Data Analytics

About Image
About Image


In today’s world data is the new Gold. Similar to gold more the data is refined more will be the valuable information out of it. Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and the techniques vary according to the organizational requirements. Data Analytics involves applying algorithmic processes to derive insights.

An example is running through a number of data sets to look for meaningful correlations a each other. It is used in a number of industries to allow them to make better decisions as well as verify and disprove existing theories or models. The usage of Data Analytics in various domains and areas can yield valuable details to enhance an organization’s growth in all aspects.

Data analytics focuses on digital transformation: multi-disciplinary teams are dedicated to help and identify key points of new competitive differentiation, and then use emerging technologies and market-specific expertise to build and implement business processes that will set organisations far ahead of the competition. Data Analytics gets applied in a variety of areas: marketing analytics, business analytics, risk analytics, supply chain analytics, digital analytics, pricing analytics, and social media analytics.

Hadoop: The New Enterprise Data Operting System

Hadoop is a framework and set of tools for processing very large data sets and originally designed to work on clusters of physical machines. These systems allow for performing many different data manipulations and analytics operations by plugging them into Hadoop as the distributed file storage system. Analysts have not only more data to work with, but also the processing power to handle large numbers of records with many attributes. While the traditional machine learning uses statistical analysis based on a sample of a total data set, the combination of big data and compute power lets analysts to explore new behavioral data in real time.

In-Memory analytics

The use of in-memory databases to speed up analytic processing is increasingly popular and highly beneficial in enterprise data analytics. In fact, many businesses are already leveraging hybrid transaction/analytical processing (HTAP) — allowing transactions and analytic processing to reside in the same in-memory database.


    Our expertise in AI and ML in general and in data analytics in particular can help our customers to be very competitive in their market place.