Last year, I was approached by an organization that was struggling with a common data literacy problem. An executive noticed that her direct reports were not making decisions based on data. When she discussed the matter with her analytics team, she discovered most of her business managers and directors were getting repeatedly hung up on superficial design aspects of their reports and dashboards rather than using the data to optimize their respective areas of the department. 

While these middle managers had extensive knowledge of their functional areas, they weren’t accustomed to using data throughout their careers. As data was becoming increasingly important to improving the performance of her department, this executive knew her team needed to become more comfortable with using data in their decisions. In particular, she needed to address improving their data interpretation skills so they could more effectively utilize data in their roles as team leaders.

Most of my focus has been on data storytelling—how to effectively communicate insights—but in this case, the emphasis was on how to consume or process information and insights. This data interpretation skill gap is more widespread across companies than many people realize. Most organizations have made significant data investments, especially from a technology or platform perspective. However, greater data access via dashboards and self-serve analytics won’t achieve much if managers are unable to interpret what’s being shared with them. A 2021 IDC report on data culture found only 33% of employees reported being comfortable questioning KPIs and metrics used in their organizations.

For the companies that have invested in data literacy training, many managers are content to send their employees to the courses, but they are often too busy to participate themselves. As a result, many organizations continue to see a lack of data-driven decision-making from their management teams. Without better data interpretation skills, these managers will limit what their organizations can achieve with their data investments.

Data interpretation is a core management skill in today’s data economy

Analysis and interpretation are often intertwined, but they are typically separate tasks. Data analysis is about finding significant anomalies, patterns, or trends in the data. Data interpretation assigns meaning to the analysis findings. Essentially, we analyze the data to find insights, and we then interpret what these discoveries mean to specific stakeholders or the business. Without one or the other, it’s difficult to translate data into action. 

With all the demands placed on managers, it’s unrealistic to expect them to routinely perform their own analyses. Data professionals mostly perform this task on their behalf, especially when a significant amount of time may be required to uncover a single insight. Data specialists can also provide leaders with a valuable, independent interpretation based on their extensive time exploring the data. However, executives and managers must remain active participants in the data interpretation process rather than just blindly accepting what others have gleaned from their explorations of the data. 

Retaining the responsibility of interpreting the numbers is important to leaders for two key reasons. First, even when a solid interpretation is provided, it can benefit from being scrutinized and validated by management. Leaders may have additional context and domain knowledge that the data professionals might not have as they form their initial interpretations. For example, data experts may not know about shifts in strategic priorities or initiatives being run by other teams. A manager would be aware of these aspects and how they might be impacting the data being analyzed.

Second, when managers are capable of interpreting the results and inferring what they mean, they can engage in deeper data discussions and develop more creative solutions. Without adequate data interpretation skills, managers cannot build on the preliminary interpretations provided by analysts or data scientists. By leveraging diverse perspectives rather than just narrow viewpoints, group-based reasoning will lead to more robust solutions. By being active participants in the interpretation process rather than passive ones, managers can enrich the decision-making process and have greater confidence in their data-informed decisions. 

Developing the ability to interpret dashboards, reports, and data stories

If your organization is striving to create a data-driven culture, it’s paramount that managers develop adequate data interpretation skills so they can read and work with the data. Leaders set an example for how data can and should be used, especially when it comes to data-driven decision-making. However, if executives and managers are uncomfortable with interpreting data, they will revert to relying on their instincts to guide their decisions—not data. It’s virtually impossible to establish a strong data culture when nobody is leading the way. 

In most cases, managers aren’t expected to work directly with raw data—their primary tools are automated reports and dashboards, which are usually comprised of cleansed, summarized data. These business intelligence tools are more exploratory than explanatory. With filters and drill-down options, a dashboard can act as a basic analysis tool for management. Typically, the information is open to interpretation from each end user. While these tools can provide managers with useful information, they won’t supplement an inability to reason through the results.

With data stories, the data has been curated by someone who has weaved an explanation into a narrative for the managers to follow and act on. In a sense, the data storyteller guides the audience members through an analysis and the interpretation of the numbers. Ultimately, executives and managers can choose to accept, reject, or further inquire about how the data has been interpreted. However, without ample data interpretation skills, they will lack the necessary ability to distinguish between a solid interpretation and a weak one.

The 4 keys to better data interpretation

Before anyone can begin interpreting data, they will first need a basic knowledge of the data. Each company, discipline (marketing, finance), and industry (manufacturing, healthcare) will have unique key metrics and data terminology. The more you understand the underlying data such as where it comes from and how it is calculated, the better positioned you’ll be to interpret it. Without this crucial data knowledge, it will be difficult for anyone to make sense of a report or dashboard. Once you have the data fundamentals in place, there are four key aspects to data interpretation:

1. Data Assimilation

When you’re presented with new data for interpretation, you need to be adept at orientating yourself to new or unfamiliar data before mentally processing it. When you approach a new chart, you want to fully inspect the information that’s being displayed before passing any sort of judgment on it. Mistakes can be made if you don’t first assimilate the information before interpreting it. 

Examples:

• Data: What metrics and dimensions are being displayed?
• Time frame: What is the date range for the data?
• Data source: Where did this data come from?

2. Data Interpretation‍

After you’ve familiarized yourself with the data and how it’s displayed, you can then evaluate and interpret what it means. When you are presented with a new data chart, you're going to examine it for various key features such as interesting anomalies or trends.

Examples:

• Patterns: What patterns or cycles are visible in the data?
• Gaps: Are there any obvious omissions in the dataset?
• Outliers: Are there data points that are detached or removed from the rest of the results?

 3. Data Skepticism

It’s important to be able to step back from the data and weigh other less obvious factors that can influence the results and their interpretation. To think critically about the data, you must evaluate how other elements such as potential biases and incorrect assumptions can shape your interpretation.

Examples:

• Biases: Is your interpretation being influenced by a preconceived notion or preference?
• Comparisons: Is the comparative information shown in the chart fair and relevant?
• Causation: Is a strong correlation potentially being confused with causation?

4. Data Curiosity

The final step is to lean into what you’re observing in the data and determine if any additional questions should be asked. In some cases, you may have the ability to explore the data further in the interface, and at other times, you may pose completely new questions that require more data and time to answer. Recognizing that you may not have sufficient information to make a good decision is also critical.

Examples:

• Outcomes: What is the total impact of this problem or opportunity?
• Extrapolation: If this is affecting one area of the business, could it be affecting others too?
• Context: Is more background information needed to better understand the results? 

Decision-making isn’t an easy responsibility. The added charge of having to incorporate data into the decision-making process can be challenging for many managers who aren’t accustomed to using it regularly. Today, most organizations expect leaders at different levels throughout their business to utilize data in their decision-making. However, without adequate data interpretation skills, many of these managers will continue to struggle with leveraging data effectively in their roles and teams. 

This is a critical data literacy problem that can be addressed with targeted training. When your managers can—with confidence—interpret the numbers in their dashboards and reports, you remove a key bottleneck that will impede the advancement of your company’s data culture. When managers begin using the same data language and lead by example with data-driven decision-making, a stronger data culture will emerge across your entire organization.  

If your company would like to start enhancing the data interpretation skills of its managers or employees, please reach out to me about developing a customized training workshop to up-level their skills in this critical area. I would love to support your organization in its data-driven journey. 

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