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Big Data Analytics

Big Data analytics is a process of examining large set of data by collecting, organizing and analyzing to uncover hidden patterns, unknown correlation, market trends and other insights. Big data analytics bring information contained within the data and help company and business to take better decision for future strategy, lead effective marketing, better revenue opportunities, competitive advantage over rival organizations, improved customer service and operational efficiency.

The data sets are so large and complex that traditional data processing system cannot handle it. With today's technology, business can identify insights and get answers immediately with big data analytics.The main advantage is speed and efficiency . The ability to work faster and stay agile gives organizations a competitive edge.

Big data requires a set of techniques and technologies with advanced tools (analytics and algorithms) and simply use of predictive analytics, user behavior analytics and other advanced tools with new integration to reveal insights from data sets that are diverse, complex and massive and extract value from dataset of a particular size.

The main challenge with traditional data warehouses based on RDS includes analysis, capture, search,sharing, storage, transfer, visualization, querying , updating, information privacy with semi-structured and unstructured data may not fit well . Real or near-real time information delivery is one of the defining characteristics of big data analytics. Latency is therefore avoided wherever possible.

Why is big data analytics important?

Big data analytics helps companies to utilize their data to identify new opportunities that brings more efficient operations, smart business moves, high profits and happier customers.

1. Cost reduction: Big data technologies such as Hadoop and cloud-based analytics bring cost advantages to store large amount of data.

2.Speed and better decision: Big Data technologies analyze data immediately and to help organizations to make better decisions.

How it works and key technologies

To analyze large volume of data, there is no single technology that holds big data analytics but combination of several types of technology highly integrated working together that comprises of specialized software tools and applications for predictive analytics, data mining,text mining, forecasting and data optimization.

Key Technologies

Data Management - Data needs to be in high quality with integrity and well governed without interference,in an easy to use format that can be reliably analyzed.

Data Mining - Data mining technology is computational process helps to examine huge amount of data to discover patterns in the data using AI, statistics, machine learning which is used for further analysis.

Hadoop - This open source java based software framework that supports the processing and storage of large amounts of data and run applications on clusters of distributed computing environment.

In-Memory Analytics - . The queries and data which needs to be analyzed are stored in server random access memory(RAM) rather than hard drive, by which immediate insights from the data can be drawn.

Predictive Analytics - Using advanced analytics techniques from data mining, statistical algorithms, modeling, machine learning and AI to analyse data to make future prediction based on historical data.

Text Mining - : It is the analysis of data contained in natural language text.It can help organization to derive business insights from text based contents such as word documents, email,blogs,facebook, twitter, feeds, surveys and competative intelligence.

At ITH provide research, software development,consulting ,knowledge mining and training in related technologies of data mining.