Use Case: Analysing Employee Skill Evaluation Survey With An Automated Scoring System

Table of Contents

    In order to maintain our client’s privacy and project confidentiality, we will describe the concept of this project while keeping the metrics generic.

    Our client is specialised in conducting surveys for organizations across a variety of sectors. This company uses a range of methodologies such as online surveys or in-person interviews to collect data.

    Once the data is collected, our client analyses and interprets the result and presents findings through statistics and various data visualization formats.

    The organization plays a double crucial role for their clients:

    1. They ensure the integrity and reliability of the collected data,
    2. They provide actionable insights and recommendations based on survey summaries and statistical analysis.

    Definition of the project

    Project scope and requirement

    Our client conducted a survey with hundreds of participants, answers to 80 questions to evaluate measure the distribution of their client’s employees on a skillset matrix, Employee Satisfaction and Soft Skills. Then, they applied a scoring system for each set of skills within these two main competency categories.

    Here’s a simple diagram to represent the data analytics project:

    blog use case employee survey scoring rules

    This analysis needs to be conducted both at the individual employee level and at the team level.

    On this project, we dealt with:

    • Hundreds of contributors
    • 40,000 answers
    • 10,400 scorings

    The challenge

    Our client conducted a large scale survey, analysing answers for each participant and provide them scoring for each defined criterion. The results will be presented through data visualization.

    This survey consists of 80 multi-choice questions. Based on the participant’s answers, they will earn points. The questions are categorized into 8 criteria, and a scoring is calculated for each criterion. Due to a significant number of respondents and the requirement to calculate scores for these 80 questions, it is crucial to automate this process. It will be easier to analyse scores at the individual level, business unit and function levels.

    The expected outcome

    The company wants to breakdown the final scores along two axes – Technical Skills and Soft Skills – to enable better understanding of the participants’ positions in the matrix.

    To add more substance to the data visualization, our client wanted to add comments next to each score and show a sample of the participant’s answers.

    Having the scoring, the sample of answers and comments will depict a better, more precise picture of how each employee is positioned compared to the others.

    How did they do it with ClicData?

    1. Connect to a cloud storage app and automate data updates

    We set up a connection to their company cloud storage application to retrieve survey results sitting on an Excel file.

    Once the connection is set up, they just had to schedule data updates to pull new records every day. This data update will also update the calculation of scores.

    With automatic updates, the interviewer will have the autonomy to change the format of answers or modify specific fields in the Excel file. Everything will be updated and calculated automatically.

    2. Implement calculation rules for each criterion based on a scoring system through a dataflow

    The role of ClicData’s Dataflow is to create an ETL (Extract, Transform, Load) process, where all necessary transformations are applied to the data to obtain the final scoring. Subsequently, this scoring is visualized on the dashboard.

    Example: In a multiple-choice question, if the answer is correct, the contributor earns 1 point; otherwise, receives 0 point. Then each criterion is attributed a score level: Low or High. To achieve the highest score, the participant needs to meet the required number of points for each criterion.

    blog use case empoloyee survey dataflow

    3. Compute the global scoring for each contributor and set up a position on a two-axis graph for each potential scoring

    The final scoring is displayed as a matrix with four key areas, allowing for the classification of all participants and showing the distribution at the individual or team level.

    • Participants with the highest scores on both axis will be placed on the green square.
    • Participants with the highest score on only one axis will be placed on one of the two orange squares.
    • Finally, participants with the lowest scores on both axes will be on the red square.
    blog use case employee survery two axes concept

    Thus, the company can observe the distribution of all contributors according its criteria.

    4. Create a dashboard to have a complete overview of individual results

    This dashboard will include the following elements:

    • Participants’ names,
    • Participants’ results for each Skillset Category,
    • Participants’ position on the graph indicating a score summary in each category,
    • Comments from both the participants and the interviewer.

    How to create this two-axis graph using ClicData?

    All we had to do it create 4 squares using our Shape widget and change their colours. Then, add transparent chart widgets and bring them In Front of the shapes and adjust them so that each vector is positioned on the respective square.

    Example: In the figure below, the selected contributor obtained the highest scores on both axes, resulting in a value of (1,1) that corresponds to the green square.

    blog use case employee survey evaluation

    Then, we added a dynamic text box to display the participants’ answer to the questions.

    blog use case employee survey answers

    Then, we added another text box with dynamic fields which are populated with inputs from a Data Form widget available to the interviewer:

    blog use case employee survey inputs

    The Real Value – Automation, Data Consistency And Actionable Insights

    The interviewer and their clients have now a comprehensive understanding of the employees’ answers. The ability to automate the calculations and base those calculations on custom rules saved an enormous amount of time and ensured data consistency.

    With these rules and two-axis matrix, they are now able to identify which criteria have a high or low scoring and help them in their consulting job by providing tailored and actionable recommendations for every single employee.