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When it Comes to Big Data for HR, Don’t Forget -
          the Basic Concepts Still Apply

 The 3 most important concepts to keep in mind

by David Bernstein on April 15, 2013

Readers of my blogs and articles know that my
focus has often been on explaining Big Data. This
Big Data phenomenon is quickly spreading
throughout the business world. Those that
understand it well will be able to garner new
insights that will give them a competitive
advantage in their business activities. This is why I’ve focused on demystifying it and talking
about how HR can start adopting this new technology to drive business results. For this post,
though, I want to change focus a bit. Instead, I want to focus on a related and equally
important idea about utilizing Big Data – developing and growing your role as a “Data Leader.”

I’ve been asking myself, “What are the most important concepts HR professionals should
always keep in mind as they embrace their utilization of Big Data into their portfolio of data
tools?” The answer to that question is quite clear. “Don’t let the excitement about Big Data
cloud your judgment.” Big or small, the same rules of data analysis still apply. And, being able
to demonstrate your data intelligence is just as critical a skill as is knowing when and how to
bring data into the decision making process. With this in mind, based on my years of
experience, here are the 3 most important concepts to always keep in mind:

1. Know the difference between “Story-telling” and “Story-selling”
As both a producer and consumer of data analysis, it is paramount that HR professionals be
able to differentiate between a neutral, unbiased interpretation of data and one that uses the
data to tell a desired story. Darryl Huff’s famous work, “How to Lie with Statistics” is full of
timeless cautions on this topic. He reminds us that despite its mathematical base, statistics is
as much an art as it is a science.

Today, the HR marketplace is replete with vendors and services all portraying themselves as the
answer to every business problem and need. With the ever increasing messaging on the need
for HR to be “evidence-based” in their decision making, it is now nearly impossible to find a
vendor or service provider who isn’t trying to use data to base their claim that their solution is
what is needed. Let me offer a simple rule to keep in mind – “Even if you can’t see any
demonstrable bias in the data, always allow yourself a reasonable sense of skepticism;
especially when the source of the data being provided is from someone who has a point to
prove or a product to sell.

2. Know the difference between “Correlation” and “Causation”
Correlation does not imply causation. This is one of the most basic tenets of statistical
analysis. The idea is straight-forward – “just because there is a connection between two
variables, does not necessarily mean that one causes the other.” This is very important in the
world of Human Resources. HR has long pursued being able to tie its activities to positive
business results. However, that pursuit will be fruitless until HR can understand which variables
have that type of impact and can then subsequently statistically demonstrate the causal
connection between HR activity and profitability.

Interestingly enough, though, HR can also capitalize on simply knowing there is a correlation
between data points. The trick is to know when correlation alone will suffice. Again, simple
rules govern when correlation is sufficient. When the intended outcome is either:

1) Preventive – i.e. reduce attrition by finding patterns in your voluntary termination
population
or
2) Increases the likelihood of a positive event – i.e. increasing the completion rate of online
applications to your job postings

When it is not necessary to know why a relationship exists and you only need to be concerned
with the fact that a relationship does exist, then you can leverage that insight to drive desired
business outcomes.

3. Know when your sample size is sufficient
This one is actually embedded in the two previously outlined concepts. However, because of its
critical importance to this discussion, I am calling it out on its own. The right sample size is an
important feature when the goal is to use data to make decisions, evaluate results, or to
determine either correlation or causation. Sample size is critical, both in terms of the size of a
population or data-set, as well as to how the sample size is determined or created. As noted,
marketing collateral’s purpose is to story-sell. The data-savvy consumer of this type of
information should always be on the alert for data that is purposely cherry-picked to convince
the buyer of the vendor’s stated value proposition.

Not coincidentally, HR and their data analytic teams must abide by this same concept. When
data will be used to support decision making, to create policy, to make recommendations, or to
purchase products, they must present an objective analysis that is based on a statistically
significant sample size.

