How Your Reports Might Be Misleading You

Organizations often rely on reporting to gather and analyze critical pieces of information, ultimately supporting smarter decision making and greater efficiency. In theory, these reports are perfect, objective tools, providing precise information that the organization can wield effectively.

However, these reports might be misleading you, and they might be fooling you in many different ways at once.

The Good, the Bad, and the Ugly of Reporting

Reporting is critical for most departments in most organizations. It’s one of the only ways to quickly and efficiently convey large volumes of information, and it’s a tool of communication that can expedite messaging between various layers within your departments.

The goal is to take large sets of data and information, then compress them and distill them down to a simplified form. This saves time in multiple ways, giving leaders and decision makers access to quick, consolidated information that they can use across multiple applications.

However, this process can present some problems of its own. Reports with inaccurate information, bad presentation forms, and other issues can leave you with imprecise or false conclusions, or could interfere with your ability to reason.

How Your Reports Might Be Misleading You

How exactly are your reports misleading you?

Bad Data Sources

At the beginning of the pipeline, reports can suffer from bad data sources. If you aren’t gathering accurate information, or if you don’t have a sufficiently large sample, your reports may become worthless. Always verify the legitimacy and reliability of your data sources before you look to reports for forming new conclusions.

Redundant Data

It’s also possible that you’re double reporting certain pieces of data. This is especially relevant in certain types of tracking and analytics software, which can overstate certain phenomena. For example, if you measure web traffic in redundant ways, it could lead you to think you have far more web traffic than you actually have.

Overreliance on Visuals

Data visuals like graphs and charts are effective at communicating complex data sets in simplistic, intuitive ways. However, they offer some striking limitations and can lead you to form bad conclusions. If you only look at visuals at a glance, or if you overestimate their ability to convey data, it could hurt your analysis.

Correlation vs. Causation

Correlation and causation are often confused, but they are distinct concepts that should not be conflated. Just because two factors are associated with each other doesn’t mean that one is causationally linked to the other. For example, seeing an increase in revenue after your company bought a new statue for the lobby doesn’t necessarily mean that the new statue was responsible for your higher revenue. True causes are often difficult to figure out.

Statistical Significance

You also need to think about the statistical significance of the report you’re reviewing, even if you’re not a scientist or a statistician. You may witness a certain trend unfolding, but do you have enough data to extrapolate a reliable conclusion from that? Is your sample size big enough? Have you studied this phenomenon for enough time?

Oversimplification

Reports often aim to simplify things, but they can err on the side of oversimplification. Many areas worthy of organizational study aren’t simply reducible; a complex phenomenon can be properly understood by stripping away all the complexities. Obviously, reports still need some element of simplification, but you need to be mindful of how this is applied.

Confirmation Bias

Confirmation bias is just one type of cognitive bias that can influence how you think about and engage with the world. Essentially, it describes our tendency to overvalue pieces of information that align with our foregone conclusions, while we simultaneously undervalue pieces of information that contradict them. This and other cognitive biases can color how you see even the most objective, otherwise perfect reports – and they can influence how we put reports together in the first place.

Lack of Transparency

Reports need full honesty and transparency to be successful. If you’re making a report, reveal all your data sources and how you created it. If you’re reviewing your report, make sure you understand how it originated.

Lack of Consistency

Reporting also requires consistency, as it’s very hard to understand the full picture of a given area with a single, temporary snapshot of it. Continue reporting at consistent, predefined intervals to better understand what’s happening and why.

The Solutions

Unfortunately, because there are so many potential issues affecting your reporting, there is no catch-all solution. Instead, your best course of action is to thoroughly review your data analytics and reporting processes, closing any gaps you find so that you can make better, more accurate reports in the future. With better tools, better systems, and better people at the helm, your reporting can substantially improve.