Make Room for AI-Analytics in Big Data Dashboard

Make Room for AI-Analytics in Big Data Dashboard

For real-time insights to help business decisions, experts recommend the combination of Big Data dashboard, AI, Analytics, and Decision Intelligence

Business Intelligence (BI) tools that have been around for years cannot keep up with the digital transformation today. Although research by Beroe predicts that the global BI market will grow up to $30.9 billion by 2022, experts believe that being accustomed to the existing BI tools can cost companies information and revenue. In the digitized, data-driven world, where personal data accounts for big problems, it is necessary to analyze and fix the data complications.

Scouring through terabytes of personal data can get a little easier by leveraging Artificial Intelligence (AI) and analytical technology to separate personal and sensitive information from the raw data. For long, businesses have depended on the big data dashboard for decision-making. Experts reckon it’s time they realize the shortcomings of big data dashboards and find an alternative strategy.

Big Data dashboards with visual elements of graphs and charts depict general customer data without reflecting any specific value. The metrics do not allow for a quick business decision to be taken when a problem arises. General indications of big data make it difficult for enterprises to create a good business strategy.

Also Read: Boosting Machine Learning Performance with the Right Data

Big data often fails to capture micro entitlements that create market demand, but not enough to ne noticeable. Because of this, many smart strategies are not created- as insights are missing from the larger picture.  The dashboard helps summarize business analytics, but in the process, it ignores real-time insights. Experts suggest brands gather all anomalies using a combination of event data. This strategy can work only by analysing data in the most insightful way possible and leveraging Artificial Intelligence proves to be the best. They can scale billions of individual data points that are added continuously to the system.

Until recently, data analysts utilized manual monitoring methods known as static thresholds to balance the shortcomings of big data dashboards. It would send several alerts to data analysts who would spend their time figuring out the problem. Sometimes, it gets too late for enterprises to work on a strategy by the time analysts picked out the actionable insights. The loss of revenue, time, and brand reputation drives the need to find a better solution, which results in the combination of big data dashboard with AI-driven analytics.

Also Read: The Importance of Embedded Analytics and the Parameters for Selecting the Right Software

AI-analytics comprises of automated anomaly detection that constantly analyses data, identifies errors, and reveals patterns from ‘noisy’ data. Correlating multiple anomalies, AI analytics filter the most crucial insights. AI-driven analytics tools can also show the cause and effect relationship between different data factors. Additionally, they can monitor massive amounts of metrics intricately, and recommend insights that can help with decisions and business strategies. Clearly, if enterprises solely depended on big data analytics, the smaller details could be missed.

A hybrid analysis model, Decision Intelligence (DI), utilizes the insights and patterns from AI analytics and the big data dashboard to help business executives come up with intelligent, creative, and experience-driven decisions. Experts believe that AI analytics fuel best decisions at a large scale and have the potential to boost revenue and streamline business decision processes.

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