Training data is among the most tangible and valuable assets for guiding crucial decisions since enterprise training is at the heart of organizational change. However, it is not enough to have data for data’s sake. No doubt that the data from training is empowering. What we need is the information from the data to see the future. Training teams have prioritized data collection, and many of us have more of it than we require. Now is the time to put that extracted information from that data to work by designing data-driven decision-making. So, if you have the information, put it to use. This tutorial will show you how to train leaders in the following areas:

  • Learn which learning analytics help you make quick, correct data-driven decisions.
  • Determine which operational elements are necessary for strategic alignment.
  • Leverage learning tech to create a data analytics system for training.

So, let’s begin!

How can enterprise training achieve data-driven decisions?

Enterprise training has evolved into a sophisticated hub of human management innovation, resulting in increased company success. Accessibility to, assessment of, and the use of a well-crafted information system feeds and drives innovation among learning teams in leading firms, not only for the training algorithm but also for the company’s overall performance.

This article will discuss the importance of data literacy and the dangers of not pursuing it as an essential ability for today’s leaders. Some misconceptions about learning data management will be debunked, preventing training teams from attaining their full potential in the organization. We’ll also show you how to design a strategy for extracting decision-ready data from your training department, converting instruction from a cost center to an intelligence database for your company.

The first stage in getting your enterprise training metrics to work as organizational intelligence is to look into how they may enter the decision ecosystem. Let’s get this party started.

Data literacy and enterprise training
enterprise training

Data literacy is a taught talent that allows you to question, comprehend, and communicate with data successfully. It incorporates logical thinking components that help with data analysis and communicating skills that help you express your story with data.

Every company leader today understands the importance of data usage. However, when the consequences of not correctly leveraging training data are considered, the need for training executives to acquire this skill increases.

The following are some of the dangers of poor data literacy in training:

  • Despite increased demand caused by workforce shifts, the failure to prove the ROI for enterprise training has put the budget and crucial project support in danger.
  • Separation of training findings, limiting the use of this organizational proof to talent management only, rather than the entire range of business impact.
  • Using reactive training approaches to react to business requirements that have already put the company under stress instead of proactive strategies that foresee training requirements and the potential of learning to improve performance.
The current state of data literacy
Businesspeople working in finance and accounting analyze financi Free Photo

If your company is just getting started with data literacy, you’re not alone. Despite a decades-long emphasis on data collection, Americans are near the bottom of an international data literacy assessment.

Data mastery hasn’t progressed much in recent years despite the intense focus on the relevance of statistics in business. According to a Forrester analysis from 2020, only 40% of participants agree they get the data skills essential for their job. Even though 82 percent of leaders want all staff to own basic data literacy by 2025, up from 40 percent in 2018, and 70 percent of workers are projected to extensively use analytics by 2025, according to the same survey.

The challenges faced by enterprise training in improved data utilization

The advancement of technology that supports the goal of enterprise training runs parallel to the discourse on data literacy inside the L&D function, as it has in any other part of the organization.

Enterprise training has maintained a considerable gap in operating technologies and technical investment, maybe more than other mission-critical sectors. This underinvestment has resulted in the loss of access to unified, cohesive data models and a delay in developing data proficiency among workers.

You can calculate the cost of not using data in terms of money. According to an analysis, enterprise-scale organizations spent trillions on essential projects to modernize their businesses, but 70% of these projects collapsed because investments surged without addressing data literacy within the teams affected. Many corporate groups, including enterprise training units, have been left with massive data. Still, they only have a few alternatives to analyze or execute due to the continuous focus on data collecting.

The first move towards overcoming analysis paralysis is to engage in data literacy. It doesn’t have to be a massive investment; a simple shift in mentality and readiness to gently examine data is a fantastic starting point.

How to improvise data literacy?

Breakdown your data

A data-literate enterprise training team can do the following:

  • To investigate your statistics, ask the right questions.
  • Recognize which information is relevant.
  • Run experiments to verify the data’s accuracy.
  • Transform data into relevant and essential outcomes.
  • To swiftly express your influence, visualize data.
  • To involve and persuade stakeholders, narrate using statistics.

Constantly question your data

Developing a data-driven interest is the price of data literacy. Address the following questions on a regular basis.

  • What is the significance of this decision?
  • Will this move benefit simply the enterprise training department or the entire organization?
  • What evidence do we have or can get that confirms or refutes this hypothesis?
  • Have we put this outcome to the test?
The myths associated with enterprise training data
what is the difference between data owner and data steward

Enterprise training professionals have always chosen to collect high-level data on trainee and program success in the face of constrained resources and shortage of time. Descriptive analytics remain critical when determining the benefit of training and quantifying this essential organizational expenditure. While these figures are significant, their potential to elicit broader insights into the future demands or present commercial prospects are restricted.

Where enterprise training data is analyzed, spreadsheets are typically used, which demand considerable effort for upkeep but give little benefit to the organization. Data connection among training platforms can be challenging to create, let alone connect to bigger business KPIs.

Change is required to improve your ability to use training data. The first shift is in your organization’s attitude toward data. What misconceptions or irrational beliefs do you have in your business that are preventing you from progressing to data-driven strategic planning for your program and your company?

The myth of excess data

Whenever it comes to information, volume isn’t the only determinant of value. If your program is like others, you’re planning, producing, delivering, and reporting on your enterprise training program using anything between nine to twelve different systems or applications.

All of these tools generates datasets. It’s easy to get carried away with the idea that you already always have facts you require because the program provided you with some figures. If you use the tool’s pre-programmed reporting, though, the result is likely to be more linked with the tool’s sales pitch and brand value than with the effect of your project on your company. Do you really have statistics showing how your program is performing, or data that shows how your tools are functioning? There’s a distinction to be made.

