Automation has played a considerable role in managing and performing multiple tasks for large enterprises. Dr Frederick Winslow Taylor, father/pioneer of ‘Scientific Management Studies’ and author of ‘The Principals of Scientific Management’ (1909), is reported to be the first who introduced the formalized system of Business Analytics in late 19th Century while analyzing production and productivity increasing techniques.
Later, when Time Studies/Motion Studies/Fatigue Studies began to be used in production processes in 1950s and when computers were used for automatic data processing in the 1960s, Analytics was used (by applying mathematical/statistical models) to provide solutions to problems identified in organizations.
However, the unprecedented upsurge in Internet and Information Technology further boosted the performance of businesses. To keep interests of organizations at utmost high level, new technological solutions have been found effective than ever before.
Business Analytics is one of the significant causes that have contributed immensely to guiding businesses towards productivity and profitability. It is an emerging field of study that has attracted the attention of academia around the globe.
Its main purpose is to improve the accuracy and efficiency of decision-making. Business Analytics, simply put, is about leveraging value from data. Analytics applied to Business Data focuses on the business implications of data and the decisions/actions that should be taken as a result.
It is the process of collecting, organizing, analyzing, and interpreting data using quantitative/statistical methods and technology, to make informed business decisions and formulate strategies. Business Analytics is a composite field of Computer Science and Business (Data) Studies that uses Mathematics, Statistics/Quantitative techniques and ML to find meaningful patterns in data.
This analytical discipline has evolved from just displaying the facts and figures into more collaborative business intelligence that predicts outcomes and assists in future decision making. It engages in all phases of data analysis, making forecasts and building models to enable better management decisions.
It provides actionable insights into customer behaviour/preferences along with comprehensive market analysis/trends, thereby providing a competitive advantage to organisations. It provides innovative solutions that help businesses comply with the demands of investors and export markets by streamlining procedures. To cite an instance, FinTech (Financial Technology) provides consumers and businesses with better, faster and quality financial services and products.
Business Analytics, same way, can be applied to various other areas of business like Sales and Customer Services, Marketing, Human Resource Management and Production Operations. By analyzing data from these functional areas, organizations can identify trends, patterns and correlations that can help them make informed decisions, optimize their business processes, enhance performance, leverage competitive advantages and create values.
Due to all these reasons, Business Data Analytics is steadily gaining currency across globe. Algorithms powered through enormous data have been determining how benefits could be reaped by government, business, civil society to education, healthcare and financial services alike. It may be indicated here that under Joseph Schumpeter’s Creative Destruction Theory, new jobs that do not exist yet will replace the ones that will be made obsolete. In this context, continuous learning, unlearning, relearning, upskilling and reskilling will be essential for all, blue-collar as well as white-collar workers.
