We designed the user interface of the popular golf simulator Skytrak. Club speed, ball spin, and launch angle are among the many data points presented to the user.

Enhancing communication

There’s a good reason why data visualizations have become more and more a part of your communications lexicon.

In business settings, this is amplified by the fact that most executives are overwhelmed by an influx of data, presentations, email, meetings, and other communication. To stand out from the noise and present information in a more palatable and memorable format, companies must embrace data visualization as a central internal communications tool and within their consumer-facing digital products. Generative AI can enhance these visualizations, making them even more engaging and insightful.

Data visualizations can be modest formats like bar graphs, pie charts, or even simple drawings that illustrate an idea. These formats can be effective for showing comparisons, trends over time, peaks, ranks, types, components, groups, percentages, and pieces.

More advanced data visualizations can include histograms, scatter plots, alluvials, flow charts, interactive visualizations, and more. These can be effective visualization tools for demonstrating clusters, distributions, relativity, connections, if/then, structure, relationships, and more.

The stage is set for AI

As organizations and customers generate increasing amounts of data, AI is becoming an indispensable visualization tool for making sense of this information.

For AI to be a successful tool in the art of creating data visualizations, it must rely on the best possible data set. AI uses machine learning and natural language processing to digest enormous datasets for the purpose of identifying and correcting mistakes and inaccuracies. This process includes the detection and correction of outliers, missing values, and other anomalies that could skew results.

This capability allows business decision-makers and researchers to gain important insights into customer behavior, market trends, and other relevant dynamics. As an additional AI-powered benefit, utilizing a language model in this context can provide narrative explanations of these trends, making them more comprehensible and actionable.

Further, AI-driven tools like NLP enable the transformation of qualitative data into quantifiable visual representations. This can be of particular value in areas like social media analysis, customer feedback, and sentiment analysis, where vast amounts of text can be converted into meaningful visualizations.

Infinum integrated numerous internal and external data sources to create an elegant interactive dashboard for the hospitality group Maistra, which they use to predict hotel occupancy and autonomously adjust accommodation rates. This is an excellent example of how AI can be used to mine different sources of data that would be extremely difficult for a user to process, synthesize, and act upon without a powerful visualization tool.

The Maistra revenue optimization tool presents data from multiple disconnected sources in an array of easy-to-consume graphics formats from scatterplots to distribution curves. These are all designed to empower the user to make faster decisions on pricing to reduce vacancies and maximize revenue. Machine learning models perform an array of data analyses, predicting the optimal price for every accommodation unit across the portfolio.

Here are just a few ideas for using AI data visualization to power better business decision-making:

For digital experience company FirstTube, our goal was to provide the end-user with an intuitive, real-time dashboard that displayed critical metrics such as concurrent viewers and peak concurrent views, allowing them to make informed decisions on the fly. We aggregated all the data to generate comprehensive visualizations that charted the best-performing platforms, ad placements, total watch time, and total view counts.

Digital products can incorporate AI data visualizations in a number of ways. Here are a few:

The app we designed for The Arbour Smart Homes features numerous data visualizations that are intended to make it easier for residents to gauge their energy consumption and make energy-saving adjustments on the go. Because it’s a deep experience, consistency and cohesiveness help tie everything together and create mental shortcuts when moving between functions.

Best practices for AI data visualization

From forecasting to communications, it’s critical to be precise, meaningful, and authentic. Several best practices come into play, specifically with data and design, for data visualization and common business processes.

Accuracy

Beginning with the data and the source, accuracy is the most important factor. Inaccurate data can lead to incorrect assumptions, conclusions, and messaging.

Relevance

Ensuring the data is relevant to the audience will help build a case.

Contextualization

Contextualizing the data is important as it allows the audience to evaluate and understand its significance. Titles, subtitles, labels, annotations, and explanatory text are beneficial. Comparisons and benchmarks will provide value by showing progress and patterns over time.

Providing actionable insights

Actionable insights can serve as a narrative aid that help users form opinions and make educated decisions based on patterns, trends, and concepts that are highlighted within the data visualization.

Similarly, there are several design best practices for creating and using data visualizations in business and marketing.

Simplicity

As with most design concepts, simplicity is the first and most important element. Avoid overwhelming viewers with too much information, too many colors or fonts.

Consistency

Consistent use of design elements will create a cohesive experience. 

Visual hierarchy

Establishing a clear visual hierarchy will guide the users to view the most important parts of the visualization in the correct order. Labels and fonts (weight and size) can be used to create hierarchy.

Accessibility

Accounting for all users is important, and including alternate text with visuals is central to any visual element.

Keeping best practices top of mind while developing charts and graphs will create a stronger user experience while drawing in the audience.

Proceed With Care

While AI can enhance the process of creating data visualizations, there are also cautions. Companies should not over-rely on artificial intelligence for data visualizations. Instead, combining AI tools with human interface is the best approach.

AI is known for inherent bias, hallucinations, inaccuracies, and lack of tone and voice. It is critical to consider company standards, policies, and ethics prior to using any new technology, visualization tool or AI. For these reasons, use of AI should always be managed closely.

Entering the new frontier with AI data visualization

As businesses enter the new frontier of big data and technology, AI can be a valuable visualization tool. When utilized with care, and together with best practices in data and design, companies can convey complex information in a highly visual and engaging way – whether that’s to the board room or to customers, empowering decision-makers with the insights they need.

Selecting a strong digital partner can provide data visualizations and AI tools to scale a business. If you are interested in utilizing AI in your digital product, check out what we can do to help.

Kelly Cutler is an educator, author, and marketing leader. She currently teaches digital marketing and communication across programs at Northwestern University. An active adviser and consultant, Kelly brings more than two decades of experience to Northwestern. The author of “Search Marketing: A Strategic Approach to SEO and SEM,” Kelly enjoys researching digital marketing, web3, generative AI, and the future of marketing. You can connect with Kelly on LinkedIn.