Three Best Practices to Avoid Errors When Using Data Analysis to Drive Success

team looking at statistical data

As we enter an age of information, our world is driven by data in many ways. Across industries, businesses use it to gain or maintain their competitive edge. Professional sports organizations change their training, and even the way games are played based on data-driven insights. But with so much reliance on the numbers everywhere you go, it’s essential to use these best practices and avoid costly mistakes.

Conduct more tests

Even if you try your best to be objective, it’s always possible to fall into the trap of believing that a set of data points proves something that’s in line with your expectations. Confirmation bias is a part of human nature, and even the best researchers can make this sort of mistake at times. Similarly, it’s easy to mistake correlation for causation. Putting two trends together and seeing that an increase in sales coincides with an increase in website traffic for a particular keyword indicates correlation; it doesn’t immediately imply causation.

Further tests are necessary to refine data and avoid the potentially dangerous mistake of jumping to premature conclusions. Online data collection tools facilitate the process of gathering a large number of data points while controlling different variables. From there, you can conduct hypothesis testing to determine if causation can be established and be certain that data is really confirming what you want to hear – or might be indicating otherwise.

Study the methods

Data analysis is rarely undertaken by organization leaders themselves; not everyone possesses the requisite skills or familiarity with an ever-growing array of tools available to the modern statistician. Even if your organization outsources this considerable task to professionals, it’s essential to understand the capabilities and limitations of the tools and methods used.

office team working together

For instance, in the field of search engine optimization (SEO), businesses analyze data and invest in SEO strategies tied to specific keywords. This is done under the assumption that those keywords drive up the search ranking. But different rank tracking tools can be in disagreement over results for the exact same keyword, even within the same time frame; this might be due to the underlying algorithms used, which could parse content differently or be affected by the tool’s server location and its corresponding Google data center. It may be a small discrepancy, but it can have huge implications if businesses make their decisions without fully understanding these tools.

Review the context

Research isn’t carried out in a vacuum; an organization may require analysis to be conducted by focusing down on a specific aspect of operations. The assumption here is that while the initial research inquiry may have a narrow focus, results will still be reviewed in the broader context; unfortunately, due to reasons such as time or logistical constraints, you’ll occasionally encounter errors stemming from using incompatible context or ignoring it altogether.

An example of this would be the measurement of trends over time. A retailer looking to engage consumers makes improvements to its website, which drives traffic up 30% from the previous month. Before assuming causation, the company should compare this with trends from the same period last year and measure the deviation from expectations; the change could be a seasonal pattern, or (expanding to a broader context) it could be driven by external factors, such as promotional campaigns being undertaken by one of the popular brands the retailer carries.

Data analysis is a powerful tool in our increasingly information-driven world. Learning how to be thorough and use it effectively will help avoid critical errors when making decisions based on these results.

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