Step 1: Collecting Raw Data
Analytics are only as accurate as the data on which they are based – Garbage In, Garbage Out is what a report will show if the raw data is too general, or entirely wrong. The quality and comprehensiveness of the data collection is critical. Setting the data sources correctly is a critical step in generating reports, ensuring that it is collected uniformly across all relevant sites, groups and departments, so that apples can be compared with apples. Using the optimum tools and technology to gather accurate and relevant information helps to ensure the purity and relevance of the raw data.
For example, a training organization that wants to compare the performance of three randomly assigned groups of trainees in the same course, should compile data from all possible sources – tests and assignments, feedback from instructors and trainees, and data from equipment used in testing, such as simulators, and sensors on the trainees in action that provide data about speed, accuracy and more. The Internet of Things (IoT) has enabled a multitude of such sensors to collect and transmit this type of data, and is a valuable source of information that could not be obtained otherwise.
Step 2: Uncovering the “Why”
Raw data is purely descriptive – it shows what happened and when, such as a decline in subscriptions over the past year, but without any reasons for this occurrence.
Once the raw data has been cleaned, by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted, it is ready to be examined and analyzed. Cleaning the data and filtering out the statistical noise is an essential step to maximize a data set’s accuracy.
At this stage we investigate the reasons why a specific result was obtained. Anyone looking at a graph of Pfizer’s increased revenue in Q4 2020 can easily correlate their production of COVID-19 vaccines to this growth. But not all correlations are that obvious, and here a strong, automated system can go far to correlate and elucidate the ‘why.’
Step 3: Implementing Predictive Analytics
While looking at past results is critical in determining why a specific event occurred, the real value of analytics is being able to obtain insights that can improve outcomes. Organizations need to look at how each unit or department is using data, to determine their current needs and future requirements. This process often uncovers duplications and other issues, which can be corrected to streamline operations, and to set realistic KPIs.
For example, a training organization can see that the implementation of an online grading process in a particular course led to more accurate and standardized results, as compared to the previous semester which used a legacy grading method. Instructors and trainees were pleased with the increased objectivity of the system and the clarity of viewing results and feedback; they were able to quantify their satisfaction with various components on digitized feedback forms, in addition to freestyle comments. The organization can use these objective findings to apply to additional courses in their roster.
Utilizing predictive analytics is an ongoing process – constantly measuring results, then using this information to improve processes and outcomes. The cycle continues as adjustments are made and measurement begins anew. Organizations can continue to fine-tune their operations and business practices based on true data insights.
This post is the first in a series that explores how analytics and reporting can assist training operations to improve their efficacy.