“The Board is concerned the competitive labor market might be a substantial obstacle to our operations over the next year. Does the training team have any data that could help us better understand how this tight market might impact us, and inform our staffing strategy?”
If you were asked that question today, what might your response sound like?
“The labor market is definitely a big challenge for our staffing situation and we might need to adjust our staffing strategy. Give me a week, and I can put together a report.”
“Actually, the training team has prepared several forecasts. We expect a deficit in skilled welders, especially by Q3 of next year because of resource constraints in the welding program. We forecast overages on some projects to increase by 20% unless we resolve resource constraints. We’ve been leveraging HR data to develop an action plan to recruit additional instructors, while also optimizing schedules to maximize training resources. Give me a few minutes and I can send you what we have so far, and keep you up to date as we keep working on our models.”
What is the difference between these answers? The first answer comes from a training team that is both reactive and siloed, only analyzing training data for the benefit of the training team. The second answer comes from a team that proactively thinks about the future and works beyond the strict limits of their own internal data. Those key factors can make the difference between a training team that is permanently on the back foot, and a team that forms a key part of overall business strategy.
The truth is most training teams aren’t making the best use of their data, and that’s preventing them from being able to confidently discuss the future of their own operations and the business as a whole. Telling the story of tomorrow is more compelling than reporting yesterday’s events, but doing so accurately and effectively requires a sea change in the way that training teams think about and leverage their data to make accurate, repeatable decisions that inform organizational strategy.
The Need for a Revolution in Training ROI
One of the biggest challenges facing training teams is the constant pressure to cut costs. Businesses face rising competition, in a rapidly-changing global marketplace, and doing more with less wherever possible is essential to scaling up.
That puts departmental budgets on the defense, and training is no exception. Training has the potential to contribute enormous value to all areas of a business, but often struggles with the outside perception that it is a cost center whose negative impact needs to be minimized, rather than a source of value to be invested in. Breaking out of that stereotype is possible, but requires demonstrating strong ROI, and that can be difficult for a department like training which only indirectly affects many daily business operations.
The need to demonstrate ROI is something that other departments are very familiar with. Marketing, finance, sales, and other teams come to board meetings armed with sophisticated projections and indicators that prove to the rest of the company the strategic importance of their operations to mainline business goals. Training, however, has struggled to keep up with this trend.
But there are strategies that prioritize training ROI. Training has all the tools it needs to match and exceed these other departments in taking a proactive and ROI-focused approach to its operations – it just needs to adopt new systems and mindsets that can leverage these tools effectively.
Why Demonstrating Training ROI is Difficult
Why has training historically struggled to prove ROI where other departments have succeeded? It partly has to do with the availability of software tools. Other teams are effective at demonstrating their ROI because for nearly a decade, they have had access to comprehensive software systems that brought together all of their data, and made processing that data easier. That enabled them to adopt a mindset in which delving deep into their data for insights into their operations was both possible and central to their success.
When these comprehensive software tools were first being developed, training was left behind. Only in the last few years have equivalent tools aimed at training teams started to enter the market, and their adoption is still low. Many training teams are still using complex tech stacks of single-solution software that can’t interface effectively and make data management a nightmare. The difficulty of accessing and engaging with data on such a tech stack has prevented training from adopting the kind of mindset towards data that other teams have taken up.
Without the right software, the mindset can’t be nurtured or sustained. But now that the software is available, there’s nothing stopping training from engaging in the same kind of data-driven decision making about the future that other teams have successfully used to prove their value. Let’s take a closer look at the attributes of a future-first mindset.
Adopting a Future-First Mindset for Utilizing Training Data
Training teams often feel more reactive than proactive. It’s assumed that other departments make strategic decisions, and the training team is expected to facilitate those decisions. But training has just as much data available as any other team, and now has the software tools to start utilizing it. All that’s needed is for training to start thinking about their data differently.
Preparing for the Future: More Data, Different Data, Better Data
The first step in making that mindset shift is for the training team to step away from a siloed view of their department. Internal data like learning analytics are essential, but they don’t paint a full picture. If training is going to be seen as a strategic investment that impacts all areas of the business, then training needs access to many different types of data, gathered from all areas of the business. That means taking an active approach to gathering data from other departments and incorporating it into your training datasets.
