The global Big Data and Analytics industry has witnessed major disruptions in the last few years. In India, the analytics industry has grown to $3.03 billion in 2019 and is expected to double by 2025 with outsourcing being the main driving force behind its growth. Furthermore, Bengaluru is said to be the biggest hub for analytics with leading MNCs and captives setting up analytics CoEs in the city. Although, we are in a nascent stage of a data era, companies are increasingly investing in data analytics capabilities to keep up with the evolving market trends. Today, most companies are implementing a Data Quality Management (DQM) policy, department, or techniques so as to leverage the impact of data quality on analysis and further decision-making process.

With the amount of data that is being generated today, it has been rightly quoted by Pat Gelsinger, CEO at VMware that, “Data is the new science. Big Data holds the answers.” The scope of big data has changed and is evolving at an outstanding pace; business enterprises need to implement the right data-driven big data analytics trends to stay ahead in the competition. Here are some of the Big Data Analytics trends that are reshaping this domain: 

IoT Networks

While new sensor, mobile and wireless technologies are driving the evolution of the internet of things (IoT), the true business value of the IoT lies in big data analytics rather than hardware novelties.   Business houses will rely on more data points to collect information for more detailed business insights.

Advanced Analytics

With comprehensive set of analytical techniques and methods, Advanced Analytics can help businesses discover trends/patterns, solve problems, accurately predict the future and drive change using data-driven, fact-based information. The major areas that make up advanced analytics are predictive data analytics, big data, and data mining.

Open Source

Open-source-based data analytics tools can help companies in facing challenges using big data and innovate and grow their businesses with more versatile approach. As compared to the upfront cost of purchasing a proprietary software license, the cost of the open source software is low, bringing an obvious advantage for the businesses.  

However, the development of business intelligence to analyze and extract value from the countless sources of data, gathered at a high scale has also brought alongside a bunch of errors and low-quality reports: the disparity of data sources and data types has added more complexity in companies’   data integration process.