These first two steps are critical, and surprisingly they are often overlooked. It’s easy to think generally about the questions being asked, and it is also easy to assume that the data is available and ready for analysis. In reality, it can be really difficult to create a solid hypothesis that will drive a meaningful analytics process. And even when the objective is well understood, identifying the actual data necessary—where it exists, and how to access it—can be problematic. Determining the data that is necessary is also only half of the work required; the source of the data also has to be identified.
In an educational system, data lives in many different places, such as the student information system (SIS), the learning management system (LMS), third-party learning platforms, and many, many others. It may also be discovered that the necessary data does not exist anywhere, and a process needs to be put in place to collect and/or generate the required data, in which case the analysis has to be put on hold until the data is available.
When the data has been identified, three tasks have to be performed—and possibly automated—to gather the data together in a place where it can be analyzed.
- A location has to be identified for where the data will be stored—ideally a secure, durable, and elastic environment where the data can be safely maintained and access can be carefully controlled.
- Processes have to be implemented to capture the data from the source system and place it in the identified location—this is where automation may be necessary. There are also tools to help with this. These tools can be configured to extract the data from the source system, put it into a standard format, and load it into the target environment, on a scheduled or even real-time basis.
- The data has to be effectively organized so it can be accurately accessed and cleaned up for proper analysis. This step in the data insight process—data cleansing—while not necessarily challenging from an analytical or cognitive standpoint, typically requires the greatest amount of human effort and cost.
While data can help users answer questions, its real power exists when advanced analytics are used to help make decisions that enable people to perform better and achieve more. Let’s look at the analysis process.
Analyze and Take Action
Only after the first three steps in the DIG-AT process are completed can a proper analysis be performed. This analysis will turn a collection of data into valuable and useful information that can drive actions that result in better outcomes for teachers and students.
Framework for Information Value
There are different ways to use data to improve the learning process. One way to look at this is to think about the value that data provides. Data alone has minimal value—it is only when it is turned into useful information that it becomes valuable, and this value increases based on the type of analysis performed on it.