Supreme NetSoft’s Big Data and Analytics capability is emphasized on large data sets to discover more about data patterns, Data correlations, Data trends, and recommend our customers with useful business information. Big data as we all know is more used for predictive analytics with low latency and high speed and the size is beyond traditional databases. It will have characteristics of high volume, high speed and high variety to ensure that decisions made out of this data will have dependency and accurate for business to make any decisions.
Most important aspects of big data are capturing the large sets of data, analysis of data and provide results/trends of the data back to business to ensure that business process definition will get benefitted by the entire framework and have full control on the business processes.
Data Capture: There are set of tools/Products available large sets of data to capture and the trend is to capture them on cloud environment as data set is large and un-predictable. Cloud technologies will help in scaling the data sets based on increased demands of data sets. Few of the current big data technologies in which Supreme NetSoft is focused on are Hadoop and Amazon’s red Shift. These technologies allow distributed data processing of large sets of data across clusters of servers. Most of the big data technologies are focused on Non transactional data sets and the objective of capturing the big data is to measure the data and experiment with an outcome for prediction.
Analytics: The captured big data is used for performing various analytics activities such as Social Analytics, Decision Science, Performance Management, Data Exploration etc. there are several techniques are used by Supreme Netsoft on behalf of customers such as Textual analytics, Sentimental analytics, Network Analytics, Predictive analytics, Analytics automation etc. for fulfilling customer needs on Analytics.
Our aim is to achieve:
- Convergence of Business sources, Instrumental Data and Analytical processes
- Convergence of Applications, Things, Data, and Devices
- Convergence of Machine Learning, Data scientists and Data
Below diagram provides the high level framework we adopt for Big data and Aanalytics.
We help our customers in:
- Building Big data frameworks for Analytics
- Integrating multiple data streams to get data aggregations
- Build a Big data and analytics data science capability
- Provide guidelines and policies for Data science and algorithms
- Build solutions on Big data and Analytics technologies like Hadoop, Data Lakes, Data blobs, NoSQL and many more on need based.