Why Finance Teams Need Data Visualization
At companies large and small, finance teams are managing a growing volume of data flowing in from many different sources across the business. Technological advances like machine learning are helping software utilize data at scale, but once it gets to humans, we still need help working with big data.
For that reason, data visualization is entering a new prominence in financial operations. Charts, graphs, maps, and other graphic presentations of information let human analysts see insights that would otherwise be invisible. Visualization also offers a powerful conceptual framework for approaching data work. It turns ordinarily esoteric data analysis into clear and impactful stories that resonate with company decision-makers.
Data visualized is data digested. Here’s why this technique for approaching and presenting information is essential to the work of tomorrow’s finance team.
Volume, velocity, and variety
If visual skills haven’t always been associated with finance, it’s because the need to process big data has become an emergency only recently.
In the next few years, our world will be swimming in a data deluge. The “total size of the digital universe” is already starting to double every two years, and enterprises will host about 60% of that increase. Managing and interpreting that data will be an enormous job — and likely one for which Finance will be responsible.
The growth in big data is commonly said to occur along three vectors that each demonstrate why visualization helps humans consume information.
Volume. By 2025, the world’s servers will be responsible for storing and retrieving about 163 zettabytes of information. Rendering data into charts and graphs helps human brains contend with the concept of that volume—its relative size, its growth, its salient qualities—much easier.
Velocity. Data is also being created at an accelerating rate: about 1.7 megabytes per living human per second, increasing tenfold over the next decade. The growing speed of the data firehose requires analysts to establish today the frameworks and techniques that will funnel it towards productive management. By helping people understand data today, visualization makes it more likely that new information will be helpful, not overwhelming.
Variety. Given that 328 million new devices are being connected to the internet every month, traditional data sources are expected to contribute a paltry share of the total information generated in a few years. That’s why it’s so helpful that visualization clarifies the distinctions and correlations between different types of data. It’s essential when trying to spot the relationship between X axis data and Y axis data, for example.
Creating graphics demands good analysis
Visualization may seem like an old-fashioned way of accessing data, but it’s actually a modern technique that arose in response to the analysis required for industrial automation. It was only fifty years ago, for example, that French cartographer Jacques Bertin developed one of the foundational theories of modern dataviz: the “principle of effectiveness.”
Bertin’s idea holds that data should be represented with the visual form that will most effectively express the data’s meaning. In order to determine that, of course, creators need to ask hard questions first: What does this data say? What is the most important takeaway to draw from it? What can I cut out?
As modern graphing guru Scott Berinato says, this process is basically editing. The same expertise a lawyer uses to mold evidence into a case, or that a reporter uses to render facts into news, a numerate analyst uses to craft data into a compelling visualization. Adhering to Bertin’s principle and other charting best practices requires a level of rigorous comprehension that serves the data analyst well beyond the creation of the graphic.
Data becomes more influential
Not only is data easier to understand in a visual summary, it is easier to communicate as well. Good visualization turns static data points into a cohesive story that anyone can understand. Visualization retains the integrity of the source data while making the insights it contains, literally, visible.
And making numbers visible is important. Research confirms that people tend to be better at understanding numbers, and especially groups of numbers, as visual representations rather than as digits on a page. If you’re trying to show trends, averages, correlations, amplitude, or pretty much any other insight, visuals will substantiate your analysis powerfully. If you’re presenting to the CEO, with the goal of delivering a succinct, convincing story, visuals are essential.
Literacy, numeracy, graphicacy
The best reason to embrace data visualization might simply be that if you don’t, you’ll be left behind. The ability to show information graphically is so valuable in our data-overloaded environment that the skill now has its own word: graphicacy. Call it what you will; it’s on the rise, and it’s going to be the new norm in any discipline that works with numbers.
As data proliferates, the influencers of tomorrow will be the human analysts who can use its visual representations to tell stories. Part of that power will come from the ability to convey information to important stakeholders. But just as powerful will be the impact that data visualization has on the way your team approaches sets of data. Their analysis will have to be deeper, more careful, and more convincing in order to generate good dataviz.
Finance teams are already poised to find those insights. Just make sure you can share them, too.