Today’s post comes to us from advisory board member David Creelman, chief executive officer of Creelman Research.

People analytics has hit a lull, somehow not quite living up to the early expectations. University of Southern California professor Alec Levenson has an explanation and, perhaps, a bit of a cure. Levenson says analytics is mainly about analysis, not statistics.

Let’s start with the “statistics” part of that statement. A lot of the hype around people analytics came from the promise that the combination of big data and advanced mathematical techniques would allow us to do predictive analytics. The problem with that promise is that it’s quite rare for HR to have large, high-quality data sets that are relevant to the issue it’s addressing. If a people analytics department is built around the idea that its value will come from predictive analytics, then it is bound to disappoint stakeholders. An undue focus on statistical expertise can send people analytics in the wrong direction.

Analysis, on the other hand, simply involves thinking very hard about a problem. For example, if managers are complaining about a lack of agility, then it takes a lot of thinking to determine where exactly this “agility” matters to the business and which of many possible barriers is hurting agility. Levenson in particular thinks we should spend a lot more time analyzing — that is, thinking about — which issues have a crucial strategic impact because there are hundreds or thousands of things you could potentially improve in the organization, and you can only focus your attention on a few.

Once you have thought through a problem, then you’ll be able to gather up relevant data to help inform your decision. More often than not, the mathematics of the analysis is pretty simple. Managers need answers to questions such as how many, is it getting worse, and which is biggest and by how much? You need data to answer those questions, but it is rare that you need deep expertise in statistics.

The main barrier to doing analysis is not HR’s skill set but the time HR has for thinking. Like most people in modern organizations, an HR professional’s time is consumed with doing things. They are in meetings, answering emails, reading reports, or filling in forms. What they don’t have much time to do is sit and think — which is what analysis requires. For a people analytics function to be successful, it needs to create an environment where the analytics pro has time to work with stakeholders to analyze problems.

A big part of that analysis involves patience. The goal can’t be to get to a solution as quickly as possible because you’ll probably come up with a solution to the wrong problem. It takes patience to ask over and over again, who cares about this, why is this important to them, and is this even the most important issue we should focus on?

We don’t want to forget about the value of good data sets and well-deployed mathematical tools. However, the future of people analytics lies in professionals who have the time and patience to do analysis, not statistics.

Today's guest post comes to us from Teresa Smith, senior manager of UKG's HCM Strategic Advisory group and was written in collaboration with Chas Fields, senior partner of UKG's HCM Advisory Group.

Many HR leaders and practitioners get inundated with terms like strategic dataartificial intelligencepeople analytics, and many others. It can be frustrating when we're told these areas should be our focus without any sense of how that will happen, or where all these activities will fit in our day-to-day.

Think of a time where you've been instructed to make an organizational or strategic change to your people processes – you pull the data and it looks like a foreign language, leaving you feeling a bit like Keanu Reeves when he gets told for the first time what "the Matrix" is. 

So how do we take our HR data, determine what's achievable, and put the right people strategies into action? Here are 3 focus areas that will help you take action:

1. Expectation vs. Reality

Leaders everywhere need access to people data to help drive organizational and departmental success. The problem is that they don't always have all the data needed when making decisions about the organization. This can be frustrating for executives, especially if data is missing, doesn't add value to their overall strategy, or doesn't provide a complete picture of key business areas. Executives expect to be able to gain insight easily and quickly into the entire organization, so they can address the needs of the business and reach their strategic goals.

When companies find themselves with a disconnect between what's expected out of data and what's available, HR professionals can help bridge the gap. A data strategy can help you identify how your people information is collected, stored, managed, and shared. These tips can help lay the foundation for your HR and payroll data strategy:

As strategic partners in the organization, HR professionals need to drive technology initiatives that will transform the way executives look at data.

2. Looking vs. Seeing

Providing executives with data across multiple reports or in a single report is only part of gathering information. While reporting is a good first line of defense when it comes to measuring, monitoring, and alerting you to what's happening in the business, it is just the tip of the iceberg. HR needs a path to action from that data for it to be effective and deliver deeper meaning to the organization.

Analytics can answer questions that come to light from reporting, interpret information at a deeper level, and provide recommendations on actions. People analytics is crucial for executives to gain a clear view of their wider business data, proactively analyze trends that are happening with their people, and ensure they are capable of achieving their goals. Delivering the right data through real-time analysis of employee activity and automating that data's delivery helps organizations plan and reach their strategic initiatives.

