For the past few years, buzzwords about (big or small) data and making sense of all that information have been thrown around quite often by industry research analysts and vendors alike.
First things first — what is big data? Where does it come from?
According to the Gartner IT Glossary big data includes high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
The fourth V, veracity can be considered the most important. How accurate is that data in predicting business value? Do the results of a big data analysis actually make sense? Data must be able to be verified based on both accuracy and context.1
Keep in mind there is already plenty of enough useful information to analyze within your Learning Management System (LMS) alone to get started.
Maximizing the Value of Training Investments
Organizations, especially those in highly regulated industries, have to make sure their employees have the knowledge and competencies they need not just to do their jobs well, but to meet government regulations, local and international industry standards, or to obtain and maintain their qualifications and certifications.
By using the right tools to analyze your talent, learning and performance management data, you gain actionable insights that will enable your organization to maximize the value of its training investments in terms of time and money.
Analytics and Training ROI
Return on investment (ROI) is a core piece of information L&D stakeholders want to know from the LMS. A question they would probably ask: “Is our investment in training working — does it have a positive impact on sales, customer satisfaction, service quality, production line efficiency, compliance, etc.?”
With analytics, you have the power to better analyze your data by using business dimensions that matter to your organization. For example, a car manufacturer would be interested in business dimensions such as business region, market, time, job role, functional department, language, and year. These can be correlated to organizational hierarchy, job roles, departments, time, types of learning, cost centers, and custom attributes.
Of course, all the data relevant to these business dimensions needs to be populated in the LMS, i.e., the cost and duration at module or session level.
Analytics give you the ability to calculate aggregated costs and duration on the fly. Having this information at a click of a button allows you to gain valuable insights into your training investment at any particular set of groups and business dimensions. Taking this a step further will allow you to compare performance statistics with your training investment.
Starting Your Analytics Journey
Talent analytics are an evolution of current HR, learning or compliance processes. Organizations can take the first steps today — using the data already available to them — in their talent analytics journey and turn these into insights.
You can have decades of relevant data, but it will not have much value if you don’t ask the right questions first. So you have to start by asking yourself — “what do I want to achieve with the data I already have or data that we may have to start collecting?”
Next, you have to consider the following questions as you begin your journey into talent analytics:
- How is the quality of your existing data?
- Do you have the flexibility to create your own reports?
- Can you easily create dashboards and charts?
- Do you have the technology to integrate multiple data sources?
- Have you built internal analytics and big data expertise?
- How effective is it to rely on IT for your analytics needs?
What truly matters when it comes to big data and analytics is the purpose of the analysis. When implemented properly, analytics will enable your organization to gain insights from data so you can make better decisions and ultimately, achieve your corporate goals.
If you’re interested in reading more on this topic, please download this white paper, which we recently published.
1Judith Hurwitz, Alan Nugent, Fern Halper, and Marcia Kaufman, Defining Big Data: Volume, Velocity, and Variety – Big Data for Dummies Cheat Sheet (Wiley, 2013).