Visualization Best Practices for Business Analysts

Print Friendly, PDF & Email

As data gets bigger and bigger, and becomes an increasingly more substantial part of how businesses operate, it also becomes increasingly more crucial that organizations are equipped and prepared to best deal with that data.

We’re past the point of having to argue the merits of data — it makes or breaks a business. The problem is ensuring that businesses can actually extract actionable, meaningful takeaways from the data.

What visualization does is clarify what would often otherwise be complicated and murky given the way that large amounts of data can be hard to classify. This is what you need to know about ensuring visualization is an asset and not a point of confusion for an organization.

Recognize the Power of Visualized Data

A business needs to first make a point of recognizing how visualized data is processed and what types of things need to be taken into account when a team relies upon to form ideas and opinions about the business.

The same components that make visualization so powerful also mean that its interpretation needs to be approached with a specific strategy. Big data isn’t just a risk because of cyber security threats and the subsequent data recovery challenges, but also because it can be so hard to accurately decipher.

Studies show that the use of visual aids like graphs allow individuals to absorb information faster, understand the implications better, and remember it longer. That’s all good — it’s why visualization is useful — but it can also cause your audience to draw incorrect conclusions about the data.

It’s vital that organizations recognize the potential visualizations have to throw them off the correct trail.

Often, people look at the dashboard and think that the groupings are necessarily the cause of the outcomes,” said Steven Franconeri and Joel K. Shapiro for KelloggInsight. “That’s really dangerous. If your dashboard shows that customers using your app spend more than customers who don’t use the app, it’s really easy to let a great visualization convince you that the app is the cause of the increased spending. You can easily forget to think, ‘maybe there’s something else causing the difference.’”

It’s critical that the way the data is presented does not create tunnel vision. It should inform in a manner that companies are able to have a more wide-ranging view of their operations, and not a deceptively simple one.

Visualization Should Guide Statistical Evidence

In addition to ensuring you’re not over diluting the message your visualization is conveying, it is also important to remember that the ways your organization interprets data are all working together in a manner that makes for an effective and accurate translation. Organizations are likely to run into trouble when they fail to rely on multiple methods of analysis.

Research demonstrates that data sets with the same summary statistics of mean, standard deviation, and correlation can actually convey radically different information. But, because the statistical markers are similar, many would be quick to conclude they are the same or similar. Only via visualization is one able to recognize their distinctness.

As Justin Matejka — who wrote the research paper — told Fast Company, “There’s still the impression that creating graphics or visualizations is really just making pretty pictures and the real stuff you need to do can be done through analysis. Even if you’re very good at statistics, you might miss something.”

Most of us know, even just intuitively, that one of the top non-technical skills for professionals to have is adaptability. It’s crucial professionals are constantly readjusting their understanding of how to leverage visualization so that they maintain relevancy and take advantage of the opportunity for further clarity. An antiquated view of translating data will almost always mean not utilizing it to its fullest potential.

Choose the Visualization Method to Best Serve Your Data

If you recognize the likelihood of misinterpreting visualized data, and you’re prepared to view all forms of data presentation as complementary to each other, the other natural and necessary component is ensuring visualized data is presented in the best possible format.

Data presented in mass format is almost never helpful. What is helpful is identifying specific problems or questions and utilizing the data to arrive at solutions. Those questions should drive the way your organization visualizes the data.

If you’re comparing values consider:

If you’re analyzing data trends consider:

If you’re showing the composition of something consider:

Given the challenges associated with proper data representation, it is important that a business is willing to employ those schooled in business analysis. A professional in analytics will consider the goals of the business as a whole, and from there decide the best format for visualizing the data, and will be able to design the most accurate picture for the company.

Visualization as a process is a best practice for businesses, but it is not without its challenges. Thus, it’s crucial organizations refrain from simply filling in data points in a program and taking the automated pie chart at face value.

Instead, the key is for educated analysts to curate the data used, and to present that information alongside other methods of interpretation that will effectively allow for accurate, relevant insights; the kinds of insights that can change the game for a company.

About the Author

Avery Phillips is a freelance human based out of the beautiful Treasure Valley. She loves all things in nature, especially humans. Leave a comment down below or tweet her @a_taylorian with any questions or comments.


Sign up for the free insideBIGDATA newsletter.



Speak Your Mind