Since 2018, Gartner said that augmented analytics was set to change data analysis and business intelligence processes. Kevin Halkerd, senior IT risk and security analyst, e4 says that while he feels that the change had already begun, augmentation will bring about the change that is required to affect positive change and outcomes of data analysis.
“We need to use augmentation rather than automation to not only lighten the load of the analyst, but to also enhance cognitive abilities and reduce the cognitive bias of the data analytics. This will ultimately help to address the extreme volumes of information we currently have available as big data continues to grow unwaveringly,” says Halkerd.
According to definitions, augmented analytics automates by finding and surfacing the most important insights or changes in the business to optimise decision making. It does this in a fraction of the time compared to manual approaches and makes these insights available to all business roles.
Halkerd says that augmentation by means of Machine Learning (ML) and Artificial Intelligence (AI) processors is vital in any data operation as techniques to automate data preparation, insight discovery and sharing: “Consider a Security Operations Centre that processes billions of logs from hundreds of devices monthly or the large FMCG manufacturers running 24/7 manufacturing, to the distribution operations and SME eCommerce vendor, all that are too busy to expand their insight. These tools, together with augmentation analytics, will provide viable business outcomes and assist with knowledgeable decision making.”
It is growing more evident that augmented analytics is distinctively different to the tools used today. Halkerd says that augmented analytics integrates AI elements into the analytics and BI process to help users prepare their data, discover new insights, and easily share them with everyone in the organisation: “This will feel different because the user experience across the BI process will be vastly different due to the subtlety of augmented analytics’ and the integration of AI and natural language processing (NLP) elements.”
The process will become more streamlined and powerful thanks also to NLP, which in a post Covid-19 world will become more popular: “There are effective benefits to NLP. In a post Coronavirus world, simpler NLP solutions like OCR (Optical character recognition), which is the core technology for automatic text recognition, will see a growth in popularity. Banks will start moving towards true digitisation but will see further enhancement to allow measures like counter-fraud analytics to be applied.”
Augmented analytics will continue to develop and build on several AI and BI trends to a point where analytics and BI will become an immersive, always-on environment.