Workplace learning is becoming increasingly digitized. That means decision-making is increasingly driven by the integration of varied data sources within learning management platforms. By fusing data from employee performance reviews, learning module engagement, and feedback surveys, organizations can gain a holistic view of their training effectiveness. What’s more, by breaking down data silos, learning and development programs can align more closely with other organizational priorities, like business goals and compliance obligations.
So long, silos
In an “everything digital” age, it should come as no surprise that learning and development initiatives are also undergoing digital transformation. And yet, simply going digital isn’t enough. Organizations can easily end up with islands of data that provide insights that are an improvement on the paper-based systems, but fall short of delivering the joined-up, frictionless value promised by today’s advanced analytics and cloud-based processing capabilities. Typical data silos that HR departments grapple with include employee performance reviews, skills assessments, role descriptions, and employee development plans. Meanwhile, in the learning and development sphere, there are attendance and completion rates, course feedback surveys, learning management system reports, and training effectiveness measurement. Similar data silos extend across organizations, severely limiting the potential insights that a holistic approach would deliver.
To address this lack of interconnectedness, organizations are undergoing data transformation or data engineering projects, an important stepping stone in their digital evolution journey that unlocks these data silos and readies them for AI-based analysis. For learning and development teams, AI-based analysis is nothing short of transformative. The magic lies in the integration of many data sources from all over the organization and outside it, synthesizing findings into accessible insight hubs. By liberating their learning and development data silos, organizations can expect to see immediate impacts across three main usage areas.
Benefit 1: More dynamic and effective training
The first impact organizations can expect to see is a big improvement in the effectiveness of their training efforts. For instance, an HR department could use AI to correlate employee performance metrics with their training history, identifying which training programs are most effective in improving specific skill sets. At an employee level, these training programs can be better personalized to suit the needs of each employee. We know training helps keep employees engaged and motivated, and, according to a recent PwC study, 53% of employees said their job needed specialist training and that three-quarters of employees are ready to learn new skills or retrain completely.
At an organizational level, tracking and evaluation of learning and development ROI and effectiveness becomes much clearer through the use of AI-based analytics. This data fusion enables more strategic decisions about developing and modifying training programs, ensuring they are aligned with both employee needs and organizational goals.
Benefit 2: Responsive training and talent initiatives for the short and long-term
The dynamic nature of workplace learning demands real-time insights to adapt training strategies effectively. This applies equally to the immediate skills requirements within an organization as well as future workforce objectives. Predictive analytics enables organizations to identify current and anticipate future skills gaps as well as engage in proactive workforce planning. Furthermore, by enabling learning and management programs to “talk” to talent management systems, organizations can gain valuable, strategic talent insights. For example, an HR department could use AI to identify employees with a high potential for upskilling, reskilling or with transferable skills from different areas of the business.
Benefit 3: Improved compliance and risk mitigation
In sectors with complex regulatory requirements, like finance, fintech, healthcare, and many others, AI-based learning analytics can help improve compliance. By synthesizing the employee training history data with certification requirements, HR departments and learning and development teams can monitor compliance levels and proactively identify and remediate any compliance risks.
Data integration challenges
As organizations move from disparate data silos to interconnected insight hubs, there are undoubtedly trip hazards along the way. Data transformation projects that break down data silos and engineer data into an easily “malleable” state can be long, complex and risky, especially where established institutions are required to overhaul archaic legacy systems. Organizations must also be prepared to revisit their policies around privacy and safeguarding given that the process of collecting and analyzing employee data is likely to raise issues on this subject.
Finally, digital transformation in all its forms requires culture change. Banishing data silos does enable “joined-up thinking” between and within departments and represents a step-change from the old order. It will require a certain amount of training to use these systems effectively.
The future of AI-based learning and development
AI plays a crucial role in synthesizing real-time data from ongoing training sessions, employee interactions with learning platforms, and emerging industry trends. As business decision-making becomes increasingly data-driven, ensuring those data sources are interconnected is now a business imperative. Learning management platforms are pivotal in workplace decision-making, not only in shaping the workforce of the future, but, with judicious cross-system integration can help improve operations across the whole of the business. Organizations can use AI-driven learning and development data to ensure they are meeting their training objectives, both in terms of their own goals and KPIs and in terms of compliance if they are operating in a heavily regulated field such as health or fintech.
ABOUT THE AUTHOR
Ramesh Ramani , CEO and President, ExpertusONE
Ramesh Ramani is a visionary technology leader, co-founder, and Chief Executive Officer of ExpertusONE, a leading cloud-based Learning Management System for enterprises. As CEO, Ramesh sets the overall strategic direction for the company − ensuring that products, organizational structure, operational practices and guiding principles work together to deliver superior value. Adept at evaluating business opportunities and structuring companies to profitably address present and future market needs, Ramesh has led many early-stage companies through successful growth. He is best known for his forward-thinking approach to solving complex problems and creating transformative technology solutions that breakthrough to lead and shape industries. As a software entrepreneur, Ramesh is hyper-focused on bringing together cutting-edge Learning and Development technologies under a single platform to create seamless, unified, and easy-to-use experiences to learners across organizations’ employees and partners.
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