For Part 1 of this series, please click here.
Business intelligence is about turning data into insights. So now that you have all of this data—from sales and marketing data to customer engagement and behavioral data—how do you actually turn that into insights?
Ideally, you want to be able to find trends, patterns, outliers, anomalies and other insights that can help you make data-driven business decisions and give your organization a competitive edge. But to do that, you need to be able to ‘visualize’ the data.
Think of an Excel spreadsheet, with endless rows and columns of data points. The data is there, but trying to make sense of it requires time, patience and, oftentimes, an extra-large cup of coffee (or two). But pulling that data into a bar graph or pie chart can help us understand the bigger picture.
A BI platform can rapidly sift through enormous amounts of data—much faster than a mere mortal can—and turn it into any number of visualizations. This is ideal for non-technical users and democratizes access to business intelligence, often eliminating the need for data analysts for everyday tasks.
What is Data Visualization?
It’s important for users to be able to access data, but it’s equally important that they understand how to interpret that data. That’s where data visualization comes in. Seeing data come to life in colorful, interactive visuals can help us tell a story with that data.
This isn’t just important for the CEO or line-of-business manager—it’s important to any employee working with data.
“Data visualization is one of the steps of the data science process, which states that after data has been collected, processed and modeled, it must be visualized for conclusions to be made,” according to TechTarget.
For example, a marketing team could use visualizations to track campaigns and metrics, such as open rates, click-through rates and conversion rates. Logistics companies could use them to determine alternative shipping routes during a storm, while healthcare providers could use them to track illness mortality rates in certain regions or age groups.
Data Visualization Techniques
Common visualization techniques include pie charts, bar graphs and maps, but there are many other options that can help users visualize data. But, it’s important to pair the right data sets with the right visualization technique (which could require some basic user training).
For example, “you need interpretative skills and an appreciation of which graphics will provide what kinds of information,” according to an article in the Harvard Data Science Review. “There is so much that can be varied: the variables displayed, the types of graphics, the sizes of graphics and their aspect ratios, the colors and symbols used, the scales and limits, the ordering of categorical variables, the ordering of variables in multivariate displays.”
If you’re looking to compare variables within or between groups, then a bar or column chart might make sense. If you want to show the composition of data, then a pie chart, donut chart or treemap (which displays related hierarchical data in ‘nested’ rectangles) might make more sense.
But there are many other types of visualizations to choose from.
If you want to explain the relationship between different data sets, consider a scatter plot (which shows the relationship between variables) or a bubble chart or cloud (which displays circles of data on a two-dimensional plot).
Or, if you want to show how data has changed over time, you could opt for a line chart or area chart. To plot geographical data, you could choose a map or a heat map (a geospatial visualization that displays data as different colors).
The Role of Dashboards
Another important component of data visualization is the dashboard, which provides an overview of data from different sources all in one place—like the dashboard of a car. Dashboards are highly customizable and interactive so, for example, you can pull together various business metrics and visualizations, like graphs and charts, to tell a story with the data.
Automatic dashboards can track key performance indicators (KPIs), so you can monitor, measure and analyze related and relevant data such as organizational performance. Basically, it makes it easier to see the big picture, so you can spot trends, patterns and outliers—or dig deeper into the data if it brings up additional questions.
A Few Things to Keep in Mind
While there are a lot of upsides, it’s important to note that data visualizations are only as good as the data behind them. As a starting point, you need to be working with clean, high-quality data. If a visualization is based on inaccurate or biased data, then any conclusions drawn from that visualization will also be inaccurate or biased.
Many data visualizations are fairly straightforward. But in some cases, users may need to interpret data and infer conclusions, so it’s possible they could make inaccurate assumptions or misinterpret data. For example, they may need to consider what data has not been included, which could potentially skew the results. That’s why it can be helpful to provide training and resources to users in choosing and interpreting visualizations.
Ultimately, data visualizations help to ‘curate’ data into a form we can easily understand and share with others. This can help us see ‘hidden’ relationships between data sets and bring insights to the surface. Stay tuned for Part 3 of Data Insights 101 to learn more about practical applications and impact!
Vawn Himmelsbach
Vawn Himmelsbach is a writer and editor specializing in enterprise IT, writing for national newspapers and technology trade magazines on everything from AI to zero-day threats. She also spent three years working abroad as an Asian correspondent, covering all things tech.
