Learn more about our fully comprehensive data model and machine-learning ecosystem.
Sparta Science provides a fully comprehensive data model and machine learning ecosystem that facilitates multi-level, multi-purpose capabilities and engagement. This enables full transparency, trust, and, overall, a unique level of data democratization and access.
To start, movement data is taken from its source (forces), and standardized with built-in error-detection and reliability software automations. Sparta Science is the only solution that provides this objective filtering as standard, which means the data and insights can be trusted and scaled. The table below categorizes different data outputs provided by the Sparta Movement Health Platform.
*All time-series data is archived in our secure data lake to enable the continuous and iterative improvement of and development of new relevant biomarkers. Additional biomarkers are continuously added, evaluated, and improved.
First and foremost, Sparta is driven by providing the most reliable and useful metrics that facilitate real-time decision-making in operational settings. This is where the Primary Biomarkers and normalized results are best placed to provide instant value and engagement.
While real-time actionability should always be the end goal, the option to interact with data for a range of research, education and customized problem-solving needs can also be an important capability. This is where the Time-Series and Raw Data are valuable. What should be noted is that these types of data generally require researchers and applied scientists to interpret, analyze and communicate meaning, and while important for learning and innovation, this data is less actionable and engaging to all stakeholders. An example could be exporting a large, longitudinal dataset and sharing this with a research and development partner to analyze and explore a range of bespoke questions.
While real-time actionability should always be the end goal, the option to interact with data for a range of research, education and customized problem-solving needs can also be an important capability. This is where the Time-Series and Raw Data are valuable. What should be noted is that these types of data generally require researchers and applied scientists to interpret, analyze and communicate meaning, and while important for learning and innovation, this data is less actionable and engaging to all stakeholders. An example could be exporting a large, longitudinal dataset and sharing this with a research and development partner to analyze and explore a range of bespoke questions.
Sparta is constantly reviewing millions of data points at all levels of its data model both internally to refine methods and strategically alongside its industry and research and development partners. Leveraging its aggregated global database and a constantly evolving feedback loop of machine learning techniques, Sparta continues to search and explore new data science approaches and metrics to improve movement health and performance outcomes.
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