Over the last decade, visualisation-based data discovery tools have transformed the traditional analytics tools. Additionally, with the introduction of next-gen augmented analytics, organisations are able to generate not only simple forecasting, visualising and clustering data, but also automated and actionable predictive and prescriptive guidance.
Augmented analytics tools work as virtual data scientists which can iteratively perform data-to-insight-to-action activities like preparing the data, deciphering data patterns and building models and distributing and operationalising the data findings. This saves both time and resources used for getting relevant business insights from the available data. As per SBWire, Global Augmented Analytics Market is expected to grow at CAGR of ~11% from 2018 to 2025.
Analytics 2.0 has already being put to test by multiple organisations. For instance, US government authorities have partnered with augmented analytics players like Stories.bi to find the most important insights from public data sets on the U.S. opioid crisis. Similarly, U.S. Health Insurance Company have been utilising Salesforce's AI-infused analytics tool, Einstein Discovery to track cost metrics based on the sickness of patients. Similarly, Workday is taking a further step with the introduction of augmented analytics to generate actionable insights around HR data.
Multiple bigtechs like Google and Microsoft have also developed products around augmented analytics. For example, Chevron Corp., US-based multinational energy corporation, is an early adopter of Google's augmented AutoML technology, which is designed to help users with limited machine learning expertise, build and train analytical models. The seismic processing and imaging team at Chevron have used the alpha version of an AutoML Vision image analysis tool to help analyze internal documents as part of the process of evaluating new opportunities for oil drilling.
Similarly, Microsoft also added next-gen analytics functionality to its cloud-based Azure Machine Learning platform, enabling the software to identify algorithms that will run applications efficiently and optimize the performance of analytical models for users. In addition to bigtechs, other organisations such as DataRobot, H2O.ai and ThoughtSpot have also developed advanced analytics platforms.
To quote Gartner, analytics 2.0 has the potential to become the future of data analytics because it moves us closer than ever to the vision of ‘democratized analytics.’ Ten years ago, it was almost impossible to find a single business application driven by analytics. Ten years from now, we won’t find one that isn’t. Analytics 2.0 will be a driving force of this change.
Credits : Akhil Handa