However, people analytics can't exist in a vacuum. HR needs it to be integrated into day-to-day processes and displayed in the same place they manage most of their activities so they can intuitively move between seeing data and doing something about it.

3. Tasks vs. Actions

While people analytics is a great way to monitor and provide insight into day-to-day activities, it shouldn't be its own item on your to-do list. Analytics need to be rolled into your priorities and goals to make effective decisions on behalf of the organization. When you're tackling recruiting, benefits, retention, operations, and other focus areas you should be using data to inform your decisions in those moments. This way it's not a burden, it's just part of your normal process. Your system should proactively serve up the data you need in those contexts.

Conclusion: Small practical steps make people analytics more effective

These steps will take you some time. As you navigate your data, when you question its validity or output, have discussions with your people managers and those making day-to-day decisions to help you fully grasp where progress or improvements need to be made. When taking action, ask yourself ”œwho will this impact and will it drive the organization forward?”  If the answer is yes, celebrate the success.  From there, monitor your decisions on a regular monthly or quarterly basis to ensure you stay on top of the trends to allow you to remain agile.

Find an expanded version of this article on the UKG What Works blog here.

Today's post comes to us from Workforce Institute board member David Creelman.

Take a look at this people analytics data prepared by Revelio Labs. It looks at turnover (churn) vs. revenue growth. There is something very odd about Costco at the bottom right.

© Revelio Labs (2021)

One thing that is different at Costco relative to their competitors is a high ratio of junior to senior employees, i.e. a flat organization.

© Revelio Labs (2021)

If you want to read more of Revelio Lab's piece on Costco click here: https://www.reveliolabs.com/news/business/what-makes-costco-a-great-place-to-work

However, I wanted to focus this blog on a larger take-away than just what's happening with Costco.

What we're seeing here are HR analytics that will be really intriguing to business leaders. The ability to use advanced technologies to gather and analyze data from a wide variety of public sources such as Indeed and LinkedIn to get a clear picture of an organization's talent strategy and its impact on meaningful measures is a huge leap forward.

Historically, HR analytics work has been myopically focused on the data we can pull out of our own HRIS systems. This new work from Revelio Labs points to a new and much bolder direction for HR analytics. We can now do all kinds of analysis that compares our organizations to competitors. We can look at which departments competitors are investing in compared to which ones we are investing in. We can look at where they are getting talent and who is taking our talent. We can look at estimates of diversity data even if our competitors don't report it.

The sheer volume of public data that is out there in the public sphere is astounding and I'm very excited to see what Revelio Labs and others continue to do to find value in that data.

What sources of data are your company looking at and measuring these days? What do you think are the most meaningful metrics in HR analytics? Share your thoughts in the Comments section.

Today's post comes to us from Workforce Institute board member and HR Bartender Sharlyn Lauby. Here she shares how to use the data you're collecting to make actionable decisions.

A couple of months ago, I wrote a post about digital transformation and why it's important to business. Digital transformation is about organizations getting answers through the strategic use of technology.

But once organizations get answers, they have to do something with the information. I've always said that one of the worst things that organizations can do is ask employees for their opinions and then do nothing with it. The same philosophy applies. It doesn't make any sense to collect a bunch of data and then do nothing with it.

The key is making data actionable. The question becomes how to do that. I wish I could say it's easy but it's not. Organizations can certainly get off track thinking that collecting the data or reporting the data was enough. Here are five steps that organizations can take to make sure that they put their data to good use.

  1. Agree on what to measure. And how to measure it. The first step in making data actionable is having everyone believe the data. No one is going to react to data that they are skeptical about. The organization needs to reach consensus on what data is important, how to measure it, and where to collect it from.
  2. Regularly review the data. Not just when there's a problem. There are two different ways to look at data. We can take a bad situation and make it good. Or we can take a good situation and make it even better. Organizations sometimes miss out on improvements because they only look at data when things aren't going well.
  3. Create a hypothesis. Including what happens if we do nothing. Think of the data analysis and action as part of the scientific method. Organizations want to make a prediction (based on the data) that tells them what will happen if they take certain actions. Let me add that it could be helpful to also make a prediction on what will happen if no action is taken.
  4. Use agile implementation strategies. Agile is used in software development to help project teams stay on track, avoid major setbacks, and better allocate resources. The premise is to take large projects and break them down into smaller more manageable steps. After each step (or milestone), the team can evaluate their progress, and make adjustments as needed.
  5. Hold implementation teams accountable. Finally, if the goal of collecting data is to make a decision - even if that decision is to do nothing - then people need to stand by the decisions they make. The good news is that data is always changing. So new data might prompt a new decision.

