Data Analytics Services

Our Data Analytics team combines deep understanding of business problems with technological capabilities. We apply our analytical and implementation skills to provide our clients with solutions to intractable problems. R&D approach is to solve broader problems in a modularized way so that our Clients are able to have lasting solutions.

AI/ML For Enterprises

We have used tools such as trend analysis, cluster analysis for decades to help identify business insights. With the renewed focus on Neural Networks and Machine Learning and the tools making it easier to use them, there has been an explosion of possibilities. However, finding the right tool, selecting the right features and ability to step through a model->POC->Implementation ->Adoption cycle across an enterprise requires a wide range of skills including those of statistical modeling, domain knowledge, data wrangling and solution architecting. ET helps companies model and develop solutions after laying a firm foundation. We have tied up with some marquee institutions and companies to deal fashion the most appropriate solutions.

Predictive Analytics

From exploratory analysis of information to statistical inferences and prediction, the needs of the business are varied. Predictive Analytics can be used for marketing purposes as well as algorithmic tuning. ET’s data analysts have expertise in algorithmic tuning, data modeling and predictions. Working with domain specialists we provide very specific solutions to our Client’s data needs, whether your need is in IoT, Enterprise syslog analysis, smart Factory or marketing and sentimental analysis.

Big Data Analysis

A typical data science project starts with a rigorous defintion of the ins and the outs, followed by data wrangling and data cleansing/transformation that takes a substantial time. An issue of significance is the handling of big data. An ill-designed database that is meant solely to hold large amounts of data is a disaster to be avoided.

Like a well-designed experiment that does away with useless cases, analysis of terbytes and petabytes of data should be well defined both for current and future uses while allowing the requisite flexibility to use and change various technologies. ET works with some of the niche as well as large vendors to fashion a solution that ages well with time.

Whether the data is IoT machine data, SNS streams (Twitter, Facebook etc.) or data from a datawarehouse the balance between technology (database, I/O, memory vs. disk), analytics (AI/Deep learning, NN etc.) and usage (batch vs. real-time, interconnections etc.) on the one hand; and technology adoption, security and cost on the other hand is important.