It’s easy to find evidence of how prevalent big data and analytics have become across organizations in most fields and industries. Big data, after all, continues to improve and provide better insights, more robust tools, and higher quality results. More importantly, the techniques and systems used for analytics have evolved from rudimentary techniques—like manually inputting data into spreadsheets—into highly complex platforms that use artificial intelligence and other emerging technologies to both simplify and accelerate data processing and understanding.
Thanks to these new technologies, data processing is taking its next leap into the field of augmented analytics. By incorporating machine learning, natural language processing, and other advanced tools, full stack augmented analytics are closer to becoming the norm instead of a novelty. Therefore, it’s important to consider what the possibilities are, and how it could result in smarter analytics, more actionable business intelligence, and faster, more accurate insights.
What is Augmented Analytics?
More and more, organizations demand faster analytics that requires less user input and provide better results. While a decade ago this sounded like a pipe dream, advances in AI and natural language techniques have made augmented analytics a reality. The name itself raises important questions, but the most important answer is that augmented intelligence is not meant to replace human intelligence, but rather enhance it.
Augmented analytics refers to the systems that automate key aspects of the intelligence gathering and processing process. This includes implementing techniques and platforms that can successfully collect data, understand it, and find the best ways to parse and present it. Because of the complex nature of these tools, augmented analytics includes several technology disciplines that until recent years had been mostly theoretical. This includes AI and machine learning that let business intelligence tools not just gather data, but continuously find better and smarter ways to present it.
Moreover, augmented analytics include a user-facing side that uses natural language techniques to make it easier to interact with the system. These methods include natural language querying, and usually will include a business intelligence bot that can interact with users both to collect and present data that is relevant. Overall, the system is designed to make it easier to analyze and find the data that is relevant to users without requiring heavy manual input.
How to Deploy Full Stack Augmented Analytics
While different aspects of augmented intelligence can be deployed into existing business intelligence solutions to help improve them, the most useful implementations are those that take a full stack approach. For augmented analytics, this means creating a clear flow from the way data is collected to the methods used to presented to users as insights and results.
The process starts with data preparation as algorithms can work to integrate different data catalogs and make informed suggestions of schemas and different meaningful enrichments for data. Here, AI can make it easier to select the right means of analyzing and organizing data to extract the maximum value.
Moving on to user interaction, full stack analytics uses natural language processing to make it easier for users to get the information they need quickly. Natural language querying can be applied to let anyone simply ask questions and have augmented intelligence find the relevant patterns and produce the correct models automatically. By removing the need to input complex parameters for the right data, augmented analytics makes business intelligence more accessible to regular end users. Eventually, natural language is also used to return answers and insights to users. Already, chatbots and other business intelligence bots provide a much easier way to understand data. Additionally, natural language tools can optimize different visualizations, as well as provide key context that can add value to data.
Finally, machine learning can be used to continuously improve the prescriptive analytics inside dashboards by providing recommendations for actions based on incoming data and prevalent trends. This can include giving you suggestions about ways to meet KPIs, different possible courses of actions based on consumer tendencies, and areas where you can improve performance and efficiency in your operations.
Full Stack Augmented Analytics is the Future
A decade ago, using AI and machine learning for business was rare in any industry. Now, however, business intelligence tools have been integrating different AI functionality to improve key aspects of the analytics and display process. By committing fully to AI and Machine learning as part of augmented analytics, companies can significantly improve the way they interact with and extract value from their data. More importantly, they can make users’ lives easier and provide better insights as well as results. In a decade, it will be rare to find an industry that doesn’t use augmented analytics to get the best possible insights.