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Data analytics is more than just the consumption and processing of raw data. It's explaining a situation or telling a story about why something has or will happen. As a consequence, one of the most important aspects of the process is visualizing the data in a way that makes sense to the stakeholders that will view this information. Platforms such as IBM Cognos BI offer compelling software that can perform visualization tasks easily, sharing information in a way that makes sense to both a regular employee and an executive. However, creating a visual result means more than just throwing up a chart. Analytics professionals must strike the right balance in presenting their findings to their audience.

Context is king
One of the first steps analytics should deliver is a result that makes sense in the situation it's delivered. How people receive the data will affect their decision-making ability. Consequently, context matters in a very important way. The Harvard Business Review suggested that visualization should take a different shape depending on whether it's delivered in a presentation or as a deck within an email. A presentation gives little time for the receipt of information. Thus it's essential to draw out bigger points in the graphs or charts displayed. On the other hand, a deck will give people time to actually view everything. In that situation, data scientists should provide more details on their findings.

How are you visualizing your data? How are you visualizing your data?

Similarly, results should have a benchmark that people understand. Applying consistent metrics, along with ideal goals, can help stakeholders understand the situation more clearly, according to GoodData. Utilizing colors in relation to those measurements and milestones can help.

Using the right visuals
Not all data visualizations are created equal. Data scientists should understand that people can interpret specific bits of information differently if using different charts or graphs. A pie chart may not deliver a critical point as effectively as a bar graph can, for example. With this in mind, visualization company Tableau suggests looking at the information and determining what works best. A line graph is useful in identifying trends, while a bar graph can provide comparative analysis for different products or services. A pie chart works best when comparing percentages and shares, but they may not work if either of these have an overlap, such as multiple-answer surveys. Scatter plots and bubble charts may work best when determining variations on a specific item. In choosing the right visual and context, businesses benefit from getting the information they need out of analytics.