In June 2019, two blockbuster data visualization acquisitions — one of Looker by Google for $2.6 billion and the other of Tableau by Salesforce for $15.3 billion — have cemented data visualization tools and the larger field of BI reporting as a critical part of end-to-end analyticsSo what role do data visualization tools play in advanced analytics? Data visualizations communicate complex relationships that help people to make better decisions. Data visualization plays an important role in three parts of the end-to-end analytics process: data discovery, model evaluation, and deployment of model output to decision makers.
One of the first steps in any analytics project, once the business objectives are understood, is to explore available data to help solve a problem. When fighting fraud, a credit card company would find common patterns of suspect behavior, like large unusual purchases of electronics or jewelry, without ever building a model.
Data scientists use visualization in modeling as well. Clustering techniques, which use algorithms to group like entities such as customers, can be understood by “seeing” groups of people plotted on a graph.
Most importantly, visualizations of model performance help data scientists to communicate model implications to other business people, who need to determine whether a model is appropriate for use in operations. A churn model in telecommunications may seem perfect for predicting customers who are likely to leave. If the marketing department cannot take action on these predictions because they do not have permission to contact these customers due to GDPR or other consumer protections, then the model is academic.
Deployment to Front-line Decision Makers
As consumers, we use products all the time without thinking about the complex analytics that underly them. When we ask Alexa or Siri or Google to tell us how to get to the nearest grocery store, we are using layers of advanced analytics: to recognize and process our speech, find the relevant information, serve up an optimal route, and so on. In this case, a map is the visualization.So too with business applications. A manufacturing solution using BI reporting may show graphs with color-coding for predictions of out-of-threshold situations that require intervention. A maintenance checklist may simply look like a list of tasks to perform; the engineer may not realize or care that it was created with predictive maintenance techniques.
Visualization has come of age. Data visualization techniques are required throughout the end-to-end analytics process, enabling business users to make better data science solutions and use the output of these solutions to run businesses better.Syntelli Solutions’ data visualization services and data reporting solutions can help you achieve clean, concise visuals yielding quicker insights and greater impact. We primarily work with leading data visualization tools and BI reporting, such as Spotfire, Tableau, QlikView, and Microsoft Power BI. For more information, see Reporting and Visualization.