In 1976, Saul Steinberg illustrated, “A New Yorker’s View of the World” for the cover of the March 29 edition of The New Yorker. The illustration shows Manhattan as the center of the world, with New Jersey still in the Stone Age, and it has been much parodied since.
Steinberg’s satirical illustration is a constant presence in my mind whenever I think about data visualization. Different users in a company see the world differently, so it becomes incumbent for a business analyst to know that a salesperson might want to see how many sales were consummated in a month, but a customer service rep might be more interested in how much customer churn occurred because of a bad experience with the company.
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Both of these visualizations represent different sides of the same event. In other words, you might see the world with a belief that New York is the center of the universe, but the world takes on a different look if your reference point is Dallas or Bangalore.
When data is visualized for analytics, it takes the form of a chart, a diagram, or some other type of pictorial representation that tells a story in a single eyeshot. The caveat is that the individual user viewing the visualization must understand both its reference point and its meaning—to the point where a business action can be taken.
This is why developing the right kind of data visualization for each analytics application is one of the most important skills that business analysts can bring to the table when they’re working with end users. It is great data visualizations that produce great business decisions.
“Visual analytics does the ‘heavy lifting’ with data, by using a variety of processes—mechanical, algorithms, machine learning, natural language processing, etc.—to identify and reveal patterns and trends,” said Julie Zuckerman, a senior product marketing director at Sisense. “It prepares the data for the process of data visualization, thereby enabling users to examine data, understand what it means, interpret the patterns it highlights, and help them find meaning and gain useful insights from complex data sets.”
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What do business analysts who work in the data science and analytics field need to do in order to fine-tune their skills in data visualization? Here are some steps.
1. Understand the business issues in which users are trying to gain insights
Putting yourself in the user’s shoes is the best way to do this. Why is COGS (cost of goods) such an important piece of information for a financial analyst? Why does the cost of production line downtime weigh so heavily on a plant supervisor?
Being able to empathize with users and feel their pain assists you in developing visualizations of data that speak to their central business issues.
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2. Choose visualization mechanisms that make sense to your users
A spreadsheet analysis might make sense to a financial analyst, but be just so much “noise” to a production supervisor. By knowing your audiences, and the types of visualization mechanisms that are most relevant to them, you can optimize the information that the analytics are conveying. This will speed your users’ times to decision.
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3. Work with technical IT to obtain the data that you need
In order to shape your data visualizations, you need to work with more technical IT team members to uncover the data sources from which the data that you need can be drawn and then integrated into an analytics composite.
In the case of a map that shows at-risk vendors in your supply chain, you may need to obtain climate, financial, political, disease, infrastructure, and other data in order to identify the most at-risk suppliers, and then plot them along the supply chain routes of your company on a worldwide map.
SEE: How to create your first Tableau Software data visualization chart (TechRepublic)
4. Practice continuous process improvement
Today’s data visualization might not hold for tomorrow. As business changes, new metrics and data visualizations will emerge. It’s important for business analysts to touch base regularly with users and to keep their fingers on the pulse of the business to ensure that the data visualizations being used are still relevant.