google, LinkedIn, Microsoft, Apple and Yahoo are with regard to their talent pool)
I think it would be fascinating to objectively examine the conventional wisdom that referrals are the best hires. I know that might sound like blasphemy to some, as many simply assume referrals are the best hires, but surveys based on people’s subjective opinions of who the best employees are aren’t objective, and they certainly aren’t based on empirical evidence. Besides, referrals may score highly on quality-of-hire metrics based more on a self-fulfilling prophecy than anything else.
It’s one thing to say, think and feel that referrals produce the highest quality of hire, and it’s entirely another to prove it with objective, factual data.
In reality, referrals may in fact simply be the least-worst source of hire. Contemplate that little gem for a bit – I’ll be writing a post on it in the near future.
What about our assumptions on where great people come from?
While we’re challenging assumptions, is a college degree really necessary to be a top performer?
What if you could leverage data to identify the potential in people before they were 18, regardless if they were on a path to college or not?
Is there already a way to leverage data and technology to increase the probability of finding and identifying people who are capable of being the next “A” player and significant contributor to your team?
Yes, I know there is. It already exists, albeit in a very crude form using tools and technologies in ways they were not intended nor designed for.
If only companies would start to focus their business intelligence and predictive analytics horsepower that many already currently use (and spend millions on) for marketing, product development, sentiment analysis, healthcare, etc., and focus it on human capital data to enable better hiring decisions (which always starts with talent identification, aka sourcing, by the way), we would begin to see Moneyball-like disruption develop in the HR and recruiting function.
There is no denying that we are well into the Information Age, which is characterized by the ability of individuals to find and transfer information freely, and to have instant access to knowledge that would have been difficult or impossible to find previously. The digital revolution has already begun and we are seeing a shift from traditional industry that the industrial revolution brought through industrialization, to an economy based on the manipulation of data and information, i.e., an information society.
As such, I agree wholeheartedly with Mike Loukides that “The future belongs to the companies who figure out how to collect and use data successfully.”
However, I’d add that the future belongs more accurately to the companies who figure out how to collect and use human capital data successfully.
That’s because the companies that can consistently hire great people, through identifying people and basing hiring decisions on data and not intuition and conventional wisdom, are more likely to develop the best teams.
And the best teams win.
If you think the idea of leveraging data and statistics to find and hire top talent defies everything we know about human resources and recruiting, I say you’re right.
I also say it’s a good thing, and that we’re just getting started.
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