In both scenarios depicted above, data preparation is still manual and takes a lot of time. Further, there is user biasing, which may lead to wrong output and could have a major impact on organizational decisions. In addition, all these tasks are usually performed by data scientists who spend 80% of their time on data collection and preparation and just 20% of their time on finding meaningful insights. They often indulge in performing simple mechanical tasks such as labelling and cleaning their data, which is makes the process laborious. Furthermore, there is no guarantee that a data scientist analyzes 100% of the data, which can be very critical to any business. Due to these reasons, several small and medium-sized businesses are still in the early stages of analytics adoption despite a strong desire to make use of the data.
As an answer and a solution to all the above-mentioned challenges, there is a dire need to embrace the concept of “Augmented Analytics”.
What is Augmented Analytics?
Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert by automating many aspects of data science, machine learning, and AI model development, management, and deployment.
How augmented analytics augments resources
Augmented Analytics helps in enabling ML/AI created data and analytics by automating the data preparation, insight discovery, and key aspects of data science and AI.NL modelling, such as feature engineering and model selection (AutoML) as described below. It also makes use of machine learning and NLP to understand and interact with data as humans would do on a large scale, free of human biases. In addition, it will reveal insights that resources would never have realized existed; inferences like establishing connections among the data and also suggesting relationships and insights, without the user even thinking to ask for them.
For e.g. A user can ask the required output in a natural language like “What is the impact on revenue over the last 4 months because of COVID?”. These questions can also be extended to other chat platforms like chatbots and voice-based interfaces.