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The Definitive Guide to Data Visualization
We are living in an age of data. Data is essential in our everyday lives, and businesses and customers use it to guide important decisions. You can see data visualization examples all around you – from the activity tracker you wear to the app your energy company has to measure your usage.
Data visualization tools also shape marketing strategies and purchasing decisions, and help companies streamline processes. Raw data can be hard to interpret which is why having a tool for data visualization is so important.
With data all around us, it is important to present information in the right way. If you want to catch the eyes of decision-makers and present convincing arguments, your data needs to look clear and compelling.
At Astrato, we specialize in helping leaders understand data so companies can confidently make business-critical decisions. This guide will show you how to create fantastic data visualizations that capture attention and convey a message.
What are data visualizations?
Many business leaders believe that data visualization is complicated because they don’t know what data visualization is. Data visualization is a way of presenting large volumes of highly granular data in a form that’s easy to understand. It turns data into charts, graphs, and other visual media that anyone across a company can use.
Businesses generate vast amounts of data, collecting Key Performance Indicators (KPIs) like sales revenues, price indices, or employee performance. Every day, millions of lines of data come from all the automated processes within a business – think of the systems monitoring stock holdings or the software tracking consumer behavior and basket values.
Data is how businesses make decisions, optimize performance and outstrip the competition. But sifting through thousands of data points to make sense of them is demanding. It isn’t a good use of business leaders’ time. Instead, tools for data visualization are used so executives and C-suite teams can understand information at a glance.
A graphical representation of your data – a graph, a chart, an infographic – helps decision-makers see trends and patterns. Data visualization software uses color, shape, and animation to grab attention and tell a story, so business leaders can make decisions that take the business toward its goals.
A study by Bain & Company found that businesses with the most advanced grasp of their data are outperforming competitors. They use data to make decisions faster, carry out those decisions, and reap the financial rewards. Ultimately, they’re likely to become more profitable than other brands in their sector.
But data visualization isn’t simply making your graphs look a little fancier or creating an infographic. It’s about getting the right message across in an intuitive way.
What to consider when using a data visualization tool
When using a tool for data visualization you must maintain the balance between conveying a clear message and presenting that message in an eye-catching way. There are important points to consider before you start.
Defining your audience
Who will be using your data visualization? Defining your audience clearly reminds you of what’s most important to them. What do they need to know? What will help them make decisions?
Creating a data visualization will convey meaning far more effectively if the message is targeted well. For example, a sales director will be most interested in learning how sales force members are performing or which key accounts are showing growth YoY (Year-on-Year). Dispatch times, though important for order fulfillment, are less likely to concern them and more appropriate for an operations director.
Be clear on the purpose of your data visualization
When you create a graph or an infographic, what are the key takeaways? Making these prominent means your data visualization brings insight and value to the business. So ask yourself, what is the most appropriate way to show the data?
Choosing the right visual representation is essential. For instance, sales performance over time would use a line graph, where data points are plotted and joined in colors that represent different accounts. If the important information is about team members, a bar graph showing comparative performance would be a better choice.
Companies have been tackling these comparatively straightforward problems for generations, but we’re now in the era of big data. Businesses today generate and collect billions, or even trillions, of data points, like eye tracking in market research or how sports fans flow around a stadium.
With so much to analyze, businesses need to know they have good data. It has to be sourced carefully.
Sourcing data for data visualizations
Volumes of data handled by organizations are growing every day. Business leaders want to make the right decisions about the future of a company, so they need certainty that the data is complete and accurate.
Inaccurate or incomplete data can skew results, leading to poor decisions. If executives don’t trust the data, it’s difficult for them to make any decisions at all. When using a data visualization tool, the data you input must be reliable.
To create reliable data visualizations, consider how the data are collected (methodology) and interpreted (confirmability). When done correctly, these elements combine so that anyone examining your data could follow a process and get the same results. It means that data over time gives a fair comparison and helps prevent bias from creeping into your datasets.