Today's technology allows us to collect good business data. We can use that data to make sound business decisions. Organizations should put a protocol in place to ensure that the data they're collecting is put to good use.

Today's post comes to us from Workforce Institute board member John Frehse. John is a data evangelist, generally advocating for more data democracy in the workplace. Here though, he asks whether data drive behavioral change.

We know that smoking is bad for us and can lead to cancer. So, how is it that there are almost 1,000,000,000 (that is 1 billion!) smokers on the planet? Even when we know the truth, it may not change our behavior. This is largely due to prioritization. We weigh, often unconsciously, the trade-off of changing behavior or staying the same based on what is in it for us. Combine an irrational sense of our own strengths and infallibility, and we often do not change when we should.

A study of American college students found that 88% of them thought they were above average drivers (gasp!). Another study found that 73% of all U.S. drivers felt they were above average. These results are telling both about our irrational confidence in ourselves and probably a lack of understanding about the realities around us.

Irrational misperception can be found in many places.

How about Waze? Do you trust or distrust Waze to get you to your destination faster? There are over 100 million active Waze users globally, so someone is taking notice. We are increasingly flooded, not just with data, but information derived from this data and asked to make decisions, for better or worse. My wife says, “never argue with Waze,” but many have discontinued service over a single bad experience. They think they know better than the crowd sourced decision-making tool.

How does this transfer to labor management and workforce management?

We need to hire and retain skilled workers to make sure our companies are successful. But how much are we willing to do to make sure this happens? The answer is, it depends on the company and the culture. Many organizations are using the same shift schedules in their hourly operations that they used 50 years ago and having a hard time attracting talent. In a growing economy where a shortage of skilled workers is the reality, overtime levels have grown rapidly and hourly workers are feeling burnout. Yes, they like the money, but it has destroyed any semblance of balance for them in their personal life.

Change is emotional and potentially disruptive. Even if the data is there, much like the smokers and legions of folks who think they are exceptional drivers, employers aren't seeing enough of the reality of what these outdated shifts are doing to make positive changes. Like the smoker lighting up another cigarette, employers are passively doing the same thing and hoping for a different result. Reality just won't support this behavior over time.

As we are presented with more and more decision-making information, is our behavior really changing or not? Workforce management is an area flooded with data and some companies have acquired the tools to turn that data into useful, actionable information. But what are they doing with it? Is it just too much work to improve labor models and shift schedules? How employers answer that question just may separate the winners from the losers in the long term.

Today's post comes to us from board member, David Creelman. When budget planning season rolls around, will your workforce planning analytics get you your fair share?

Workforce planning is one of the oldest areas of HR analytics. You can find many articles from the 1960's on workforce planning–or as it was known then “manpower planning” (see for example Models and Modelling for Manpower Planning by W.R. Dill et al in Management Science Journal, 1966). The basic principles haven't changed much in the last 50-odd years, what has changed is our access to data and the tools we have to manipulate it.

Despite its maturity, workforce planning analytics can be a frustrating topic to address. No matter how good our analytics tools, we are still making predictions about an uncertain future based on managers' estimates about what the business will need. Also, managers may be unclear about what specifically they want from workforce planning analytics, leaving analytics pros with an unmanageably large task.

Here are four steps that will guide your approach to workforce planning.

Step 1: Get clarity of purpose

The most important step in workforce planning is getting clarity on what management will do with the analysis. For example, if they are interested in making a budget forecast over the next three years then that will require a different kind of detail than if the primary concern is forecasting the technical skills needed over to coincide with a planned product launch.

Managers may lean towards thinking that analysts can do a single forecast that covers every possible use of workforce planning but that's unrealistic.

When you discuss their needs, make sure you cover these key factors:

Step 2: The organization's demand for talent

One half of workforce planning analytics is determining the demand for talent. Typically, this information comes from both:

This will have to be reconciled–and that reconciliation is more political than technical. The analytics pro should avoid getting caught in the middle.

You'll need to have information about all the clarity of purpose factors to know how detailed the forecasted demand for talent needs to be.

In all cases, we are dealing with a combination of hope and guesswork. It often makes sense to run several scenarios to reflect that uncertainty.

Step 3: The organization's supply of talent

Analytics pros are usually comfortable studying the supply side of talent. Here they can model turnover, retirements, lateral transfers, and promotions to predict where the current workforce is likely to be in various time periods.

