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How Can AI Efficiently Reduce the Time to Deliver Insights?

How Can AI Efficiently Reduce the Time to Deliver Insights?

How Can AI Efficiently Reduce the Time to Deliver Insights?
by Meenu EG May 28, 2021
AI tools can now reduce the time that is taken to reach data insights.
Data analytics and business intelligence are imperative to enhance business growth today. They have far-reaching benefits like improving customer experience, delivering better services, developing agility, risk management, fraud detection, and many more. AI has been a blessing to industries so that they could create better and intelligent insight. These data-driven insights are the crux of businesses today and companies are trying to get to these insights as fast as possible. The customer-centric approaches in the current scenario and the fast-paced technology-driven businesses demand faster insights. But how do you reduce the time taken to reach these insights derived from analytics?
 
AI in Reducing Time to Insights
Thanks to the recent and emerging innovations in artificial intelligence, it makes it easier to ensure business productivity. AI is the soul of data analytics and business intelligence. Faster and accurate demand forecasting, predictive maintenance, personalization of services, and optimizing manufacturing processes are examples of what AI can do to businesses. Apart from all these, AI is also capable of delivering faster insights. Here are some methods to reduce time to insight.
Discussions have been going around about parallel processing architectures for faster data insights. Parallel processing combines and integrates conventional data analytics with spatiotemporal analysis. Spatiotemporal analysis occurs when data is collected across time and space and has spatial properties to it. Parallel processing data infrastructures provide a visualized form of insights and democratize it. This method can eliminate the data manipulation processes required otherwise, like down-sampling. It enables users to visualize insights geographically, cross-filter the results in real-time, and gives a wider picture.
Video content analysis is another technology that can enhance the quality and speed of actionable insights inferred from videos. Today, the technology is being proven effective in identifying objects or people and providing insights based on them. But, as the advancements rise in sentiment analysis , machine learning, and AI, this technology will be able to generate context and meaningful story arcs from videos. Video analytics is powered by AI and is extensively used in security and surveillance, transport and traffic management, etc. Video content analysis can perform real-time monitoring, identification, and alert users regarding suspicious motion activity at an unimaginably faster pace and with better accuracy. Google has been conducting experiments on video ads through its Display and Video 360 tool to generate faster insights with AI.
Augmented analytics is driven by AI and it leverages the power of Machine Learning, Natural Language Generation, and Natural Language Query. Augmented AI analytics provides faster data analytics and insights, automated report generation. Natural language generation can convert machine learning data into comprehensible insights using natural language. NLQ provides intuitive data analytics and increases the speed to reach insights.
Sentiment analysis, and NLP-based text mining and data analytics will yield better and faster insights. Language is a powerful tool that is used to express sentiments and emotions. AI enables us to read the language and analyze the sentiment in it to arrive at better insights. For today’s customer-centric businesses, it becomes important to understand the users, their interests, emotions, and needs. Machines can understand the intended emotions displayed in words through advanced AI and other techniques like embedding, data mining, and NLP.
Machine learning and NLP can be used in text mining to deliver faster insights through sentiment analysis. Today, social media plays a potential role in customer engagement and business growth. Hence, using these techniques on social media feedback and comments can increase customer experience and traction. An example would be the Natural Language Text Analysis API by Google that efficiently provides meaningful insights from textual data.
Apart from these tools and techniques, a company needs to have relevant datasets, updated or real-time data, friendly user interfaces and infrastructures, and digital access and devices to perform advanced, faster data analytics for insights. AI’s capabilities are still being explored and with passing time, we might witness even more efficient innovations to extract intelligent and meaningful analytical insights.