Rethinking laboratory data

Smart analytics that understand science.

Statistics

Algorithms that can exploit raw data in flexible ways, including: clustering, trending, tagging, inexact  matching, and
pattern recognition across structured or unstructured sources.

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Semantics

Logic-based models that provide reference data structures in the forms of controlled vocabularies, taxonomies, and layered ontologies. Data models now be used by both humans and machines.

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Machine Learning

Applying self-correction capabilities to algorithms so new patterns and insights can be gained automatically by machines. Improve statistical analytics and system performance.

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Reasoning

Utilize both deductive and inductive logics which can run over and against semantic models.  Provide advanced forms of query patterns, automated rules, and new logical patterns of interest.

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Why Big Analysis?

Silos Exist

Data and information exist in digital and human silos.

Non Digitized Information

Knowledge is scattered across the organization.

Lack of Common Vision

Missing alignment between business units and scientists.

Lack of Understanding

Data schemas are not explicitly understood.

Briddle/Rigid Integration Strategies

Software systems are often proprietary. Legacy architectures are brittle and rigid.

Compatibility Issues

Software systems are often proprietary. Legacy architectures are brittle and rigid.

We need to leverage big data and machine learning as new resources instead of thinking of them as new costs.

4Vs of Big Data

Big data Volume, Velocity, Variety and Veracity can be only all be tackled using a new form of Data Science which combines formal ontologies with statistical heuristics at large scale leading to new and powerful models that allow for bigger & better analytics – or as we call it Big Analysis.
MASTERING VOLUME

Data is ever increasing in size and will continue to do so into the foreseeable future.  The ability to manage large data sets relies on skills around NoSQL database technology, indexing and advanced search.

OSTHUS has experts in these Big Data technology platforms allowing us to scale solutions for enterprise-level needs.
MASTERING VARIETY
As new technologies emerge, so to do the types of data they produce.  Companies today must leverage a wide variety of data sources in order to solve real-world complexity and hidden/dark data problems. OSTHUS’ knowledge of semantic integration provides a means to integrate data from a wide variety of sources, allowing users to answer even the most complex problems.
MASTERING VELOCITY
The world does not sit still – and neither should your data.  Data velocity can pertains to the ever increasing rate at which data flows into modern organizations from new sources. The modern speed of business requires organizations detect, react and pivot faster than their competition. OSTHUS has skilled personnel who know how to help keep up with your data and ever changing world.
MASTERING VERACITY
Veracity refers to the uncertainty of data.  Much data in the world is probabilistic in nature – meaning it is “fuzzy”.  In this sense, understanding data’s veracity often allows one to better understand issues of truth and associated data quality.  Decisions made on unreliable information are bad decisions. OSTHUS’ capabilities in statistical analytics provide a means to detect, deduce and exploit uncertainty and better understand the dangerous unknowns in your data.

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.

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Free Webinars

Demystifying Semantics

Practical Utilization of Semantic Technologies for Real World Applications. This webinar explains semantics, the separation of model/data, graphs, applying meaning to data.

Why you should consider using these technologies with real world examples and how to pick the right tech for your needs.
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Smart Data For Smart Labs

The use of semantic technologies to integrate laboratories instruments, SME knowledge and legacy data systems by providing a common reference layer.

A demonstration of use cases for semantic technologies and analytics providing unprecedented business value to customers.
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Improve Your Lab Maturity

As devices become smarter and produce data about themselves, it will become increasingly important for scientists to take advantage of more powerful machines, as well as more powerful data techniques, including:semantic technologies, analytics, machine learning, natural language processing and trending.


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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. 

Eric Little

Chief Data Officer

Data  Science Expert , Thoughtleader, Philosopher


Geoff Gross

Senior Data Scientist

Data Fusion, Algorithms Design, Analytics, Machine Learning


Heiner Oberkampf

Senior Semantics Engineer

Data Architect, Linked Data Enthusiast, Project Leader


Larry Crochet

Big Data Engineer

Multidisciplinary Orchestrator of  Semantic Solutions


Contact The Experts!

Contact us to get started today.

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.

www.OSTHUS.com