Using analytics to extract important insights is now commonplace in business and finance. It is plain to see that data and creativity in utilizing and analyzing it is what brings value to decision-making in most if not all departments. What is relatively new, however, is the idea that humans should only come in at the final stages of data-based decision-making. This idea is behind augmented analytics, which utilizes
AI-based tools and procedures to do with data as much as possible before people get to look at the results. So, what exactly is it and how does it work? Let us go over it.
Definition and key concepts
The world is moving fast these days and this speed of pace is especially felt in business. Augmented analytics can be taken as a case and point for this notion. Although the term has not been around for a long time, it is already gaining prominence among data-driven executives.
Augmented analytics can be defined as an approach to data analytics that is based on automating as much as possible in the process of insight extraction. To achieve this high level of automation, experts rely on some of the most important developments in informational and AI technology.
The key concepts of augmented analytics describe some of the most important procedures and tools related to artificial intelligence and business analytics. Here are the main pillars of this approach to automated data analysis.
- Machine learning. Augmented analytics utilize the ability of algorithms to learn to come to solutions on their own as they encounter more and more data. Since AI tools can figure out on their own what to do in new cases, human interference is not necessary. This allows automating data analysis to an extremely great extent. A related concept of deep learning is also important here. It is the part of machine learning, that, as the term implies, goes deeper, by applying multi-layered data extraction.
- Natural language generation (NLG). This concept defines any software tool that is capable of translating data into natural language that humans can easily read and understand. This feature makes it possible to turn raw data into information that even non-specialists can comprehend. Thus, data insights can be produced without the necessity for the technical staff to translate them into a more common language.
- Augmented data management. Many of the data management tasks, from data categorization and cataloging to metadata management and data transferring, can be automated. Augmented data management allows moving, structuring, and using data with as little human oversight as possible. AI tools are capable of handling standard procedures efficiently all the way to the analysis stage.
- Natural language querying. This feature allows searching for the extracted information using natural language terms. Thus, anyone with or without skills in data science can simply type familiar business terms in a box to access insights that have been produced through augmented analytics.
Taken together with other innovative solutions these concepts enable what is now known as augmented analytics. That is a way to turn raw data into insights that are understandable for business professionals of all backgrounds.
The top benefits of augmented analytics
There are many ways in which a highly automated and adaptable data extraction model can benefit businesses and financial firms. Augmented analytics provide such a model and have already been successfully applied by businesses and financial firms.
It might still be worthwhile to point out some of its most useful and widely applicable benefits. Here are 4 crucial examples.
1) Augmented analytics is the more efficient approach to data analysis. Automation always saves time. There is no sense for humans to do the work on data that computers can do so much faster. The shortening of time needed for data to become insights will allow benefiting out of more of them. When information arrives in an accessible form in such a fast manner, it is much less likely that a valuable opportunity will go unnoticed.
2) It removes human bias. People are bound to look at things through their subjective perspectives. No one is completely immune to some level of bias when analyzing information. AI tools, however, have no such bias and can simply tell you what the data says. This allows viewing information through a more objective lens and mitigates the risks of errors.
3) It saves money. Along with being effective in relation to time, utilizing augmented analytics is also cost-efficient. Without the need for manual labor in data management companies are able
to invest their resources where they can be used best. Doing more with less, businesses cut costs and open ways for further innovations which will in turn boost profits even more. 4) Lastly and crucially – augmented analytics make data insights accessible for everyone. One of the most important goals of modern businesses is to make sure that their data assets are utilized on all fronts. When AI tools can go all the way from receiving data to turning it into readable plain language information, it is more possible than ever before. Now anyone can look at the data and search for answers that will help them with conducting everyday tasks or solving unusual issues.
These and many other advantages of utilizing augmented analytics make it the way forward. This new but quickly developing approach is sure to change and adapt as it attracts more and more attention. Thus, more exciting innovations in data analytics are surely waiting around the corner.