External data needs to be considered too. Any PESTLE factors relevant to training operations should be considered and included in the data that the training team is dealing with. Many training teams already deal with PESTLE factors informally, but incorporating them into the formal decision making process will inform even better data-driven decisions.
The team should constantly be asking themselves “Are we missing anything?”. If there is a dataset or trend that might have an effect on future training operations, and it isn’t being discussed by the training team, something has been missed and your training data is incomplete. That might mean looking for data sets that seem to have nothing to do with the training function, because making data connections is key to your team’s strategic success. Projected sales data might seem only distantly related to training, but if increased sales place strain on the customer service helpdesk, then the need to hire and train more representatives makes that sales data a training concern.
Training Data as the Story of Tomorrow
Getting out of a siloed mindset and considering how organizational and external data might relate to training is a big step. But an even bigger step, and one that will push training teams even further towards demonstrating training ROI, is reframing data as clues for telling the story of tomorrow.
Most of the data that a training team collects are descriptive analytics – fundamentally, data about what happened yesterday. On their own, they don’t tell you why it happened, and they don’t tell you what’s going to happen tomorrow, but they are a critical starting point. By thoroughly interrogating and analyzing this data, you can gain valuable insights into the future.
Looking to the future is essential for developing a proactive approach to training. The best teams, in training or any other field, look at all of the data they collect as a potential roadmap for the future instead of as a chronicle of the past.
Making the Most of Predictive and Prescriptive Analytics
Predictive and prescriptive analytics aren’t new. But often, these powerful tools are underutilized, particularly by training teams. A lack of easy access to data and a reactive mindset have stopped training teams from investing more of their efforts into understanding and preparing for the future – but that doesn’t need to be the case. With the advent of training management systems, training teams have everything they need to take that next step.
Predictive Analytics For Training Data
Predictive analytics allow your team to make concrete, data-backed models of the future, which are crucial for proving the ROI of your training operations. This typically requires software tools that are capable of conducting regression analysis on large datasets. But that isn’t to say that predictive analytics are a matter of plugging in the numbers and letting the computer do all the work. Training teams need to be active participants in the analytical process, not passive observers of a computer’s reports.
If you want more than a pile of disconnected tables and charts, you need to carefully consider what data you are analyzing – and what data you aren’t. Taking a detective-like approach by considering what relationships between data sets might be meaningful or insightful, and which ones probably won’t be, is crucial to making sure that your predictive analysis generates results that can actually inform data-driven decisions.
Prescriptive Analytics for Training Data
Having a data-backed idea of what the future holds is a huge step. But having a data-backed plan to respond to that future is even more important for demonstrating the strategic importance of the training team. This is where the cutting-edge field of prescriptive analytics comes into play.
Prescriptive analytics take predictive analytics further by generating recommendations for future action. This requires even more powerful analytical tools, and often relies on machine learning and artificial intelligence to abstractly consider huge amounts of data. That can seem like a daunting challenge for a training team, but there’s a simple truth that has to be remembered: training data can be processed, analyzed, and utilized in exactly the same way as the data generated by any other department. All that’s needed are the right tools, and the mindset that analyzing your data can and should be an essential part of your operations.
Training teams were left behind when predictive analytics tools were first being developed. But with prescriptive tools becoming more common and less experimental by the day, training teams have another chance to be right on the forefront of the data-driven decision making revolution.
An Ongoing Model for Data Driven Decision Making
In the end, regardless of the tools used, one lesson is essential for making data-driven decisions: it cannot be an occasional task. Making decisions based on data, and especially decisions based on predictive or projected models, has to be a continuous process of assessing new data as it comes in and adjusting models, forecasts, and predictions to best match what’s actually happening. Dwight D. Eisenhower famously quipped “I have always found in preparing for battle that plans are useless but planning is indispensable.”
The truth is that even the best analytical prediction will likely get a few things wrong. You can trust thorough data analysis to provide a reasonably accurate and actionable model of the future, but it will never be infallible, and that’s okay. The process of continuously examining, interrogating, and utilizing your data will still vastly improve your decision-making and make training seem much more critical to business goals, even if the future doesn’t unfold exactly as planned.
A mindset shift in using training data is the first step toward building a scalable decision-making engine. The next step is to evaluate your learning technology to see if you can benefit from a training operations platform, like Administrate.