Last semester we learned about the importance of AI technology and how it interweaves into many platforms we use today. This semester we are taking a deeper look into data analytics, learning about different tools to analyze data like Tableau which is a data visualization software. Recently there is talk of a new way to handle big data, one that uses AI technology. This new process is called augmented analytics.
Augmented analysis is an approach that automates insights using machine learning and natural language processing to transform how analytics content is developed, consumed, and shared. The data in this process is prepared through a streamlined automation process from various sources like external portals, internal data, cloud data, and any other locations. The advantage of using AI technology is that it’s always working, with the ability to learn and enhance results.
Augmented analysis has set itself to be a huge disruptor to data analytics, fundamentally changing the workflow. Analysts will now be able to explore and analyze data without having to build models or write algorithms, already decreasing the time it takes to get from raw data to personalized insights. This tool will also provide new and unexpected insights outside of what analysts may have been looking for.
In order for companies to use this tool, there are certain things to take into consideration. First, they will need to confirm that their existing platform has the capability to handle the computing power this system may have. There are other challenges that may arise when transitioning to augmented analytics, if companies don’t have the right data to train the analytics model, then the insights may not be valuable. Some ways to solve these problems include starting small and making sure your KPI aligns and training employees with strategies and tools to increase data literacy.
The world is changing, what we once knew about data has changed and big data is becoming more prevalent. This is why we need tools and processes that are made especially for it. It will behoove companies to use augmented analytics not only because it will simplify and quicken workflow but how it will connect databases and live data sources and then provide data visualization that is able to be shared across their entire organization. Here is an overview look at how it compares to traditional business intelligence tools:
Resources:
“Augmented Analytics: Examples & Best Practices.” Qlik, QlikTech International, 2021, www.qlik.com/us/augmented-analytics#:~:text=An%20example%20of%20augmented%20analytics,gain%20the%20insights%20she%20needs.
“Augmented Analytics: the Future of Business Intelligence l Sisense.” Sisense, Sisense Inc, 4 Feb. 2021, www.sisense.com/whitepapers/augmented-analytics-the-future-of-business-intelligence/.
“Big Data Trends to Watch Out in 2021: Nex Software.” NEX Softsys, Nex Software, 2021, www.nexsoftsys.com/articles/big-data-trends.html.
Jacquez, Jason. “4 Reasons Why Augmented Analytics Is the Future of Business Intelligence.” Platform Leader, Oracle, 30 Oct. 2020, blogs.oracle.com/platformleader/4-reasons-why-augmented-analytics-is-the-future-of-business-intelligence.
Sallam, Rita, and Carlie Idoine. “Augmented Analytics Is the Future of Analytics.” Gartner Gateway, Gartner Inc, 30 Oct. 2019, www.gartner.com/document/code/444837?ref=ddisp&refval=444837.
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