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 â€” 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 “who 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:

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 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.

Today's post comes to us courtesy of board member John Frehse, senior managing director at Ankura Consulting Group.

Does your company provide employees with access to information in a useful format? You may think the answer to this question is “yes”, but, at the risk of sounding harsh, I’m guessing it’s actually “no”.

Here’s why: Google. Your employees have grown accustomed to having access to any information they may want at any time, from anyplace thanks to our friends at Google. Whether it is through Google Search, Gmail, YouTube, or any other platform they own, Google does one thing better than anyone else: they provide access to information in a breathtakingly simple, fast and useful format.

Need to fix something? Watch a tutorial on YouTube. Need to know how many kilometers are in a mile? Google it (the answer is 1.6, FYI). Need to find the closest gas station that is open 24 hours on your way home from that concert that went until midnight? You know the drill. And so do your employees.

In short, Google is enabling your employees to win on a daily basis.
So, with that in mind, back to my first question: are you providing your employees with access to information in a useful format? Compared to you-know-who?

Now that you have answered “no”, understand: you are not alone.

Unfortunately, this super search mentality has just not transferred to many workplaces. Rather than Googling information in milliseconds (for example, I recently Googled “analytics” and got 683,000,000 results in .79 seconds and Google’s algorithms put the most relevant results on the first page for me - thank you, Google!), massive data requests arrive days late and then hours are spent using pivot tables and other processes to find the answer. By the time results are tabulated, the information is old and worth very little, leaving your employees in the dark with zero access to relevant information to enable success.

Does this make Google your enemy? I don’t think so. Instead, I think we all need to see Google as our coach on Team Analytics.

Analytical tools are one way to enable winning in the workplace. Instead of crunching data in Excel for hours and hours or even days and weeks, analytical tools can act like Google, organizing large data sets into useful tables and charts, allowing everyone to make better decisions faster.

Whether you are an hourly employee or salaried, everyone wants to win. At the end of each day, it is not just the money that drives satisfaction but one’s ability to have an impact, make a difference, and drive results. Without access to information in a useful format, employees struggle to be effective, have visibility into results, and feel genuine satisfaction.

So keep Coach Google in mind as you develop your analytics strategy. Google may be known for self-driving cars, crazy-looking glasses, cat videos and the best cafeterias in town, but what they are really known for, what they do better than anyone else is one thing: they empower employees with useful data quickly and in a simple format that they can use to make smarter decisions faster.

So whatever it is your organization is known for, don’t lose sight of the fact that without access to good information quickly, your employees are losing. With better analytics, we all win big.

summer jobThe following guest post is courtesy of our board member, David Creelman.

With Spring upon us and Summer right around the corner, many businesses are actively hiring seasonal workers to ensure they have coverage for their busy seasons.

For example, just last month, Home Depot, the nation’s largest home improvement chain, announced that it was hiring more than 80,000 workers nationwide to ensure that its nearly 2,000 stores are staffed and ready to welcome spring 2016, its busiest season.

The home improvement industry isn’t alone, of course: golf courses, recreational facilities, summer camps and programs, seasonal restaurants and retailers, along with many others are all in hiring mode right now to match this seasonal business cycle.

Beyond just getting bodies onboard, leveraging this seasonal workforce can be a big competitive advantage. Whenever we are dealing with a potentially big strategic advantage; particularly one involving large numbers of workers; we want to call on the power of people analytics.

So how can people analytics help? The truth is, they can help in so many ways that I could never name them all in a short blog post, but I can say that there are a few questions that every seasonal employer should be asking themselves right now:

• Are we scheduling in a way that minimizes labor costs?
• Are we scheduling in a way that minimizes turnover?
• Are we running an analysis to discover the right trade off between the two?
• Would increasing training for seasonal workers pay off?
• What are the most costly mistakes seasonal workers make, that regular workers don’t make?
• What is the most effective way to screen seasonal workers?

You can approach these questions with sophisticated statistical methods, but you don’t need to. If you don’t have the data, tools, or skills for advanced analytics, you’ll find you can go surprisingly far with the back of an envelope or Excel. Yes, you want to do as good a job as you can; especially when it’s a strategic issue, but my mantra is that some numbers are almost always better than no numbers. Don’t let lack of heavy duty analytics horsepower be an excuse for not thinking about these issues.

