Benefits and Complexities of Visualization as a Tool in Predictive Analysis

Napoleon Bonaparte said, “A good sketch is better than a long speech.” In predictive analytics, data visualization presents analytical results as a picture that can be easily used to build realistic, actionable narratives of possible futures. This is because the human brain finds pictures easier to digest than text or numbers.

Narratives based on analytics can be archived and transmitted throughout your organization, helping to form the basis of its approach to its business.

The benefits of visualization

Using visualizations to present the results of your predictive analytics model can save you a lot of time when you’re conveying your ideas to management. Visualization can make the business case for you, providing an instant understanding of complex analytical results.

Another benefit of using charts and graphs is to ease the process of decision-making.

For example, you can use visualizations to identify areas in your business that need attention, as when you show maps that present comparative sales of your product by location and can more easily identify areas that might need more advertising. Doing several such analyses and presentations over time can create a narrative of predicting sales volume by location.

Walking into a meeting with eye-catching graphics rather than spreadsheets of numbers can make your meeting more effective because visualizations are easy to explain to a diverse audience. Meetings can then become opportunities for discussion, focused imagination, and ingenuity, leading to the discovery of new insights.

Visualization can be used to confirm or disprove assumptions made about a specific topic or phenomenon in your data. It can also validate your predictive model by helping you determine whether the output of the model is in line with the business requirements, and the data supports the claims made for the model.

In summary, visualization:

  • Is easy to understand

  • Is visually appealing

  • Simplifies the complexities of the analysis

  • Is an efficient medium for communicating results

  • Makes the business case

  • Validates the output of your model

  • Enables the decision-making process

How to deal with complexities

Visualization may help simplify communication, but making effective use of visualization isn’t exactly simple. Using data visualization to draft the storylines of scenarios that portray the future of your organization can be both powerful and complex.

The complexities of using visualization in predictive analytics can crop up in various areas:

  • Visualization requires a wide range of multi-disciplinary skills in (for example) statistics, analysis, graphic design, computer programming, and narrative.

  • A large body of data that comes from a variety of sources can be unruly to handle. Finding innovative ways to plot all that data — and represent it to the decision-makers in ways they find meaningful — can be challenging.

  • Visualizing analytical results can accidentally convey misleading patterns or predictions. Different interpretations and various possible insights might come from the same visualization.

    To head off this difficulty, you might want to have different analysts discuss these possibilities and their meanings beforehand, in depth; get them to agree on a single, consistent story derived from the visualization before your present it to management.

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