Big data has made some lofty promises. And our clients are reaping the rewards. With over 300 Data Scientists, Data Engineers and Visualization Experts, we apply science and the latest tools and technologies to improve decisions at the world’s largest companies. Whether you are just getting started, or already have a sophisticated architecture in place, the Big Data Managed Services team at Absolutdata can have significant impact. We integrate disparate data, design on-demand machine learning models and build custom reports and dashboards to unlock the full potential of any big data platform.

  • Case study 1
  • Case study 2
  • Case study 3


The power of Big Data is multiplied manifold when different data sources are combined. We have the technical and business knowhow required to efficiently and effectively combine various data sources. Clients receive analytics ready data that is primed to deliver insights and drive decisions.

  • Pull data from a variety of sources
  • Combine technological and business information
  • Implement a hybrid architecture
  • Get an in-depth look at the whole company
  • Explore more Data Integration services
  • Case study 1
  • Case study 2
  • Case study 3


Setting up a Big Data ecosystem is a challenge. Data warehouses and data lakes are an essential component of these systems. An intelligently designed data warehouse meets your needs now and in the future.

  • Plan data warehouse capacities for current and future use
  • Optimize Big Data infrastructure for your workflow
  • Incorporate data from new and traditional sources
  • Blend the right technologies to achieve specific goals
  • Build optimized data structures
  • Case study 1
  • Case study 2
  • Case study 3


Extract insights from fast-moving data on the fly with big data analytics. Absolutdata uses machine learning, text analytics, and other advanced techniques to provide information that businesses need to react to events and make better decisions faster than ever.

  • Increase efficiency of the data analytics process
  • Apply modern techniques like Naïve Bayes, Hidden Markov Models, Gradient Boosted Models, Supervised and Unsupervised Learning, self-learning models, etc.
  • Test and adopt new tools such as Hadoop, SAP Hana, Hive and Python to generate more impactful results
  • Draw a more complete picture of your information
  • Incorporate advanced analytics like complex event processing, sentiment analysis and SPSS modeling
  • Leverage the expertise of over 100 data scientists and data engineers
  • Case study 1
  • Case study 2
  • Case study 3


Data visualization turns raw data into meaning and meaning into understanding. Business Intelligence takes technology and translates it into the words of the business world. Together, these revolutionary tech applications clearly address your toughest questions.

  • Build industry-specific templates
  • Design executive summary dashboards
  • Get data-driven insights as they occur
  • Access results fast with in-memory visualization
  • Develop action plans based on clearly presented data
  • Access dashboards on mobile devices
  • Case study 1
  • Case study 2
  • Case study 3


Predictive and behavioral analytics allow businesses to quantify and forecast the value of individual customers across time, product lines, segments and even channels. The ability to identify and curate high value customers brings clarity to marketing strategies and initiatives.

  • Design and deploy solutions for internal or external use
  • Map custom-built application to business needs
  • Leverage our product managers, business experts, UI specialists
  • Support campaign optimization with NAVIK Converter
  • Run concept tests with NAVIK Concept Test
  • Reduce sales cycle time with NAVIK for Sales
  • Case study 1
  • Case study 2
  • Case study 3


Get customized big data applications designed to explore, analyze, and manage a wide range of data based on your needs. Data types that these applications can handle include streaming, traditional, and unconventional data that previously was difficult to incorporate.

  • Get mobile or web-ready apps
  • Support real time, near real time or batch processing
  • Collaborate on the most effective UI design
  • Integrate the app with your existing big data framework