23 Analytics
Explore & Engage:
Here are some ways you can start to explore and engage with analytics in your people analytics journey to build your desired skills.
- Collaboration is Key: Partner with analysts, qualitative researchers, visualization experts, data scientists or AI specialists within your organization. Learn from their expertise and leverage their insights to enhance your people analytics capabilities.
- Learn foundations and stay up to date on trends: The field of analytics is based both on statistical foundations that are long-standing and do not change; making it possible for you to build a solid foundation. But it also is rapidly evolving in terms of how foundational approaches are being applied and new approaches and techniques are emerging rapidly. Subscribe to industry publications or follow thought leaders on social media to stay ahead of the curve.
- Embrace Experimentation: Don’t be afraid to experiment with new tools and techniques. Just be careful about what you put into the tools that you experiment with – NEVER put company or employee data into a technology tool (especially not a Generative AI tool) that wasn’t explicitly built for HR data privacy!!!
- Start Small and Specific, then Scale Up: Begin by applying analytics to a very specific people analytics challenge. For example, start by running basic descriptive analytics on quantitative survey data and move along to try diagnostic, predictive, prescriptive or cognitive approaches as you go. You might also simultaneously start learning to code the open-ended responses to that same survey using sentiment (sentiment analysis) before moving on to coding multiple types of themes (thematic analysis) and you would want to have a grasp of simple text analytics before branching into more advanced natural language processing approaches.
Self-Discovery: Analytical Skills
Discover your strengths and uncover areas where you may want to grow new skills by trying a self-assessment. The below tables give a list of people analytics skills you can build. You’ll also find blank rows in each section where you can add any additional skills or techniques you may have heard of that you want to try so you can keep track of them all in one place.
The reason this is designed a self-assessment, rather than just a list of skills is because people analytics doesn’t require you to have all the possible technical skills and it doesn’t require you to have them all at the same depth. Depending on what you want to do in your career, there are some technical skills where you may only need to be aware, but in others you may need to be advanced or expert. Also, there is a lot of personal choice and preference in the way you choose to apply your technical expertise in a people analytics career. Some techniques are going to be of interest to you and you may choose to set your sights on developing to an advanced or expert level. For others, you might be happy stopping once you get to a Learner or Intermediate level. The self-assessments you will find here and throughout the step 3 section on technical skills are meant simply to provide you with many possible skills so that you can identify where you are, where you hope to be, and explore new possibilities on your skills development journey.
How to use these tables:
- Read the description of each skill and make your best estimate of your current level of ability based on the following scale:
- Aware: You are brand new to this skill. You may know about it conceptually, but have not yet completed the task yourself.
- Learner: You’re just to learn this skill or still rely on detailed instructions and guides to complete this task.
- Skilled: You have some ability in this area, but you still need assistance or to tackle certain parts or advanced aspects.
- Expert: You confidently handle challenges in this topic area, do not need support, and can clearly and simply explain concepts to others.
- Strategist: You are applying this technique in new ways and creating new ways of doing this, you are able to mentor/guide intermediates and advanced individuals in this skill.
- Optional: Determine if there are any skills for which you would like to have a different future skill level and indicate your desired level. Keep in mind, that your desired skill levels may change over time and may change as you consider different possible career options. You will likely come back to this table and change your desired levels many times.
Note. Don’t worry too much about where you rate yourself, there’s no true score or measurement for something like this. The scale provided is only meant to provide a general sense of guidance and to serve as a thought provoking exercise.
