{"id":15070,"date":"2020-12-21T20:58:24","date_gmt":"2020-12-21T15:28:24","guid":{"rendered":"https:\/\/coforge.site\/cigniti\/blog\/?p=15070"},"modified":"2020-12-21T20:58:24","modified_gmt":"2020-12-21T15:28:24","slug":"augmented-analytics-business-intelligence-solutions","status":"publish","type":"post","link":"https:\/\/coforge.site\/cigniti\/blog\/augmented-analytics-business-intelligence-solutions\/","title":{"rendered":"Deliver value faster with Augmented Analytics"},"content":{"rendered":"

Data and analytics have become critical for navigating an organization\u2019s strategic decisions. We have been talking about data being the new oil and the propeller for digital transformation for quite some time now. But the data alone is more or less useless \u2013 it is just numbers, figures, and some charts, which do not tell anything.<\/span>\u00a0<\/span><\/p>\n

For organizations to be able to develop, implement, and propagate well-informed and carefully-designed strategies, they need insights and not just plain numbers. The numbers need to tell a story about what has been, what is, and what will most probably be.<\/span>\u00a0<\/span><\/p>\n

The insights that the numbers offer constitute the function of business intelligence, which organizations have been using for years to determine their next course of action.\u00a0<\/span>However traditionally, business intelligence involved extensive manual churning of data to derive valuable information.\u00a0<\/span>Today, with the rapid increase in the scale and volume of data-generating elements, from IoT devices to smart phones, traditional business intelligence is demanding a swift pivot to\u00a0<\/span>a\u00a0<\/span>more efficient and less labor-intensive<\/span>\u00a0process.<\/span>\u00a0This is where Augmented Analytics comes in.<\/span>\u00a0<\/span><\/p>\n

Gartner defines Augmented Analytics as<\/span>\u00a0<\/span>\u201c<\/span>the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment.<\/span>\u201d<\/span>\u00a0<\/span><\/p>\n

Let us understand how Augmented Analytics, with the help of smart next-gen technologies like Artificial Intelligence, Machine Learning, and Natural Language Processing, is changing the face of data-driven business intelligence.<\/span>\u00a0<\/span><\/p>\n

Limitations of traditional Business Intelligence<\/span><\/b>\u00a0<\/span><\/p>\n

Traditional Business Intelligence process comprised two parts \u2013 data collection and data analytics.<\/span>\u00a0<\/span><\/p>\n

The process required employment of highly-skilled professionals with rich technical expertise to collect data from multiple, disparate sources, and then apply analytical scheming to the collected information to produce business-critical information. The main drawbacks of\u00a0<\/span>this process are:<\/span>\u00a0<\/span><\/p>\n