Understanding that business insights are key to bringing in efficiencies in the business process, Veritis team of Data Analytics experts have been providing solutions.
How we achieve it
- Detailed study and evaluation of your models and process.
- Implementation of appropriate model and evaluation of fitness of the model into your organization.
- Logical, agile architecture of design and production process.
- Dynamic data service delivery model of Veritis helps you to visualize your data in the profits section of the balance sheet.
- End-to-end enterprise data management solutions to support the organization’s transactional and Decision Support Systems.
- Business requirements analysis and data modeling
- ETL architecture and development
- Custom reporting and OLAP solutions: data mining
- Upgrades and Migrations
Why Big Data is Becoming Important Now?
RISE OF SMARTPHONES
WITH GPS & INTERNET
There are 4.6 billion mobile-phone subscriptions worldwide and there are between 1 and 2 billion people accessing the internet.
AERIAL SENSORS &
The NASA Center for Climate Simulation stores 32 petabytes of climate observations and simulations on the Discover super-computing cluster.
Facebook has 1.06 billion monthly active users with 30 billion pieces of content shared on Facebook every month.
There are roughly 175 million tweets every day, from more than 465 million accounts.
- Big Data
- Big Data
- Big Data
HOW Big IS BIG DATA?
2.7 Zeta bytes (that’s 27 with 21 0s after it) of data exist in the digital universe today.
By 2020 analysts predict the amount of data will be 50x what it is today.
In 2012 90% of all the data that existed in our entire history had been created in the previous 2 years.
Every 2 days we create as much information as we did from the beginning of time up to 2003.
Data Science is a technology sphere which requires deep dive into business problems. The Data science team at Veritis has been involved in solving complex problems of clients in various industries.
Data manipulation & analytic applications addressing automation, application development and testing.
Data modeling covering key areas like experimental design, graphical models and path analysis..
Statistics and machine learning through classical and spatial statistics, simulation and optimization techniques.
Text data analysis through pattern analysis, text mining and NLP by developing and integrating solutions or deploying packaged solutions.