Forecasting talent supply can be done in a rough way on the back of an envelope or by using sophisticated models informed by past data. Choose a level of analysis that fits the need and remember that the accuracy of the forecast supply of talent should be aligned with the accuracy of the forecast demand for talent.

Step 4: Filling the gaps

It's self-evident that once we've forecast supply and demand we can determine the gaps. Once we have the gaps, we can take action to address them.

Determining how to fill the gaps opens many interesting and important opportunities for analysis:

This kind of analysis draws on the creativity and insight of the workforce planning analyst. It's here where we move from seeking to understand the future to recommending actions that will take the organization into the future. If this step is not executed well, then the earlier work will be in vain.

Workforce planning is a complex process and there are many decisions that need to be made before you start the analysis so that it produces accurate and actionable recommendations at the end.  The good news is that if you have the right tools and clean data then the analyst will be able to keep their eye on this end result, rather than be overwhelmed by the manual work of simply getting the analysis done on time. We could be entering a golden age of workforce planning, but only if we don't underestimate what it takes to point the analysts in the right direction.

This post by Sharlyn Lauby, the HR Bartender and a member of the Workforce Institute board of advisors, is one of the most popular we've ever published.  Sharlyn writes about how the scientific method of investigation can be applied to solving problems in a business environment.  This topic is near and dear to my heart as I was a scientist and science teacher early in my career.  It's ironic that the demand for data scientists has never been higher, even as the very definition of the word "fact" is under fire.  Sharlyn is right on in her analysis about how the scientific method can help non-scientists to find the right solutions by basing human resources decisions on measurable evidence.

Companies face challenges on a regular basis. As such, employees need to know how to problem solve. A tried and true problem-solving process is the scientific method. I know many of us haven't thought about the scientific method since our school days but it does provide a logical way of tackling business problems. As a reminder, here are the steps to the method:

1.  Identify the problem. The first step in the scientific method is to identify and analyze a problem. Data regarding the problem can be collected using a variety of methods. One way we're all accustomed to is the classic: who, what, where, when, how, and to what extent? The scientific method works best when you have a problem that can be measured or quantified in some way.

2. Form a hypothesis. A hypothesis is a statement that provides an educated prediction or proposed solution. A good format for a hypothesis would be, “If we do XX, then YY will happen.” Remember, the hypothesis should be measurable so it can help you solve the business problem identified in step one.

3. Test the hypothesis by conducting an experiment. This is when an activity is created to confirm (or not confirm) the hypothesis. There have been entire books written about conducting experiments. We won't be going into that kind of depth today but it's important to keep in mind a few things when conducting your experiment:

4. Analyze the data. Once the experiment is complete, the results can be analyzed. The results should either confirm the hypothesis as true or false. If by chance, the results aren't confirmed, this doesn't mean the experiment was a failure. In fact, it might give you additional insight to form a new hypothesis. It reminds me of the famous Thomas Edison quote, “I have not failed. I've just found 10,000 ways that won't work.”

5. Communicate the results. Whatever the result, the outcomes from the experiment should be communicated to the organization. This will help stakeholders understand which challenges have been resolved and which need further investigation. It will create buy-in for future experiments. Stakeholders might also be in a position to help develop a more focused hypothesis.

Now let's use the scientific method in a business example:

Step 1 (identification): Human resources has noticed an increase in resignations over the past six months. Operational managers have said that the company isn't paying employees enough. The company needs to figure out why employees are resigning?

Step 2 (hypothesis): If we increase employee pay, then fewer resignations will occur.

Step 3 (test): For the next three months, HR will have a third-party conduct exit interviews to determine the reason employees are resigning.

Step 4 (analysis): The third-party report shows that the primary reason employees are leaving is because health care premiums have increased and coverage has decreased. Employees have found new jobs with better benefits.

Step 5 (communication): After communicating the results, the company is examining their budget to determine if they should:

  1. Increase employee pay to cover the health insurance premium expense or
  2. Re-evaluate their health care benefits package.

I've found using the scientific method to be very helpful in situations like the example where a person or small group have a theory about how to solve a problem. But that theory hasn't completely been bought into by everyone. Offering the option to test the proposed solution, without a full commitment, tells the group that their suggestion is being heard and that the numbers will ultimately provide insight - after the full scientific method has been followed.

How do you use the scientific method to come up with effective solutions? Share your experience in the comments.

Photo by Elevate on Unsplash

Today we hosted a tweet chat to discuss the implications of our 2018 predictions.  We were joined by a number of our board members, as well as guest tweeters with an interest in workplace issues.  Following are the questions we posed to our participants.  You can see the full transcript of our conversation below.

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