Interpreting Business Data

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This Skill, Interpreting Business Data, will present methods and practices for interpreting different types of business data and how these data can inform organizational strategy and decision-making. Data interpretation assigns meaning to the information being analyzed by determining significance and implications. This process helps stakeholders make sense of quantitative numeric measures and contextual qualitative data for the purpose of developing valid and informative conclusions. Upon completion of this section, you should be able to:

  • Identify what data interpretation is
  • Describe the different dimensions of consumer data
  • Outline business data considerations and interpretation strategies
  • Understand common data interpretation problems and challenges
  • Recommend specific data interpretation mediums to others

Whether employed at a large corporation or a small local business, data interpretation methods are quickly becoming an essential skill set as data becomes more accessible and paramount in day-to-day business operations. Big data, the cloud, and the proliferation of accessible dashboard technologies present the perfect environment for businesses to make good decisions, unless data are misinterpreted. Data misinterpretation leads to false conclusions with potential negative implications for the company’s bottom line.

One doesn’t need to be a data scientist or possess advanced data analysis skills to interpret data effectively. Data interpretation is about utilizing some key skill sets and principles to read data and visualizations effectively for the purpose of making informed decisions. Some general questions and parameters to guide this work include:

  • What are the data presented attempting to communicate?
  • Have the data analysis and presentation been conducted with efficacy?
  • Have I considered all of the implications associated with the data?
  • Which biases do I bring to data interpretation, and how might my feelings, emotions, or experiences impact any conclusions I draw from the data?
  • How could these data be presented or communicated with others from an impartial perspective?

Here are some general tips to get you off to a great start with data interpretation:

  • Always start by reading the title of the graph, chart, or other graphical representation as it often provides a summary intended to aid your data interpretation.
  • Then search for a key and units of measure such as the number of research participants to quickly understand the magnitude of information being shared. If a statewide survey captures 200 responses, it probably isn’t representative of the population, but if a small town captured the same 200 responses, it might be considered valid, reliable data from which to make decisions.
  • With the title and units of measure in mind, continue by examining the general trend such as any central line or other patterns in the data to help inform your conclusion about what’s presented. Is the slope positive or negative? What are the highest and lowest values? Be sure to look for data points that don’t seem to fit the trend.
  • A final step within the data interpretation process is synthesizing and triangulating across data sources and including any local or relevant data you or others have to assign meaning and take action based upon the data. Some potential questions and conclusions to guide this reflective process might include:
    • Are these data in alignment with our expectations? If not, we might need to explore additional data sources to validate these findings or implications before considerable investment in organizational change processes are made.
    • Does the data trend warrant action? Two new sales representatives were added in January of the current year and sales were up 100% at the end of the year, but sales growth for the previous year was 200%. It probably doesn’t make sense to add more sales representatives based on a smaller sales increase with more sales representatives onboard.
    • Could these data have multiple interpretations? Be sure to consider how different audiences might interpret these data throughout the data interpretation process.

With these frames and considerations in mind, you will be prepared to carefully and objectively interpret data and evaluate its implications for the organization. The remainder of the data interpretation skill will focus on pragmatic application of the principles identified above.

Further Reading

Byrne, D. (2017). Data analysis and interpretation. Project Planner. SAGE.
Cairo, A. (2019). How charts lie: Getting smarter about visual information. W.W. Norton.
Dykes, B. (2019). Effective data storytelling: How to drive change with data, narrative and visuals. Wiley.