For many of us, the terms analytics and analysis regularly come up in our daily work. But what do these words actually mean, and are we using one when we should be using the other? In this two-part blog series, we’ll first explore the difference between analytics and analysis and how that relates to learning analytics, followed by how analytics and KPIs differ when it comes to learning and development.
What are the differences between analytics and analysis?
While analytics and analysis are more similar than different, their contrast is in the emphasis of each. They both refer to an examination of information—but while analysis is the broader and more general concept, analytics is a more specific reference to the systematic examination of data.
Think of analysis as what a person is doing when they are interpreting information, gathering it into a coherent understanding, and building a narrative or plan of action in response.
Think of analytics as what a computer is doing when it accepts, stores, calculates, and makes resulting information available for examination.
So, for example, a “business analyst” describes someone who is applying a process of analysis to a body of information for some purpose, while an “analytics platform” describes a system that enables the systematic calculation and analysis of data and statistics. The difference here is in the emphasis analytics places on data and systems.
From a more practical standpoint, we often think of analytics as a thing, and analysis as an action. In that regard, analytics can be thought of as the toolbox, tools, and workbench, while analysis is the process of building or repairing something with those.
Where do people get tripped up when using these terms?
When a person or team is manually bringing together data and other information from various sources, creating presentations and narratives around that information, and then presenting this information to interested parties, this is sometimes incorrectly dubbed an “analytics process,” or the team is incorrectly considered the “analytics team.” Rather, they are analysts in that capacity, bringing analytics to bear on the interpretation and presentation of data.
More often though, the bigger confusion comes in thinking of analytics as analysis. The many heralded successes of machine learning and artificial intelligence typically underemphasize the role of the analyst. As such, many people tend to think analytics are routinely capable of understanding and presenting insights without human intervention. But true value comes from an adept analyst who can work with models and apply them to data correctly using the right set of tools and the right understanding of the data.
What’s the value of using learning data and learning analytics?
The most fundamental way that organizations compete and deliver value is through the skills and knowledge of their employees. Organizations are made up of people, and what those people learn, how they learn, and how they apply what they learn is paramount to the organization’s success. As such, gaining objective insights into this process and its results represents a key opportunity to maximize the organization’s chances for success.
It’s true that data and analytics aren’t required to disseminate information or deliver learning. But they are absolutely required to understand those processes, not to mention understand the effectiveness of the material, or the resulting impact.
Without insights into where and what people are learning, staggering amounts of investments in learning can go to waste (i.e. course libraries that are never accessed, poorly designed materials and assessments, or even well-designed materials that don’t show any impact on performance). Without an objective view of the data, these types of wasted investments can go on for years.
How can analytics and analysis be used together or in a complementary way?
Indeed they must be used together. Analytics are used for the purpose of analysis. Without analytics, there is little “raw material” for an analyst to use in their understanding, interpretation, and presentation of data. Without analysis, the data and statistics calculated with analytics is just a pile of numbers waiting for a purpose.
Up Next: What’s the difference between analytics and KPIs?
We’ve covered how data is the raw material, analytics is the toolbox, and analysis is the process. Next, we’ll look at key performance indicators (KPIs)—or the blueprint, if you will—that define what success means for both the business and L&D.
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