Smart analytics that understand science.
WHY BIG ANALYSIS
We need to leverage big data and machine learning
as new resources instead of thinking of them as new costs.
4 Vs of Big Data
Benefit from our knowledge.
BIG DATA TRENDS
Data continues to grow in both size and complexity.
Many of the Big Data approaches focus on the narrow issues of data size and performance. So companies are now shifting their big data technologies to Data Lakes. This trend is spreading across many industries with the hopes of providing unprecedented capabilities for data integration and data analytics.
Big Analysis is more than running statistics, number crunching and pushing big data against Hadoop clusters. The result is more than graphs and disconnected data. Tactic or Trend – The Potential of the data lake
LEARN TO USE YOUR DATA
The result is improved capture of data types/sources, from laboratory instrument data, to clinical data, to regulatory rules & submissions, all the way to business drivers for the enterprise. Providing advanced analytics capabilities that can be built as modules and expand across an enterprise.
Big Analysis is the very generic concept of bringing Semantic and Statistical concepts together. We invite you to learn more about Semantics, Machine Learning and Pragmatic AI on our YouTube Channel.
Meet the Experts
OSTHUS possesses a team of world-class theorists, developers and engineers with decades of experience in designing, building and deploying state of the art software systems for a large varierty of industries and applications.
Data Science Expert , Thoughtleader, Philosopher
Data Fusion, Algorithms Design, Analytics, Machine Learning
Data Architect, Linked Data Enthusiast, Project Leader
Bioinformatics, Semantics & Databases
OSTHUS brings innovative scientific lab informatics applications
to the world’s largest research and development centers. Experts at partnering
to guide your business strategy to successful IT solutions. With teams of
unique interdisciplinary experts, we help our clients solve the impossible in
global data-driven challenges in Pharma and Life Sciences.