What data do you omit when creating data visualizations?
When creating data visualizations, it’s essential to show a clear, accurate picture to get a message across and tell a story. For skilled data analysts, that might mean removing anomalies and outliers from the data – they’ll distort the results and give an inaccurate view.
For example, say there’s a breakdown partway along a production line. Further down the line, you’re monitoring the rate at which completed products are packed into boxes. A breakdown that takes a day or more to fix but that only happened once could lower averages and give an inaccurate picture of overall productivity. Purchasing teams could set the wrong ordering cycles, meaning just-in-time deliveries don’t arrive when needed. In this instance, it would be better to omit the anomaly from the dataset.
However, it’s just as important not to cherry-pick data. Cherry-picking is where data is presented or withheld to meet a specific agenda – usually making an organization look good. If the results of your customer experience survey show your customer services team is rated as polite and helpful, that sounds positive. But if you’re not reporting that customers were experiencing long waiting times to report faulty products, you’re giving a highly inaccurate impression of the truth. Your organization is held back in improving performance.
How will the data visualization be presented?
Now that you have clean, accurate data, it’s time to think about how your data should be visualized:
- Use one visualization for each message rather than trying to combine several takeaways.
- Choose the right chart to tell the story. Does your data show a trend over time? A market share? Relative values or volumes? Each of these requires a different type of visualization to show the message clearly.
- Use color in your visualizations. It’s eye-catching, but it also makes your message more accessible. Assessing relative basket values across competitors? Use their brand colors to make an intuitive read.
- Color can also draw attention to the most important part of the chart – for example, the most recent performance data. But make sure colors don’t become a distraction. A busy chart is a confusing chart, not a clear one.
- Label your data and use text to draw out important information so users can understand the key message. But keep font sizes appropriate and be sparing in the text you use. In good data visualization, less is usually more.
Are your data visualizations credible?
Leaders throughout a business need to be able to rely on accurate, representative data to make decisions. When employees have accurate data, they can work more efficiently and on more interesting and valuable tasks than data checking and correcting.
So how can you ensure your data visualizations will stand scrutiny? In addition to the strong methodology and the confirmability mentioned earlier, there are other ways to keep data visualizations credible:
- Triangulation helps to confirm data accuracy. This means collecting data from several different sources and methods to test for consistency – think of it as checking your answers.
- Storage can cause problems if it isn’t appropriately maintained. Hardware and software should be regularly updated to keep data accessible and safe from corruption.
- To optimize your analytics capabilities, data ownership must be spread across business functions to ensure all areas have access to the same data.
Does your business have robust methodologies for data collection? Is it stored and accessed appropriately? Can you be sure there are no biases? Do your visualizations present a true picture? Examine processes and practices regularly to keep your data visualizations credible.
What problems do non-credible data visualizations pose?
Without credible data, businesses expose themselves to costly mistakes. Firstly, there are the biases we mentioned earlier. These can result from simple human error or poorly throughout collection processes. But at their worst, data skews are caused by unconscious bias – a legacy of historical views or practices leading to underrepresentation or disregard for groups like women or ethnic minorities.
Secondly, there’s the problem of outdated data. Consumer data trends change fast, and different departments can end up working with different data sets. When this happens, it creates friction and misunderstanding, causing issues in areas like marketing and product development.
Without credible data visualizations, decisions are made with incomplete information. Companies risk wasting valuable time and money by pursuing the wrong strategies.
Data visualization is crucial to leaders who want to make the right decisions for their business. Good data visualization allows vast quantities of data to be presented clearly and simply, so businesses can adapt their strategy and tactics faster in response to changing trends. According to Bain & Company, companies with data visualization are five times as likely to make quick decisions than their competitors – and once those decisions are made, they are three times more likely to action them.
Empowered businesses use data visualization to unlock big data without the time-consuming and inaccurate processes of manual analysis. Ready for robust and credible data visualizations? Book a demo to see how Astrato can benefit your business.
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