Understanding data and data analytics is a key skill as learning and development (L&D) progresses through transitions in design practice since 2020. In this article and the next, I will provide some background on data and data analytics and then outline a method for selecting tools to do the work involved.
The fundamental understanding required is about data. Data (singular or plural) consists of facts that can be used or analyzed to calculate or plan so that the design team can gain knowledge or make decisions. The data is statistics or other information that can be processed, usually by computer in the context of this article but it may sometimes be analyzed mentally or by manual calculations. According to the dictionary, data is usually text or numbers written on paper, or by bytes and bits inside the memory of electronic devices, or even by facts stored inside someone's mind.
By itself, data is just disorganized facts and figures. Data in and of itself has no meaning, and it requires analysis in order to identify and comprehend its significance. In its raw form, data is either descriptive information (qualitative data) or numerical information (quantitative data).
Until data is analyzed, it is not knowledge and cannot be acted on. Once data analysis has processed the data so that it is understood through patterns, observation, and description, it becomes business intelligence. At that point, individuals can make sense of the data as insights and trends, and can use those to solve specific challenges and problems. The process generally includes extracting and categorizing the data.
Patterns that emerge from the analysis can reveal useful and relevant information about the organization, such as group behavior, individual performance problems and so on.
Types of data analytics
Depending on the outcomes, there are four main types of data analysis. Understanding these is a key to selection of software tools, which will be covered in the next article.
- Descriptive analysis: identifying what happened
- Diagnostic analysis: understanding why it happened
- Predictive analysis: identifying what is likely to happen
- Prescriptive analysis: determining the best course of action
As an instructional developer, there are two general ways in which you will be using data analytics:
- As part of the "front-end analysis" or "needs assessment" of a business problem (descriptive and diagnostic analysis)
- As part of predictive or prescriptive analysis to decide whether there is a problem and the best way to resolve it.
Selection of data analytic applications
There are many application and software types used to perform data analytics. In the next article, I will examine how to select software or applications to consider for use.