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Understanding Modern Data Visualization Techniques
Businesses today generate a huge amount of data. It’s known as Big Data, and it’s a goldmine of information that helps companies make decisions and build strategies. But this is only helpful if leaders can interpret the trends.
To take thousands, millions or billions of data points and understand their meaning, and spot a pattern or trend, businesses use data visualization.
Data visualization is the portrayal of data in charts, graphs, maps and other visual tools to help identify trends and relationships in your data. The right chart can turn huge datasets into clear stories on any topic.
Astrato delivers simplified Business Intelligence (BI) for modern data teams, purpose-built for The Data Cloud. In this article, we’ll summarize the modern data visualization techniques you need to know, who uses them, and what they can do for your business.
Which industries use data visualization?
Data visualization is widely used by tech brands in industries like software, analytics, FinTech, and BI. But it’s becoming increasingly important in other areas.
The emerging application areas of data visualizations can be sorted into three main fields:
- Public: news, journalism, publishing, and media. GovTech, law enforcement, public safety.
- Industry: software, analytics, medical, health, geospatial, business intelligence, FinTech health, InsurTech, LegalTech HR.
- Science: higher education, e-learning, STEM education, neuroscience.
Sectors in all areas of life and business are embracing the possibilities of data visualization. So what are the benefits?
The business benefits of data visualization
The human brain can process images 60,000 times faster than it can interpret text. That’s because 90% of information received by the brain is visual. Since we’re visual by nature, we can use this strength to enhance data processing.
Look at it this way: imagine a spreadsheet filled with data. The average human will struggle to pick out trends, patterns, or a data story from the raw data alone. It’s much easier to understand what you’re looking at if the information is presented in charts or graphs. Data visualization allows enormous amounts of complex data to be ‘read’ and interpreted with a glance. It’s far more accessible.
Data visualization provides three main benefits to an organization:
- Increased engagement: Good data visualizations are intuitive. Use them to improve comprehension and make analysis more dynamic, while keeping an audience engaged.
- Improved data accessibility: Help employees find improvement opportunities, adapt strategies and optimize business outcomes.
- Faster interpretation and action: With the right data visualization techniques, you can use performance indicators can be visualized in real-time. This keeps businesses agile and responsive.
Visualizations offer a swift, intuitive and simple way of conveying critical concepts universally. Trends and outliers are easy to spot by anyone using the data. In fact, recent studies found that use of visualizations in data analytics could shorten business meetings by 24% .
Not only that, but a BI strategy with visualization capabilities brings an impressive ROI of $13.01 back on every dollar spent.
Key data visualization techniques
There are many different ways to display data. The visualization technique you choose will depend on your dataset and the story it needs to tell. Here are some of the most important:
Box plots show the distribution of your data, giving insight into skew and outliers. Data is arranged from lowest to highest value and grouped into four parts, known as quartiles, and arranged around the median (the middle value in the dataset).
Box plots are useful when analyzing data like exam results, allowing educators to see how a body of students is performing-a wide distribution might indicate that teaching methods aren’t engaging all students.
Similar to a bar graph in appearance but with the area, rather than the height, indicating the frequency of occurrences. Histograms are useful when data is sorted into groups of unequal width, for example when analyzing dataset with different-sized age groups.
A heat map overlays color onto a scenario to indicate the intensity of activity. Typically, higher values are shown in red and lower in blue. Heat maps are handy when analyzing how visitors view a web page: areas where their eyes are most often drawn are shown in red, and those that gain little attention appear blue.
Charts come in many forms and are among the most straightforward to plot. Common types are line charts, bar charts and pie charts. A line chart shows the relationship between two variables, like speed and time to give acceleration. A bar chart is useful when comparing quantities in different categories, like types of fruit sold by a greengrocer. A pie chart is useful for showing the relative share within a whole, like the proportion of the electorate voting for each political party.
Useful for showing thousands of items at once, tree maps have groups of rectangles arranged by size and color to indicate value and category. Tree maps are used for many applications, including sales or population data.
Word clouds are used to show the frequency of words used in a text or a dataset. They can be useful in giving a sense of the qualitative feedback in, say, customer satisfaction surveys.
A network diagram shows how individual actors within the dataset are connected, using nodes and ties to show relationships. These are useful when analyzing social networks to spot influencers.
Why keeping up with data visualization techniques is key
Keeping up with the latest business technology trends is crucial to modern organizations. In a fast-paced world, standing still actually means you’re falling behind your competitors: your audience will stop talking about you, and your marketing efforts will have less impact. Find the right data source, interpret it, and present it in a dashboard to stay competitive and remain relevant.
Use the latest technology to improve customer retention. Keep customers coming back by delivering a great experience.
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