Data is essential for HR consulting, but it can also be overwhelming, misleading, or misused. How do you balance data in HR consulting, so that you can deliver valuable insights and solutions to your clients, without compromising your credibility, ethics, or creativity? In this article, we will explore some tips and best practices for using data effectively in HR consulting.
Before you dive into data analysis, you need to know where your data comes from, how reliable it is, and what limitations it has. Data sources can vary from internal records, surveys, interviews, assessments, benchmarks, to external sources, such as industry reports, academic research, or social media. Each source has its own strengths and weaknesses, and you need to evaluate them critically and transparently. For example, you should consider the sample size, the response rate, the data collection methods, the validity and reliability of the measures, and the potential biases or errors in the data.
It is also essential to know the exact time period in which the data was collected as when evaluating data, it should be put into context e.g. the pandemic years can often distort the picture.
Once you have identified your data sources, you need to define your data goals. What are you trying to achieve with your data analysis? What questions are you trying to answer? What hypotheses are you testing? What outcomes are you measuring? Your data goals should be aligned with your client's objectives, expectations, and needs. They should also be SMART: specific, measurable, achievable, relevant, and time-bound. Having clear and realistic data goals will help you focus your analysis, avoid unnecessary or irrelevant data, and communicate your results effectively.
Depending on your data goals, you need to choose the appropriate data methods to analyze and interpret your data. Data methods can range from descriptive statistics, such as mean, median, mode, frequency, or percentage, to inferential statistics, such as correlation, regression, t-test, ANOVA, or chi-square. You may also use qualitative methods, such as thematic analysis, content analysis, or narrative analysis. The choice of data methods depends on the type, level, and quality of your data, as well as the level of complexity and sophistication you want to achieve. You should use data methods that are suitable for your data, consistent with your data goals, and understandable for your audience.
One of the most effective ways to communicate your data findings is to visualize them. Data visualization can help you present your data in a clear, concise, and engaging way, using graphs, charts, tables, maps, or dashboards. Data visualization can also help you highlight the key patterns, trends, or insights from your data, and make them easier to remember and act upon. However, data visualization also requires careful planning and design. You should choose the right type of visualization for your data, avoid clutter and distortion, use colors and labels wisely, and follow the principles of simplicity, clarity, and accuracy.
We have worked with hundreds of clients to deploy visualizations of HR data using tools like tableau. While the appeal to provide highly interactive a complex visualizations is tempting, easy to understand remains the king. For your visualizations, the level of detail and granularity should be decided upon based on both your audience and the story you want to tell with the data. For executive briefings, simple and concise visualizations are key. For analysts and data savvy stakeholders, complex and detailed reports may reign supreme. At the end of the day, knowing your audience and know that your story is perceived in an actionable and meaningful way is the goal.
Finally, you need to tell your data story. Data story is the narrative that connects your data findings to your client's context, challenges, and goals. Data story is not just a summary of numbers and facts, but a compelling and persuasive way to convey your value proposition and recommendations. To tell a good data story, you need to know your audience, their pain points, their expectations, and their preferences. You also need to structure your story logically, using a clear beginning, middle, and end. You should use language that is simple, concise, and relatable, and use emotions, examples, and analogies to make your story memorable and impactful.
An example that I've seen working for many professionals is understanding how to tell a story using data. Rather than just presenting facts and figures, think about crafting a narrative arc that connects to your audience's context and goals. Introduce key challenges, then build your analysis and insights as the logical solution. Make complex data tangible through simple language, examples and analogies. Resources like Nancy Duarte's book "DataStory" provide good guidance on structuring your data into a persuasive story.