BIG DATA ANALYTICS

Description

The term “big data” tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Analysis of data sets can find new correlations to spot business trends, prevent diseases, combat crime and so on. Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research.

Big data often poses the same challenges as small data; adding more data does not solve problems of bias, but may emphasize other problems. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. There are five dimensions to big data known as Volume, Variety, Velocity and the recently added Veracity and Value.

Where did this data come from? The use of data-intensive technologies increases with developed economies(e.g. ITOA-IT Operations Analytics). While many vendors offer off-the-shelf solutions for big data, we recommend the development of in-house solutions custom-tailored to solve the company’s problem at hand if the company has sufficient technical capabilities.

Service offerings

  • Enterprise Data Warehouse
    • Data Archiving
    • ELT Offload Architecture
    • Datastore, Governance & Security Management
    • Self Service BI / Discovery
  • Data Science
    • Strategy & Roadmap
    • Prototyping & Tool Evaluation
    • Data Integration, access & services
    • Construction & Go-Live Enablement
  • Analytics
    • Real-time Ingestion
    • Scalable Data Processing & Storage
    • Analytics, Dash-boarding & Alerting