ROOK is developing an optimization for Granular Data a new feature designed to provide clients with more detailed and standardized information.
Many companies working with health data require precise and consistent information. Until now, Granular Data from different sources arrived at varying times, leading to inconsistencies in analysis and making data-driven decision-making more challenging.
Greater control and standardization
Granular Data allows clients to customize the reception of information
- Temporal Standardization: Data from multiple sources is unified within the same time frame, preventing inconsistencies in user graphs and analyses.
- Personalized Management: Clients can choose to receive data as delivered by original sources or apply intelligent adjustments filling in missing data using averaging algorithms and AI for greater accuracy.
- Duplicate Elimination: When duplicate data is detected from different sources, the system prioritizes the most reliable and relevant information.
Impact on our Clients
With Granular Data, clients and their users will access more reliable health information, facilitating the development of innovative products and insights.
Our goal is to deliver more useful and accurate data, allowing clients to focus on what truly matters: Innovation and strategic decision-maker.
Frequently Asked Questions
What is Granular Data Standardization?
Granular Data Standardization is a ROOK feature that ensures data from various health sources (Such as Garmin, Apple Health, Health Connect) is aligned within a unified time frame, reducing empty values and duplicates for more consistent and reliable insights.
Why is Granular Data Standardization important?
Health data is received at different intervals and formats depending on the sources, leading to inconsistencies in analysis. Standardization ensures greater accuracy, making data easier to interpret and enhancing the end-user experience.
What are the Benefits of Granular Data Standardization for Clients?
- Temporal unification: Ensures data consistency for more reliable analysis.
- Duplicate Elimination: Removes redundant data from multiple sources.
- Customization Options: Allows clients to receive raw data or apply intelligent algorithms for enhanced accuracy.
How Does Temporal Standardization Work?
Temporal standardization adjusts data to arrive at a consistent interval. For instance, if one source sends data every 5 seconds and another every 20 seconds, intermediate values can be estimated using averaging or artificial intelligence algorithms.
Can Clients Choose How to Receive the Data?
Yes, Each client can choose whether to receive data as delivered by the original source or prefer it to be adjusted using standardization mechanisms and missing data filling techniques.
What Happens with Duplicate Data?
If a metric is received from multiple sources (E.g., Garmin And Apple Health), a priority criterion will be applied to select the most reliable one, preventing duplicates in the analysis.
What Types of Data Are Being Standardized?
Some of the data that has already been standardized includes:
- Physical Activity: Steps, distance traveled, activity level, cadence.
- Heart Rate and Variability: HR, HRV, RMSSD, HRV SDNN
- Oxygenation and Breathing: Oxygen saturation, VO2
- Other Metrics: Elevation, torque, speed and power.
What Happens If a Source Does Not Provide Data at the Same Interval as Others?
If a source sends data less frequently, imputation methods can be applied to fill based on averages and predictive algorithms.
How Is Granular Data Configured?
Clients interested in this solution should contact their Customer Success representative to request this feature. Please note that this feature comes with an additional cost beyond the client´s existing plan.
Does Granular Data Affect Data Processing Performance?
No, We have implemented optimizations to ensure that standardization does not slow down data processing, seamlessly integrating into existing pipelines.
What Happens with Null/Empty Information?
When searching for a consistent period in granular data, there may be cases where no data source contains the value corresponding to the generated timestamp.
In these cases, the client has 4 options:
- Mark the missing data as empty
- Fill the missing data using the last available
- Fill the missing data using the average of the neighborhood.
- Fill the missing data using the median of the neighborhood.
How Can I Get More Information About This Feature?
For more details, you can contact our support team.