Human beings are now part of a ‘data era’, which is more of Big Data. The modern era has revolutionized rapidly and has led to the formulation of a completely new digital era and has taken the world to the next stage of revolution in terms of “Industrial Revolution 4.0”. All these changes are data-driven and the most interestingly, datasets have been dynamic, fast-moving, and ultimately massive. Legacy BI applications that manage data cannot even comprehend the pace of the arrival of data.
Automation in data storage reveals a specific picture. This not only automates data collection; it also simplifies the role of data scientists.
Coined by Gartner, augmented analytics is the future of data analytics that leverages emerging technology such as machine learning / artificial intelligence approaches to simplify data processing, knowledge creation, and knowledge exchange process.
Information analytics software that incorporates augmented analytics deals with information like individuals do, but on a broad scale, to serve the interests of big data. The research process also starts with a compilation of public or private data.
Legacy data pipelines were generated by data scientists who had to spend 80 percent of their time on gathering and sorting the collected data, and just the remaining 20 percent on extracting information. This made such data scientists work on the technological aspect to enhance and speed up the data processing and sorting time. This would have been possible only with the help of technology. The aim of advanced analytics data collection and data processing technology was to save 80 percent of the time of the data scientist, which they could rather use on analyzing the results and derive more useful and knowledgeable insights.
Although it was unexpected that advanced analytics will be able to eliminate the repetitive labor of the data analysis departments with AI. Augmented Analytics will take control of the whole cycle of research from data acquisition to the delivery of market insights to decision-makers.
Augmented Analytics for Data Management :
Augmented Analytics can help to answer most of the questions which are usually the most important for the company to take the production decision and vital decision-making process like “why did the sales rise last year” or “what was the reason for the market share price to rising/ fall”.
These questions can be automated by BI tools that integrate augmented analytics. For example, users can type a query in a search field and expect an answer in natural language, preceded by imaging and knowledge.
This is therefore essential to consider the adaptability of a strategy to an emerging digital environment where data is a hot and uncertain problem and emerging innovations are quickly evolving.
Through major technology advances, companies can collect more data than they have ever. Data analysis provides an opportunity to secure deeper transparency into the customer life cycle.
With advances in data protection and enforcement with the GDPR (General Data Protection Regulation), businesses that exploit e-commerce and online connectivity platforms are dealing with evolving regulations as they consider whether to collect user data.