My challenge for businesses is this: is it a normal part of your culture to bring analytics savvy to challenges like optimizing the effectiveness of your seasonal workforce? We are all enamored with Big Data and the latest analytics tools; but in my experience the lack of an analytics culture is usually the bigger problem. My advice? Especially if you are starting from scratch, focus less on what the analytics tools are, and more on creating an analytics-focused culture that puts value on numbers-based analysis.

big-data imageToday's post comes to us courtesy of our newest Board member, John Frehse, Managing Partner at Core Practice and a sought after speaker on the topic of workforce management. John's post aims to help organizations get started in making sense of big data and using it to solve real problems.

As technology advances allow us to analyze every aspect of business operations, many companies are inundated with data. Although we celebrate the advances in technology, this “data revolution” has also blurred the line between valuable insight and mundane non-value added information. As a result, many companies are now buried in indecipherable numbers. To successfully pierce through the mountains of useless data and focus on the strategic insights, it is critical businesses have the tools to translate heaps of distracting data into useful information.

For many organizations, payroll and labor IT systems are a significant source of operational data. As labor is almost always the number one controllable cost, dedicating time to this aspect of your business can yield significant financial and operational improvements. Many techniques, including Lean and Six Sigma, encourage large amounts of data analysis and dissemination of this information to all levels of the organization. The challenge often begins with employees’ ability to efficiently and effectively digest massive amounts of data.

The following three data sets are a good place to start:

1. The Workforce to the Workload Mismatch (WF/WL)

The Workforce to the Workload Mismatch shows how well the employees are matched up to the required hours needed to get the work done. Looking at the Workforce to Workload Mismatch on a granular level can show where labor waste occurs and more importantly why it occurs. Is Friday always understaffed? Are there too many people in the beginning of the month and not enough at the end of the month? Are there seasonal or variable spikes in demand that are not met? Seeing this mismatch and understanding root cause will quickly allow for improvements.

2. Demand Volatility (LVIX)

The Labor Volatility Index (LVIX) is a complex analysis that measures how much and how often the volatility in the demand for a service or product changes. The LVIX is prescriptive in guiding management teams to the best labor strategies based on their current situations and the challenges they face. The analysis provides management teams with how much labor is needed to satisfy demand. When we look at labor effectiveness and utilization, managers and supervisors are often told that they could have done a better job putting the right people in the right place at the right time. The LVIX provides a more sophisticated analysis, yielding a deeper understanding of the degree of difficulty required to achieve this goal.

3. Absenteeism

Absenteeism is loosely defined as periods of time where employees do not come to work. There are both planned activities like vacations and holidays, and unplanned activities like sick time and general call-offs. Unplanned absenteeism in particular negatively impacts the entire organization. Increasing visibility and transparency on this issue will immediately help improve the problem. Scrambling to find replacements or operating with less people than appropriate kills quality, service, and performance for everyone. Do more unplanned absences happen on Mondays after a holiday weekend? How likely are employees to show up for overtime on Saturday or Sunday compared to a Tuesday? Do employees call off on certain days and then show up for overtime opportunities? Identifying the trends can help isolate the behaviors and take corrective action.

By focusing on these three key performance metrics, management teams are able to quickly analyze large amounts of data, avoid data paralysis, and make strategic and well-informed decisions about important labor-related topics. By bringing a little focus to your Big Data strategy, you can reap big rewards.

Big-DataToday I discussed the implications of big data for workforce management with our board member, David Creelman and a Kronos expert on workforce analytics, Kristen Wylie.  David Creelman is CEO of Creelman Research and does writing, research and speaking on the most critical issues in human capital management. He also  leads a community of practice on evidence-based management. Kristen works in Kronos’s product marketing group and is our analytics guru.

I asked David and Kristen to comment on the following questions:

  1. How you define big data?
  2. Big data can seem overwhelming to people.  After all, a lot of big data systems are built on top of huge amounts of transactional data.  What kind of tools can help organizations to translate mountains of information into digestible bits of insight?
  3. What kind of changes and opportunities is the big data trend creating for human capital management?
  4. Is big data just for big companies, or can small and mid-sized businesses benefit as well?
  5. What are the potential pitfalls of applying big data analytics to workforce issues?
  6. What is the future of big data? What will be discussed in 5 years?

If you'd like to hear their answers, you can listen to our podcast here:

Big Data Podcast

Other relevant information you might want to check out:

They're Watching You at Work - The Atlantic

Kronos Tools for Workforce Analytics

Just What is a Data Scientist Anyway?




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