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How Machine Learning Improves Data Visualization

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How Machine Learning Improves Data Visualization

Data visualization is a very handy tool to have in any data scientist's belt, so how can it be improved to give even more insight? Machine learning might hold the key.

· AI Zone ·
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Lately, it seems like everyone in the business world is talking about machine learning. There's no doubt that it has changed just about every aspect of the tech world and even permeates into everyday life outside of the office. Most people, however, might think of machine learning just as the technology behind Facebook learning what content they like to see or their favorite search engine's autocomplete feature.

Image titleAs evident by the latest Gartner report, however, machine learning and its contributions to augmented analytics will continue to have a big effect on business intelligence and data visualization. How does machine learning benefit visualizations? There are several ways that integrating machine learning into your data visualization tools can enhance your efforts and improve your analytics.

Make Your Visualizations Dynamic With Real-Time Analytics

One of the biggest benefits of machine learning algorithms is expediting the data discovery process. Because they're designed to automatically improve their analysis as they scan information, machine learning tools are ideal for companies that have constant data streams. This real-time visualization can let you see what is happening exactly at all points of your production chain and understand how new factors affect existing data.

More importantly, these algorithms can help you identify outliers and unexpected outcomes. Combining data visualization and machine learning lets you establish baseline metrics for performance before scanning for any situation that breaks that mold. This can help you react faster, more effectively, and avoid downtime across your operations.

Find More Granular and Actionable Insights

As expert as data scientists may be, they're no match for a computer's raw processing and parsing power. Machines can run through millions of data points in seconds and can often find more profound insights from larger datasets. By adding a machine learning component, you can teach your data visualization software to search for deeper connections in big data sets. Moreover, machine learning algorithms assist you with getting as granular as needed with user data.

For visualizations, this level of granularity means you can better understand consumers, product chains, logistics systems and more. You can craft more specific visualization tools alongside building charts and graphs that pull from wider databases.

Create Better Searches for Visualization Dashboards

A major challenge of fashioning useful dashboards for visualizations is the ability to search for specific parameters in a straightforward manner. One of the most prominent applications of machine learning is in search engines that can predict users' questions and make better queries of available data to deliver results. Visualizations can display a massive number of different data combinations, but it can be time-consuming to specify parameters on dashboards without a useful search functionality.

By using tools such as natural language processing, machine-learning-driven tools can understand queries in a simpler form and deliver more accurate results when parsing through user questions. This allows you to generate more creative visualizations to better understand your business.

Make Your Data Tell a Story

Visualization is a vital component of the analytics project, but the information presented without context loses some of its insight. Data is more than just one set of numbers. Insights may take from several parts of a set or combine diverse sources into an actionable understanding. However, without the ability to quickly and logically order it, conveying the information is a less direct process.

Machine learning algorithms are designed to understand historical data and apply their findings to new information, constructing better visualization models. This allows you to shape data sets into a more defined narrative, giving you a better context for the information you are viewing. Moreover, understanding your data's narrative gives you a better picture of your overall operation, and where to improve to continue growing.

Create Better Predictive Models

One of the most exciting research avenues being explored with machine learning is in predictive analytics. Until recently, however, these models often relied on human interaction to properly work, as they were unable to adjust for small variations over time and unexpected data inputs.

Now, machine learning allows models to continuously learn from existing data and adapt, providing more accurate visualizations while simultaneously improving the quality of projections. By incorporating not just information, but the lessons that information contains, machine learning tools give data visualization platforms a more reliable projection model alongside a better way to forecast results and needs.

Understanding your data is more vital than ever before. Simply grasping what it means is no longer enough, and visualization is a key component of finding ways to turn data into action. By incorporating data visualization with machine learning tools to construct visual models that are more dynamic, you can build a better knowledge base for your business.

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Topics:
ai ,machine learning ,data visualization ,real-time analytics ,predictive analytics ,data analytics

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