The myth of data accessibility
what is the difference between data owner and data steward

This story, like many wonderful stories, appeals because it contains some reality. It was difficult to envision a strategy to develop a comprehensive data model and reporting coherence for the enterprise training function until lately.

In reality, corporate training platforms like PlayAblo arose partly as a result of this requirement. Because business training affects expertise, skill, competence, compliance, and a wide range of academic and social resources, it’s critical that all training data be available and documented. That accessibility is now available. Obtaining this type of availability may necessitate a shift in how you approach building a training technological infrastructure for your course, similar to how operations develop ERP systems.

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The myth of impact in silos

Most companies have moved out of siloed mentality, but only a handful have operationally defined it. With employment shifts like “The Great Resignation” and the still-unresolved issue of remote teams versus blended versus in-office work concepts, the importance of enterprise training teams has never been more evident, not only in the organization’s long-term viability but also in its effort to remain flexible and competent. Many prominent firms are currently at a juncture in terms of growing their commitment beyond training’s purpose to training’s statistics. They understand that instruction is vital, but how can they extract the valuable business knowledge that data provides?

Training leaders are adopting business intelligence’s new position and preparing their staff to give analyses and recommendations beyond traditional up-level reporting. They are offering business effect projections regarding student performance and integrating this data into production capability, planning, and other factors. Learning and development is a critical stakeholder in recruitment and retention efforts, as applicants, who most expect to change professions 3 to 4 times in their lifetimes, value training online with investments and savings plans.

The first move in debunking this fallacy is to link the rich corporate data that training provides via analytics techniques. You can illustrate how enterprise training data is valuable for other departments after you can explain how learning influenced key KPIs across the business. Advanced predictive analytics may then be used to affect strategic choices in areas like skills, productivity, and finances. Adding relevance to enterprise training data is necessary to break it out of its isolation.

The solution

Breakdown your data-related myths

By busting these training data myths, you’ll be able to:

  • Demonstrate the importance of training to the organization as a decision-maker.
  • Link learning outcomes to bottom-line results to increase training ROI.
  • Instead of fighting for more money, lead the discussion about technology investment.
  • Lay the foundation for a data-driven, sophisticated training operation.

Consider a shift in mindset

These suggestions will assist you in dispelling these myths:

  • Identifying who and why can profit from enterprise training data.
  • Understanding what is the difference between data owner and data steward.
  • Examine the information you have and determine whether it is helpful and important.
  • Consider using a more sophisticated solution to replace technology that generates too many datasets or pre-made summaries.
  • Master the notion of data science and apply it to your data sets.
Designing decision analysis
what is the difference between data owner and data steward

To engage in the decision ecosystem, enterprise training teams must explain their narrative using data, integrate data sets to bigger organizational objectives, and digitize their decision-making. Even some of the most data-savvy teams, armed with the most dependable data, can fall short of making correct and consistent judgments. Why? Many people ignore the key to decision analytics that is there in front of their eyes: operational needs.

You may well not realize that the operational needs which guide your everyday training programs are also crucial for data analyses. Still, they serve as the precise inputs that allow the tech to automate many forms of making decisions for you.

Consider how a fortnightly learning event calendar is put together: times spent gathering requirements across the company, plenty of calendars surveyed, whiteboards scrawled with comments about educators, classes, or hardware, and your training group spending late hours sorting out the complexities of scheduling conflicts. It takes a couple of weeks, if not a month.

Such whiteboard remarks and caffeine-driven resource limitations ideas are insights that add context to datasets and, in turn, help automate difficult training logistical choices.

Planning an event calendar is only one instance; your team is immersed in similar operational entanglements on a daily basis. They’re all possibilities for decision-making.

What’s the solution?

You may comprehend the structure of decision-making as a system by monitoring operational needs. You could cross-reference a class calendar with your instructor’s schedules, for instance, and therefore only arrange that educator into accessible classes.

That is a fairly simple option that is unlikely to transform your company, but then when you extend such a simple decision throughout your whole enterprise training function, the human work required becomes a genuine cost to the company.

Now add extra criteria to this straightforward case. What if evidence shows that this instructor is particularly effective with certain groups of students? What if those batches are required to complete regulatory criteria for specialized enterprise training within a certain time, causing the initial training schedule to fall apart?

You could certainly train these cohorts with a separate set of trainers, but their effectiveness might suffer as a result. How can you strike the right balance in this case to achieve the greatest results? These are the considerations that go into making a final decision.

Drive decision making by linking it to operational needs

The cost of formalizing decision-making is low: these questions probe your enterprise training operations for demands that will drive decision-making. These questions should be easy for your team to answer.

  • Do you have sufficient resources to assure the success of an enterprise training strategy? If not, where do you think you’re falling short?
  • Which members of the team are necessary for a specific job or project? What are their distinct obligations? How many important details do they have in their heads which need to be written down?
  • What modules are required to fix a certain issue? What are the elements that go into making the course?
  • What are the tasks that a student must complete in order to pass a course? How about the professor? Are you in charge of training? Who exactly are pedagogical designers?
  • Is there a set order in which connected assignments in a course should be completed? Within the context of a learning journey?
  • How will you know if this course was an achievement?
  • Who are the partners in this course, and who is concerned about its success?
  • What methods are used to settle issues with students and instructors?
  • Is it possible to automate any part of a strategy? If that’s the case, how much time would it save you? Is the automation dependable? How much manual labor will it still necessitate?


You can convert decision-making into an economy of scale by merging literacy, debunking enterprise training data misconceptions, and using a corporate training platform like PlayAblo.

Ad: PlayAblo’s Enterprise-Grade Micro-Learning platform is built for millennial learners. Micro-Learning, along with assessments and gamification features, ensures learning outcome measurement and sustained engagement.
Find out more and request a custom demo!

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