A few months ago, we talked about the four methods to overcome your company’s big data. Because you know what? Raw data can be intimidating. Looking over a collection of unstructured data can make you feel like there must be a message in there if only you are smart enough to coax it out.
You have to recognize that not all data can be transformed into something useful. That’s why we narrow down the data based on three essentials and visualize it based on one of three goals.
The Data Essentials
Before you even think about visualizing the data you have, you must make sure that it is all three of these:
The data must be original enough to provide new insights. If it is too old, you won’t be able to use it. The type of visualization impacts the expiration date, so you may have to revisit this.
For example, older data may still be valuable for Error Detection or Verification visualizations but less valuable for most types of Learning visualizations. You’ll learn more about these visualization types below.
Data must also be pertinent to your audience. Start by understanding what matters most to those who will be evaluating your visualization.
Contextual data is the most important consideration because data is not valuable outside the context of the metadata that defines where it came from. You need to know who collected the data, how it was done and what assumptions were made in compiling it. Otherwise, you will open yourself up to misapplication of the data and incorrect insights.
As you can see, only a small fraction of the data you have now will be useful at any given time.
3 Goals of Visualizations
The next step is to decide which of three functions your data visualization will serve:
1. Learning — This includes standard financial reporting, as well as projections about how those systems are likely to develop. Best case and worst case scenarios in project management commonly use these types of visualizations. Here’s a link for an introduction to more than a dozen data visualization tools for learning, including some that require no coding experience whatsoever.
2. Error Detection — Before you run statistical analysis on the data to find outliers and relationships between variables, do an initial visual check. People are visual first, so these exploratory data analysis (EDA) techniques can turn up anomalies quickly. Your goals are to test assumptions and assess which models you will use going forward.
3. Verification — In the past, business leaders had to make life-or-death decisions based on gut feelings. Intuition still plays a role in the data-driven world, but profitable companies typically verify initiatives based on solid data. “Accenture” states that “to ask the right questions, analysts and decision makers alike need a deep intuitive understanding of the organization, its strategy and its objectives.”
Making the most of the data you can access starts by not wasting time with data that could pull you off course.
Determine the strength of the data you are working with, and know what you intend to do with the data visualization at the end of it all. Do this first, and you will be able to accomplish much more with the data visualization software you have.
More and more companies are becoming data-driven organizations. To stay ahead of the industry’s changing landscape, continually hone your data interpretation skills so you can stay ahead of the curve.
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