The world is experiencing advanced data analytics, whose results turn very profound. What could be the source? Augmented Analytics is the immediate answer. This approach uses Machine Learning and Natural Language Processing to process the analytics automatically, which otherwise would be performed by the data scientists or specialists manually.
What is Augmented Analytics?
Augmented Analytics has emerged as a “Disruptive Innovation”, which means that the technology has challenged the established business standards. The future of it will be more innovative in data science, Power BI, ML platforms, etc. It is time for data scientists to use Augmented Analytics.
Before getting into know to use Augmented Analytics, it is essential to know its requirements:
Man in the modern age generates enormous volumes of data from different sources. What serves as the base to derive valuable insights from it. Initially, understanding the data and knowing the requirements lie as the base. Following this, one needs to develop an algorithm or model to assess it. Accomplishing this is not an overnight job and requires significant effort and time.
What does Augmented Analytics do?
Augmented Analytics plays a crucial role to automate the process of understanding data, analyzing it and deriving valuable insights to display them as simple and clear visuals.
Sub-categories of Augmented Analytics include the following :
Data Discovery: The role of data discovery is prominent, for it can generate valuable data insights as visuals automatically instead of creating algorithms or developing models manually.
Data Preparation: Machine Learning techniques are used to detect leakages, impute missing values and perform time-series feature extraction.
Data Science and Machine Learning: All the essential features of analytic modelling are automated. Consequently, the need for experts who manage the models and the algorithms will decrease.
Let us plunge into the benefits of Augmented Analytics.
The core concept of developing Augmented Analytics is to handle repetitive tasks efficiently, manage human data, make prompt decisions and help businesses thrive. This being the basic overview, let us get to know about the significant advantages below:
Excellent decision making: Data visualization has proved its record in the past decade. However, analyzing data according to the business standards and gaining valuable insights from them still has not reached the expected mark. But, now we are experiencing how AI and ML technologies driven by human intelligence end up making excellent decisions.
Optimal productivity: We all experience how repetitive tasks are cumbersome and consume time. Spending time on such tasks results in a decline in productivity. However, with the advent of AI technology, users and businesses can make effective decisions and draw conclusions quickly.
Generate value: Higher level business tasks needs focus and careful attention to deliver successful results. Spending time on repetitive tasks decreases the overall value of the work. So, choosing an automated system is always preferable. This helps accomplish data preparation, gain valuable insights and execute ML and DL algorithms to increase the value of any business.
Transform retail analytics: Augmented Analytics and Natural Language Processing have truly transformed retail analytics. One needs not to be an expert analyst to derive the essence of the analytics.
Conclusion:
It is indeed true that Augmented Analytics has created a wave in the field of data and analytics. Wish to experience it? Then get ready! ONPASSIVE, an AI and ML organization, has built AI products to ease the complex tasks of business operations cost-effectively.
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