I felt compelled to write this article largely because I’m noticing an increase in the amount data
and analytics oriented marketing messages being presented to the HR community. I am
concerned about the HR professional who is not yet accustomed to examining the claims made
by others that are “supported” by their own reported data. It is important that HR
professionals not blindly accept information at face-value simply because it is presented as fact
supported by graphs and charts. With so many “Big Data” and analytic oriented solutions now
coming to market, HR must know how to take a critical eye in evaluating these claims.
This same critical eye is also what HR must bring to their own data analytic work. Objective
storytelling based on adequate sample sizes, with appropriate conclusions as to correlation or
causation are the core traits of a seasoned data veteran.



I remember back to the start of my career in Human Resources. There I was, a Generalist in an
explosive growth company called PeopleSoft. Back then, Dr. Lyle Spencer’s 1995 book on
reengineering HR was like the bible to us. Dr. Spencer’s definition of a strategic HR function
was one that was able to switch its focus from spending 60% of its time on Administration work
to instead focusing 60% of its time on planning and the development of HR programs. Today,
many HR departments have moved well past that milestone. The stature the HR profession has
achieved is clearly evidenced by the fact that 79% of CEO’s have their senior HR leader
reporting directly to them – primarily to play a key role in defining the direction and overseeing
the performance of the company. HR has graduated from its role as a “data steward” to that of
a business leader. HR now must regularly evaluate data and information as it guides and leads
the businesses it operates in. And because of Big Data’s statistical analysis underpinnings, it is
even more critical that HR take its data-savviness to entirely new heights.

Phil Simon, in his book, “too BIG to IGNORE – the business case for big data” talks about the
popular perception of HR folks tending to almost exclusively rely on gut instincts versus making
decisions based on data. I’m convinced that as the profession continues to embrace the use of
data and analytics into its practices, that it will also continue to solidify its role within the C-
Suite.




David Bernstein


David Bernstein is the head of eQuest's Big Data for HR/Predictive Analytics Division. This division
studies the performance and effectiveness of job postings on job boards and social media sites, enabling
organizations to make better-informed decisions about their recruiting and hiring strategy. Bernstein's
focus is on yielding critical insights for HR departments to drive talent acquisition and workforce
planning strategies. Contact him at david.bernstein@equest.com.

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Big Data Analysis - The 3 Most Important Concepts to Keep in Mind

