Industry 4.0.  It’s the latest industrial revolution beginning in 2011.  But fast-forward just a little over 12 years, and it seems like this newest movement has catapulted ahead.

With the sudden jolt of the global pandemic to the launch of ChatGPT, it seems as if we’re in a more mature Industry 4.0 – with every trade impacted, including human resources.

But what is at the core of Industry 4.0?  Data.  Big Data. Quantitative data. Nominal data. Qualitative data. Discrete data. Continuous data.

Data is extraordinarily helpful in understanding where we are and what needs to be done to get to where we’re going.

However, not all data is clear even with all the Industry 4.0 tools we now have.  Some data is, well, fuzzy. So, how do recruiters and HR professionals plan with data when it’s fuzzy? After all, what are the numbers telling us if they’re not crystal clear?

What Is Fuzzy Data?

Fuzzy data is essentially “[i]mprecise data with uncertainties which indicates that the observed values cannot be considered as the true unique values.” In other words, the data you may be using does not include “precise numbers, or vectors, or categories.”

However, most “real” data is not precise – or fuzzy.

Let’s look at HR and recruiters specifically.  According to a recent study, when examining Big Data, professionals gather insights around a “wide range of tasks solved by the personnel, both organizational, economic and technological.” However, with such large amounts of data – often surrounding people and their tasks and characteristics – we can’t always determine “true and false.” So instead, we look at multiple possible truths (or in other words, degrees of truth for each interaction, resulting in various possible (and reasonable) conclusions.)

How Can We Plan with Fuzzy Data?

Industry 4.0 has directly impacted HR – with some now calling this impact HR 4.0 – allowing the industry to become more automated and focused on high-level strategic strategies as opposed to manual, repetitive activities.

Through the Internet of Things, artificial intelligence, Big Data, technology stacks, and data analytics, recruiters and HR professionals can now build “more efficient and lean teams,” through attracting, retaining, and mobilizing top talent in this continually evolving industrial revolution.

However, not everyone understands how to pull insights from fuzzy data.  For example, if a manager asks specifically about productivity – but the number of successful key performance indicators (KPIs) don’t match up, then the data may not make sense.

That doesn’t mean the data is bad. It just may be fuzzy.  Someone who understands how to read HR data can specifically pull valuable insights from that data as opposed to someone who is only skilled in reading data while making true or false conclusions.

Here are some best practices to keep in mind when analyzing often fuzzy HR data:

  • Data often has to be read in “real time,” as the needs of HR change on a seemingly daily basis. So, knowing when to read this data is critical to garner insights that aren’t stale.
  • HR is often behind other departments in having the best (and the right) data analytical tools. Leaders need to reprioritize HR when analyzing Big Data, ensuring that organizational budgets align with needs and strategies.
  • Leaders must also prioritize reskilling and upskilling recruiters and HR teams, allowing them to garner the necessary skills for a strategic and insightful analysis of fuzzy data.

It’s time for HR to embrace fuzzy data with the right tools and support.  After all, what benefits HR benefits the organization as a whole – and it’s time to recognize that.

Jenny Kiesewetter

Jenny Kiesewetter is a prolific Nashville-based freelance writer, teacher, and coach, specializing in human resources issues, employee benefits, employee training and development, and legal compliance issues. Jenny has a knack for making complicated topics understandable, digestible, and valuable. In her free time, Jenny enjoys spending time with her friends and family, traveling, live music, and dining out.