The Power of Augmented Analytics

Feb 03, 2020

Data management and intuitive data analysis have long been time-consuming and highly detailed processes, typically requiring significant amounts of coding and analytical knowledge to be executed correctly and promptly. While demand has led to the growth of the population specialized in these skills, data analysis techniques and technology are evolving at the same time. Even though people are learning more about these topics, and developing more diverse approaches to solving modern problems, time can still be a limitation in developing large scale and in-depth analytical models. In the era of Big Data today, speed and efficiency are everything. So, with the constant growth and advancement that databases are experiencing every day, making sure a company’s analytics are running quickly and accurately has become a top priority.

This is where the emerging system of augmented analytics steps in and can now play a crucial and innovative role in a company’s analytics process. Augmented analytics, which has gained prominence in the last couple of years with the publication of a report from Gartner, is the use of machine learning and natural language processing to take raw, uncleaned data and quickly transform it into results ready for further analysis and interpretation. This constantly evolving realm of analytics draws from aspects of artificial intelligence and business intelligence, in order to properly assist both technical and non-technical users in their respective day-to-day, time-consuming activities. While most of a typical data analyst’s time is taken up by either collecting and cleaning data or translating results into a presentable report, augmented analytics eliminates the most tedious aspects of analytics and allows companies to spend more time understanding the results, solving more intricate problems, and choosing the most impactful and efficient action plans.

Augmented analytics uses machine learning algorithms to remove many of the longer tasks needed in the data analysis chain of events that would otherwise require extensive manual time and effort. Having these algorithms in place to automatically take in, clean, and analyze data can save a company’s employees a large amount of their time, and thereby open up their availability to dive deeper into more thoughtful and useful analyses for their team. Augmented analytics also allows those who are not as knowledgeable in the field of data analytics to easily view and understand the output of an analysis. Instead of needing someone who has extensive knowledge of which model or algorithm is appropriate given the specific data set, augmented analytics uses all of its stored knowledge and algorithms to automatically run through the now clean data and determine the best solution for the problem at hand.

Natural language processing (NLP), within augmented analytics, provides a massive leap for technology and data analysis. Although it is still developing today, this process not only assists the analyst in saving time, but it is also extremely useful both for management and for those with a less technical background. Rather than having to type up and format every report for executives, advanced NLP allows a user to have reports automatically output, in the format or style they would like, again eliminating a huge amount of time that would have been taken in order to create it by hand. NLP also utilizes voice recognition to transcribe a user’s spoken questions, whether it be a simple and non-technical request or a more complex one and generates the quantitative value or desired response, within seconds, without manual labor. This allows for real-time clarification to be done, eliminating the time it would have taken in order to code and run a brand-new, and possibly very difficult analysis, in order to answer the question at hand. With augmented analytics, the answers to the questions would be readily available because it will have already been automatically run in the background.

A companies use of augmented analytics allows them to significantly increase their efficiency, thereby leading to faster, more accurate solutions and more impactful decision making. Cutting out the portion of an analyst’s time that is required to clean the data, figuring out which model is best, and creating a report from the output, improves the productivity of the business processes and allows for better time management for the analyst. The capabilities of this technology introduce a realm of further opportunities for more complex, in-depth analysis of results and for better use of data to further improve operations and decision-making for a business.



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