  • 1. When it Comes to Big Data for HR, Don’t Forget - the Basic Concepts Still Apply The 3 most important concepts to keep in mind by David Bernstein on April 15, 2013 Readers of my blogs and articles know that my focus has often been on explaining Big Data. This Big Data phenomenon is quickly spreading throughout the business world. Those that understand it well will be able to garner new insights that will give them a competitive advantage in their business activities. This is why I’ve focused on demystifying it and talking about how HR can start adopting this new technology to drive business results. For this post, though, I want to change focus a bit. Instead, I want to focus on a related and equally important idea about utilizing Big Data – developing and growing your role as a “Data Leader.” I’ve been asking myself, “What are the most important concepts HR professionals should always keep in mind as they embrace their utilization of Big Data into their portfolio of data tools?” The answer to that question is quite clear. “Don’t let the excitement about Big Data cloud your judgment.” Big or small, the same rules of data analysis still apply. And, being able to demonstrate your data intelligence is just as critical a skill as is knowing when and how to bring data into the decision making process. With this in mind, based on my years of experience, here are the 3 most important concepts to always keep in mind: 1. Know the difference between “Story-telling” and “Story-selling” As both a producer and consumer of data analysis, it is paramount that HR professionals be able to differentiate between a neutral, unbiased interpretation of data and one that uses the data to tell a desired story. Darryl Huff’s famous work, “How to Lie with Statistics” is full of timeless cautions on this topic. He reminds us that despite its mathematical base, statistics is as much an art as it is a science. Today, the HR marketplace is replete with vendors and services all portraying themselves as the answer to every business problem and need. With the ever increasing messaging on the need for HR to be “evidence-based” in their decision making, it is now nearly impossible to find a vendor or service provider who isn’t trying to use data to base their claim that their solution is what is needed. Let me offer a simple rule to keep in mind – “Even if you can’t see any demonstrable bias in the data, always allow yourself a reasonable sense of skepticism; especially when the source of the data being provided is from someone who has a point to prove or a product to sell. 2. Know the difference between “Correlation” and “Causation” Correlation does not imply causation. This is one of the most basic tenets of statistical
  • 2. analysis. The idea is straight-forward – “just because there is a connection between two variables, does not necessarily mean that one causes the other.” This is very important in the world of Human Resources. HR has long pursued being able to tie its activities to positive business results. However, that pursuit will be fruitless until HR can understand which variables have that type of impact and can then subsequently statistically demonstrate the causal connection between HR activity and profitability. Interestingly enough, though, HR can also capitalize on simply knowing there is a correlation between data points. The trick is to know when correlation alone will suffice. Again, simple rules govern when correlation is sufficient. When the intended outcome is either: 1) Preventive – i.e. reduce attrition by finding patterns in your voluntary termination population or 2) Increases the likelihood of a positive event – i.e. increasing the completion rate of online applications to your job postings When it is not necessary to know why a relationship exists and you only need to be concerned with the fact that a relationship does exist, then you can leverage that insight to drive desired business outcomes. 3. Know when your sample size is sufficient This one is actually embedded in the two previously outlined concepts. However, because of its critical importance to this discussion, I am calling it out on its own. The right sample size is an important feature when the goal is to use data to make decisions, evaluate results, or to determine either correlation or causation. Sample size is critical, both in terms of the size of a population or data-set, as well as to how the sample size is determined or created. As noted, marketing collateral’s purpose is to story-sell. The data-savvy consumer of this type of information should always be on the alert for data that is purposely cherry-picked to convince the buyer of the vendor’s stated value proposition. Not coincidentally, HR and their data analytic teams must abide by this same concept. When data will be used to support decision making, to create policy, to make recommendations, or to purchase products, they must present an objective analysis that is based on a statistically significant sample size. I felt compelled to write this article largely because I’m noticing an increase in the amount data and analytics oriented marketing messages being presented to the HR community. I am concerned about the HR professional who is not yet accustomed to examining the claims made by others that are “supported” by their own reported data. It is important that HR professionals not blindly accept information at face-value simply because it is presented as fact supported by graphs and charts. With so many “Big Data” and analytic oriented solutions now coming to market, HR must know how to take a critical eye in evaluating these claims.
  • 3. This same critical eye is also what HR must bring to their own data analytic work. Objective storytelling based on adequate sample sizes, with appropriate conclusions as to correlation or causation are the core traits of a seasoned data veteran. I remember back to the start of my career in Human Resources. There I was, a Generalist in an explosive growth company called PeopleSoft. Back then, Dr. Lyle Spencer’s 1995 book on reengineering HR was like the bible to us. Dr. Spencer’s definition of a strategic HR function was one that was able to switch its focus from spending 60% of its time on Administration work to instead focusing 60% of its time on planning and the development of HR programs. Today, many HR departments have moved well past that milestone. The stature the HR profession has achieved is clearly evidenced by the fact that 79% of CEO’s have their senior HR leader reporting directly to them – primarily to play a key role in defining the direction and overseeing the performance of the company. HR has graduated from its role as a “data steward” to that of a business leader. HR now must regularly evaluate data and information as it guides and leads the businesses it operates in. And because of Big Data’s statistical analysis underpinnings, it is even more critical that HR take its data-savviness to entirely new heights. Phil Simon, in his book, “too BIG to IGNORE – the business case for big data” talks about the popular perception of HR folks tending to almost exclusively rely on gut instincts versus making decisions based on data. I’m convinced that as the profession continues to embrace the use of data and analytics into its practices, that it will also continue to solidify its role within the C- Suite. David Bernstein David Bernstein is the head of eQuest's Big Data for HR/Predictive Analytics Division. This division studies the performance and effectiveness of job postings on job boards and social media sites, enabling organizations to make better-informed decisions about their recruiting and hiring strategy. Bernstein's focus is on yielding critical insights for HR departments to drive talent acquisition and workforce planning strategies. Contact him at david.bernstein@equest.com.