Data
Revolutionizing Healthcare with AI and Digital Technologies:
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into life sciences is a game-changer, offering unprecedented capabilities in data analysis, discovery and predictive modeling.
Therefore, wearable devices, mobile apps, and remote monitoring systems are not just tools for health management, they are catalysts for a more personalized, effective, and efficient healthcare system.
We believe that data science is a powerful tool that can transform businesses and we help you leverage your data to achieve your goals.
Our Engagements
IT&M Stats is committed to providing its clients with innovative services tailored to their needs. We are also engaged in the training and development of our consultants.
We have an international team ready to take inhouse your projects or to deliver them on your systems as a dedicated team.
We aim to bring more flexibility in our solutions:
- Offer a mode of operation complementary to the consulting
- Provide technical skills state-of-the-art
- Develop and deliver, to clients but also our teams, quality training adapted to each specific situation
- Capitalize on knowledge and experience through various projects, such as production of catalogues, guides regarding data technology
Data technology is the heart of our DNA
In other words, we are excellent in gathering and controlling information to buildup databases, playing with, and displaying data for decision making, as well as reporting and popularizing analysis outcomes.
We have experience delivering in different life sciences areas such as pharma, medical devices, biotech, agri-food, veterinary…
Such exposure has contributed to straighten our data operations knowledge and comfort us in extending our expertise to secure data quality through the management of the well-known 5 dimensions of data quality.
The 5 dimensions of data quality
- Consistency (Conformance in representing and validity in the meaning for coherence across the datasets)
- Extensiveness (Completeness and coverage or sufficiency for the purpose of the project)
- Reliability (Accuracy and precision for faithful representation of collected information)
- Relevance (Right type of data for research query or analysis strategy)
- Timeliness (Right time data availability to the right person for decision making)
Definition of data requirements:
It is essential to determine the data required and its nature from the outset of the project, particularly as some of these elements are directly involved in evidence generation.
- Data collection or generation: gaining data reflecting the observed reality.
- Data management and processing: including data transfers, normalization, and cleansing.
- Data publishing: making data available to consumers.
- Data procurement and aggregation: sourcing data from one or more consumers.
- Testing and acceptance: assessing the suitability of the procured data for intended needs.
- Data exploration : predictive data to support specific activity for example analysis.
Last but not the least, IT&M Stats brings its business expertise to slide the ever-increasing data volume challenges into an opportunity of more detailed analysis that lead to swelling rigorous life sciences interpretation using artificial intelligence and machine learning.