Data visualization refers to the presentation of data in graphical form, such as pie charts. This helps users recognize patterns more quickly. Interactive visualizations allow decision makers to drill down to the level of detail. This change in presentation allows users to see the facts behind the analysis.

Here are seven ways data visualization can influence business decision-making and strategic adjustments.

1. Move faster

The human brain processes visual information much more easily than written information. Using a chart to summarize complex data ensures that relationships are understood faster than with a cluttered report or spreadsheet.

This provides a very clear way of communicating, allowing business leaders to understand and process their information faster. Big data visualization tools can provide real-time information, making it easier for stakeholders to evaluate the entire enterprise. Faster adjustments to market changes and rapid identification of new opportunities are competitive advantages in every industry.

2. Discuss the results in a constructive way

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Many business reports presented to senior management are standardized documents that are often exaggerated by static tables and various chart types. It is precisely because it is produced in too much detail that the senior management cannot remember the content and therefore does not need to see too much detailed information.

However, reports from big data visualization tools allow us to present complex information in a few short graphics, or even a single graphic. Decision makers can easily interpret a variety of different data sources through interactive elements and new visualization tools such as heat maps and fever charts. Rich but meaningful graphics help busy executives and business partners understand problems and pending plans.

3. Understand the connection between operations and results

One benefit of big data visualization is that it allows users to track connections between operations and overall business performance. In a competitive environment, finding correlations between business functions and market performance is crucial.

For example, an executive sales director at a software company might immediately see in a bar chart that sales of their flagship product in the Southwest region are down 8%. The executive can then drill down to see where these variances are occurring and start developing a plan. In this way, data visualization allows managers to spot problems and take action immediately.

4. Embrace emerging trends

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The amount of data now collected on consumer behavior can bring many new opportunities to adaptable companies. However, this requires them to continuously collect and analyze this information. By using big data visualization to monitor key indicators, business leaders can more easily spot market changes and trends in various large data sets.

For example, a clothing chain might discover that sales of dark suits and ties are rising in the Southwest. This might allow them to promote a collection of clothing that includes both, or a new skinny tie, well ahead of competitors who haven't noticed the trend yet.

5. Interact with data

The main benefit of data visualization is that it brings risk changes to light in real time. But unlike static charts, interactive data visualization encourages users to explore and even manipulate the data to discover other factors. This provides better insights into the use of analytics.

For example, a large data visualization tool could show a boat manufacturer that sales of its large craft are down. This could be due to a range of reasons. But team members actively exploring related issues and linking them to actual boat sales can identify the root cause and find ways to reduce its impact to drive more sales.

6. Create a new discussion

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One of the advantages of big data visualization is that it provides a ready way to tell a story from the data. Heat maps can show the evolution of product performance across multiple geographic regions, making it easier for users to see which products are performing well or poorly. This allows executives to drill down into specific locations to see which ones are doing well and which ones are not.

They might realize that targeting higher-income market segments doesn’t sell higher-priced products, or that traditional cleaning product sales are less popular than environmentally friendly green products. These insights can be used to brainstorm ideas that support higher sales.

Big data visualization tools provide a more efficient way to use operational data. For more business leaders, changes in real-time performance and market indicators are easier to identify and respond to.

7. Machine Learning: Come on, come on

All the hype around machine learning is about to become reality. In addition to some big companies, such as Amazon and Google, which are using machine learning to eliminate spam, Pinterest uses machine learning to show users relevant content, and Yelp uses machine learning to organize user uploaded photos. Even companies like Disqus are using machine learning to remove spam. Get ready to start applying machine learning to your own business or your clients’ business.

Home Depot uses machine learning to help users find products faster, and even smaller companies like Lyst use machine learning to help customers find relevant information for any kind of query.

Customer service is being transformed by machine learning’s ability to interpret customer emails and categorize them to correct departments or regions within a company, meaning that phone calls may never be needed again.