Data Analytics

Data mining tools empower enterprises to extract usable data from a large set of raw data and analyze hidden data patterns to categorize these patterns into useful information. Data mining managed services would be used extensively by enterprises to gain traction in the global data mining tools market. Managed services enable organizations to focus on their core business competencies while managed service providers mine data for them and provide insights from hidden data patterns.

These services further help organizations in focusing on customer-centric aspects and adding value to business operations.

The global process analytics market size is expected to grow at a Compound Annual Growth Rate (CAGR) of 50.3% during the period 2018 -2023.

Business functions such as share service center outsourcing, banking, financial services & insurance (BFSI), business process outsourcing (BPO), procurement outsourcing, and human resources outsourcing (HRO) are currently experiencing rapid growth. Thus, there is an enormous market potential for robotic process automation across various business verticals owing to its ability to perform a variety of tasks such as account opening and closing, completing requests for quotation and proposals, IT systems testing and monitoring, and handling queries in the billing and customer service departments.

A report published by Market Research Future states that the robotic process automation market is expected to grow at 29% CAGR between 2017 and 2023.

The creation and consumption of data continues to grow by leaps and bounds and with it the investment in big data analytics hardware, software, and services and in data scientists and their continuing education. The availability of very large data sets is one of the reasons Deep Learning, a sub-set of artificial intelligence (AI), has recently emerged as the hottest tech trend.

On the other hand, machine learning (ML) is an AI application that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it learn for themselves.

The increasing interest and investment in AI, in turn, will lead to the emergence of new tools for collecting and analyzing data and new enterprise roles and responsibilities.