VIP-Global’s Big Data and Analytics practice works to make your data useful. Your data holds "secrets" which are awaiting to be discovered to provide new insights in your business, a better understanding to make better decisions and optimize processes.

VIP experts will work with you selecting the right software or tool as well as designing the infrastructure that is both integrated and compatible with your existing infrastructure and operation. VIP has the expertise to prepare your data (ingest, link, match, and cleanse) to enable data relationships and hierarchies.

Our services include:


VIP-GLOBAL works on face-to-face or virtual-remotely style compiling critical data points from your processes and operational databases. Providing remote support is about seeking information from multiple sources and quickly forming conclusions about what's happening, what could happen and what to do to ensure continued positive or improved performance.


VIP-GLOBAL’s Data Analytics Consultants assisted by specialized analytics systems and software, study big data analytics and point the way to various business benefits, including new revenue opportunities, more effective marketing, better customer service, improved operational efficiency and competitive advantages over rivals.


Our data analytics professionals analyze growing volumes of structured transactional data. They also study a variety of forms of data that are often left untapped by conventional business intelligence (BI) and analytics programs. Types of data we work with include:

  • Semi-structured and unstructured data – That includes, internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile-phone call-detail records and machine data captured by sensors connected to the internet of things.
  • Data Analytics of data sets and subsequent drawing of conclusions about organizations and how they can make informed business decisions.
  • Advanced Analytics, which involves complex applications with elements such as predictive models, statistical algorithms and what-if analyses powered by high-performance analytics systems.
  • Hadoop distributed processing framework (Apache) open source. A clustered platform built on top of commodity hardware and geared to run big data applications.
  • Hadoop ecosystem for large internet and e-commerce companies, as well for retailers, financial services firms, insurers, healthcare organizations, manufacturers, energy companies and other mainstream enterprises.

  • Data Warehouses that are based on Relational Databases oriented to structured data sets.
  • NoSQL databases as well as Hadoop and its companion tools, including: YARN, MapReduce, Spark engine, HBase, Hive, Kafka, Pig, Hadoop Clusters and Analytical Databases for analysis
  • Organization of Hadoop data lakes that serve as the primary repository for incoming streams of raw data. Data distribution by Hadoop Distributed File System and extract, transform and load (ETL) integration jobs and analytical queries.
  • Data Mining, Predictive Analytics, and Machine Learning, which tap algorithms to analyze large data sets; and Deep Learning, a more advanced offshoot of machine learning.
  • Statistical Analysis and Data Visualization tools. For both ETL and analytics applications, queries can be written in batch-mode MapReduce. Python and Scala; and SQL.
  • Relational databases SQL-on-Hadoop technologies and other open source stream processing engines, such as Flink and Storm.