One of the 2019 tech trends consists of using augmented analytics tools for data automation. The tools which create automated functions will identify data sets, identify data patterns, and develop hypothesis. The evolution of machine learning and data science platforms heavily influence how businesses build and use analytics insight. By increasing its use, data automation is on the way to represent over 40% of data science within the next two years.
To get ready for relevant business insights from analyzing the data, organizations focus on incorporating artificial intelligence in building models and simplifying the data to integrate it for automation. The flow from data to insight and to action implies preparing data and decoding it to reveal patterns and to create models for data distribution and viability. An important quality of augmented analytics consists of ignoring human bias while identifying hidden data patterns. The automated business insights along with augmented analytics will be incorporated in enterprise applications.
In the augmented reality, users are not required to ask predefined questions to generate suitable inquiries from the data at hand. They do not need to get skills in data preparation and automation. The Business Intelligence system, the foundation for executing augmented analytics can reveal data containing business-critical relationships and then build specific dashboards and visualizations automatically.
Augmented reality helps us to understand data exploration faster, analyze information to a greater extent and blend users’ engagement with the data thus generating a greater impact on the outcome. In doing so, the process of generating data through augmented analytics seems personalized and humanized discarding any obstacle between us and the data we want to use.
The continuous need for data processing along with its increased volume and sophistication significantly raises the demand for data scientists.
While the data volume keep raising exponentially, the data scientists are faced each day with the daunting task to prepare, browse, analyze, sort and group to ultimately draw appropriate conclusions. Applying the classical data mining techniques, these tasks become impractical and unattainable. With the assistance of AI in augmented analytics, however, this means that businesses can achieve clarity on key insights from hypotheses found through automation that the data scientists did not have the ability to experience before.
With augment analytics tools, business data insights can be automatically prepared saving precious time and other resources while existing and moving broadly across businesses and proving significantly more rapidly accessible between decision makers, data and business analysts, as well as operational workers.
Cory Popescu, SIP Writers’ Forum