We live in the Data Era. Not just Data, but Big Data. Datasets have gotten unpredictable, fast-moving, and progressively gigantic. Legacy Business Intelligence frameworks that dealt with information cannot understand the volume information coming at live speed. The automation of data management tells an alternate story. In addition to the fact that it automates data management, it also simplifies the task of data scientists.
Coined by Garner, augmented analytics is the future of data analytics that utilizes disruptive technologies such as machine learning/ artificial intelligence techniques to operate data preparation automatically, insight discovery, and intelligence sharing.
Data analytics software that unifies augmented analytics interconnects with data as humans would do but on a large scale to cater to bid data requirements. The analysis process frequently begins with public or private data collection. Legacy data pipelines are developed by data scientists, who spent 80% of their time on collection and data preparation, and just the unused 20% on discovering insights. The aim of augmented analytics automatically operates the process of data collection and data preparation to 80% of data scientist’s time. Still, eventually augmented analytics would completely replace the manual work of the data science teams with artificial intelligence. Augmented analytics would completely handle with care, the entire analysis process from collecting the data to presenting the business recommendations for decision making.
Ultimately when data experts carry out data analysis, they are trying to find the answer to a question. This question may be straightforward, like “What were the sales figures last year by channel and region?” These types of questions lookout facts and hard numbers, and are the precursor to more complex questions like, “Why did sales decrease last year?”
Business Intelligence tools that integrate augmented analytics can automate these questions. It is also imperative to regard a solution’s adaptability for an increasingly digital era, where data is a hot and volatile topic. With the huge technological advancements, businesses can acquire more data than before. This data helps businesses to acquire more insights into the customer life cycle. It also gives out a challenge since companies should decide how to host and structure data sources.
