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Top 5 Techniques for Data Visualization

Gone are the days of pouring over Excel spreadsheets to find key indicators and outliers because AI-driven analytics and data visualization has changed the game. Data science is continually advancing, and today's data visualization software is more user-friendly and accessible than ever. So what are the best techniques, and what can you get out of them?

The Benefits of Data Visualization

They say “a picture is worth a thousand words” because human beings have an incredible capacity to derive meaning from visuals, dating all the way back to our ancient ancestors. It's easier to process information visually, which makes data visualization a powerful tool. It allows us to make sense of vast amounts of data from various data sources and make smarter, faster decisions.

Some of the top use cases for data visualization are:

  • Identifying market opportunities
  • Trendspotting
  • Reviewing performance metrics
  • Analyzing relationships
  • Comparing historical data

Data visualization can also help you improve the quality of a product or service, give you a comprehensive view of your business, and much more. With that said, your visualizations are only as strong as the strategy and techniques behind them. Here are five of today's best data visualization techniques:

1. Invest in data virtualization solutions.

The first step to data visualization is collecting accurate, readable data points from all of your various data sources. Data virtualization wrangles all of your data into a single, user-friendly dashboard to simplify access while supporting API security. Virtualization software can be used with your current systems, including Excel and SQL servers, and it has native support for Javascript, SQL script, and more. By hiding the IT complexity and presenting business-friendly data views, you no longer have to be a data scientist to read large sets of data.

2. Utilize AI-driven advanced analytics.

Most organizations have silos of unstructured data that remain untapped because the datasets are too vast and complex to break down. The good news is that AI-driven visual analytics make big data analysis easy and accessible. Using AI and machine learning, advanced analytics can process big data, find key insights, and generate visualizations. This information is presented in readable, interactive dashboards that allow businesses to expand the number of users and empower their entire team.

3. Choose the best data visualization tools.

There's a lot of data visualization software you can choose from, many of them open-source. Even software that's strictly for commercial use usually has a free plan as well. Choosing the best data visualization software depends on what visualization and business intelligence tools you need — some are more advanced than others.

Some common data visualization tools are:

  • Comparison infographics
  • Pie charts
  • Bar charts
  • Histograms
  • Interactive charts
  • Tree diagrams
  • Scatterplots

Which tool should you use? It depends on your goal. Scatterplots are a great tool for identifying trends and outliers, while bar charts are useful for comparing metric values. Choose the tool that will best communicate your intended message.

4. Use interactive visualization.

With interactive visualization, you can do away with the guessing games and get the answers you need in real-time. From a single dashboard, you can manipulate and interact with data in different ways for a customizable analysis. Use interactive charts in reports and presentations to grab attention and engage your audience. We use interactive maps to track COVID-19 cases, election results, and much more — so you can see just how valuable these tools are when it comes to delivering insights on rapidly changing data.

5. Know your message and keep it simple.

It can be easy to get carried away with all of these new data visualization tools, but the best way to approach data visualization is to keep it simple and concise. It doesn't matter how eye-catching your visuals are if your key points aren't easy to understand, so make comprehension your number one priority. Keep your goals in mind and let the important data shine through.

In today's data-driven world, you need to have a strong data analysis strategy to keep a competitive edge. Stay ahead of the curve and make sure you're using the best data visualization tools and techniques to analyze and present your data.

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