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.

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.

Yesterday I chatted with our board members David Creelman and William Tincup about what organizations need to do to create HR analytics that matter to the business.  Big Data is one of the linchpins of the big 4 SMAC themes in technology today: Social-Mobile-Analytics-Cloud.  Deployed alone and in various combinations, these technologies continue to transform the way work gets done. For many people,  harnessing the power of big data remains a new frontier.  In this podcast, David and William share their thoughts on some of the following questions:

You can listen to our conversation here:

What have you done to embed more data-driven decision making into your organization?

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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