Discovering, extracting, and analyzing data patterns in textual data from the myriad data sources streaming into modern data-driven organizations is no easy task. Organizations must be equipped with state-of-the art techniques such as Natural Language Processing (NLP) within well-developed Artificial Intelligence (AI) and Machine Learning (ML) platforms, to reliably understand the pulse of their consumers in real time, while also controlling the data deluge that often overwhelms under-prepared organizations.
Pattern recognition is identified as a key human skill that has supported the rise of people to become the dominant species. However, there are limits to this crucial ability, especially when confronted with masses of information that differ only slightly. Small, but significant, variations are more easily recognised by machines that can minutely inspect and compare differences without fatigue and with low margins of error. Humans are, thus, using machines to augment their pattern-recognition capability by teaching machines how to recognise patterns and correlate seemingly disparate data to gain new insights. "Specialised machine-learning algorithms are used to evaluate large quantities of data and derive and/or exploit relationships in the data," says IBM Watson Advanced Cognitive Technology and Solutions data scientist Stefan van der Stockt.
Decision intelligence is a framework that supports data and analytics architects model, align, develop, implement and track decision-making models and processes. Decision intelligence is thought to have a huge impact on business results and performance, with Gartner forecasting that over 33% of organizations will have analysts that practice business intelligence by 2023. Decision intelligence connects business problems and applies data science to find appropriate solutions. For this to be achieved, stakeholder behaviors need to be analyzed and incorporated into the decision-making process. Data intelligence is best described as an amalgamation of data science, business intelligence, decision modeling, and overall management.
Artificial Intelligence (AI) is poised to have a transformational impact on business. Information technology is no longer just about process automation and codifying business logic. Instead, insight is the new currency, and the speed with which we can scale that insight and the knowledge it brings is the basis for value creation and the key to competitive advantage. According to Gartner, AI will be one of the top five investment priorities for more than 30% of CIOs globally, by 2020. Many organizations are still early in their data science journey and are trying to understand how AI can transform their businesses.
While COVID-19 pandemic has had a huge impact on people, function and process in innumerable ways, it has brought about an acceleration in the adoption of digital transformation across business and social sectors. The industry needs to rapidly ramp up on skills required to manage this rapid digitalisation. One of these critical skills is Data Engineering – in fact the DICE report of 2020 has labelled Data Engineering (DE) as the fastest-growing tech job with a 45% year-on-year growth. The pioneers of formal Business Analytics/ Data Science education in India, Praxis Business School, are launching a 9-month full-time post-graduate program in Data Engineering to address the business need for people with these skills. This course by Praxis is supported by industry giants Genpact and LatentView, who are providing industry inputs and know-how to strengthen the program.