Descriptive Analytics Skills
| Skill | Description | Current Level (Aware/Learner/ Skilled/Expert/Strategist) | Desired Level (Aware/Learner/ Skilled/Expert/Strategist) |
| Calculate counts, sums, frequencies, percentiles | Determine the number of times a specific event or category appears in a data set and how the frequency of values are distributed. | ||
| Measures of center (mean, median, mode) | Understand how to calculate and interpret various measures of “center” in a data set. | ||
| Measures of dispersion (min, max, variance, standard deviation) | Understand how to calculate and interpret the spread of data in a data set. | ||
| Rates, ratios, odds | Measure the frequency of an event occurring over a specific period of time, compare relative sizes of numbers or quantities, express probabilities and odds of events happening. | ||
| Correlation | Understand how to identify, measure and interpret linear relationships between two variables. | ||
Diagnostic Analytics Skills
| Skill | Description | Current Level (Aware/Learner/ Skilled/Expert/Strategist) | Desired Level (Aware/Learner/ Skilled/Expert/Strategist) |
| Statistical significance testing | Set desired confidence levels and assess if your findings are statistically meaningful or potentially due to chance. | ||
| Group comparison tests (t-tests, chi-square, ANOVA) | Compare data between different groups to identify differences or patterns. | ||
| Regression analysis | Model linear relationships between variables to identify and explain the degree to which a variable influences the other (diagnostic application). | ||
| Exploratory analysis | Utilize analytics approaches for exploration to identify patterns and relationships in data. | ||
| Root cause analysis | Conduct systematic investigations to uncover underlying reasons behind specific outcomes. | ||
| Drill-down analysis | Deep dive into data at a detailed level to find insights hidden within aggregated results. | ||
| Comparative analysis | Compare data sets to identify patterns and differences between groups, subsets, or time periods. | ||
Predictive Analytics Skills
| Skill | Description | Current Level (Aware/Learner/ Skilled/Expert/Strategist) | Desired Level (Aware/Learner/ Skilled/Expert/Strategist) |
| Regression analysis | Learn to use regression beyond relationship identification to identify and explain the degree to which a variable(s) predicts possible future values (predictive application). | ||
| Causal inference | Learn to establish cause-and-effect relationships between variables. | ||
| Decision trees | Use decision tree models to classify based on various factors and predict outcomes (predictive application). | ||
| Time series analysis | Learn to analyze time-dependent data to forecast future trends. | ||
| Survival analysis | Understand how to predict the likelihood and timing of events. | ||
| Machine Learning (ML) | Use machine learning algorithms to identify patterns and make predictions. | ||
Prescriptive Analytics Skills
| Skill | Description | Current Level (Aware/Learner/ Skilled/Expert/Strategist) | Desired Level (Aware/Learner/ Skilled/Expert/Strategist) |
| Optimization modeling | Understand how to use mathematical models to create ‘optimization criterion’ for identifying the best solutions for complex decisions. | ||
| Decision trees | Learn to use decision trees beyond classification to analyze different scenarios and recommend the most favorable course of action (prescriptive application). | ||
| Simulations | Understand how to build and use simulations to test the potential impact of various strategies. | ||
Qualitative Analytics Skills
| Skill | Description | Current Level (Aware/Learner/ Skilled/Expert/Strategist) | Desired Level (Aware/Learner/ Skilled/Expert/Strategist) |
| Qualitative Data Coding | Able to assign codes to segments of qualitative data for organization and analysis of the data. | ||
| Sentiment Analysis | Able to assign emotional scores to data to analyze the sentiment and content of the data. | ||
| Thematic Analysis | Able to identify, analyze, and interpret recurring patterns and themes within qualitative data through thoughtful and systematic examination of theme coded data. | ||
| Qualitative Research Methodological Approaches | Understanding of and ability to choose between and apply various qualitative research methodologies such as Narrative Analysis, Phenomenology, Discourse Analysis, Grounded Theory, Conversation Analysis. | ||
Advanced Analytics Skills
| Skill | Description | Current Level (Aware/Learner/ Skilled/Expert/Strategist) | Desired Level (Aware/Learner/ Skilled/Expert/Strategist) |
| Organizational Network Analysis | Learn to use statistical and graphical models to map people, tasks, groups, knowledge and resources of organizational systems for the purposes of identifying patterns. | ||
| Automation | Learn to apply automation to improve efficiency and accuracy of analytics tasks. | ||
| Advanced Modeling (e.g., ML, deep learning, etc.) | Use more advanced applications of machine learning (ML) concepts like supervised and unsupervised learning, classification, and regression algorithms or deep learning like neural networks for complex tasks. | ||
| Natural Language Processing (NLP) | Use natural Language Processing (NLP) techniques to analyze text data (can include but is not limited to other analytical techniques mentioned such as word frequencies, sentiment analysis, and topic modeling). | ||
| Computer Vision | Use computer assisted techniques to extract insights from images and videos (e.g., image recognition, object detection). | ||