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MeData and Its Collection

MeData is a collected data value that contains information about a user or user’s possessions. The user’s Date of Birth, inferred age, mobile phone brand, downloaded apps, gender, Twitter tweets, income, average resting heart rate, and inferred personality class can all be examples of MeData.

MeData is the main building block of the DataSapien Platform and is base of all the functionality provided by it.

  • MeData Definitions define what MeData should be collected on-device, from which sources, and how it is stored.
  • Segments & Audiences group and target your customers for certain use cases based on their on-device stored MeData.
  • Rules contain MeData value-assertion-based conditions and trigger various actions to be performed when the condition is met.
  • AI & ML Models are downloaded and are given access to on-device MeData to enable insights & personalized outputs.
  • Journeys are designed and deployed to collect and use MeData (including on-device intelligence) and create meaningful outputs for your customers to increase loyalty and trust.
All On-edge

Note that all of the above happens on-edge inside customer mobile devices. Unlike traditional and regulatory problematic ways of gathering and storing personal data on a backend data environment, the DataSapien platform allows you to achieve all of these on-edge and in a 'human-centric' and regulation proof way, putting your Legal Teams at ease.

MeData Definitions​

To collect MeData, you should first 'define' it: how to name it, what possible values it may have, from which source and how to collect it. These all specify a MeData Definition.

Two important concepts for a MeData Definition are:

  • Source: specifies where the value(s) of the MeData Definition will be collected from. Native, Question & Answer, Script and Inferred are possible sources.

  • Value Type: specifies shape and data type of the values. Most of the value types are basic types such as string, number, boolean etc. Having a correct and well-defined data type allows error free processing of MeData in scripts and in intelligence layers.

Some MeData like Gender, Age, and Country of residence are common among different enterprises. But say, a shoe company may need Shoe size as a MeData definition, whilst a health insurance company may need Daily step count. One stores dates using a USA date convention (MM/DD/YYYY) and the other uses a European convention (DD/MM/YY).

DataSapien provides a common set of MeData Definitions out of the box but you can define your own in Orchestrator MeData Definitions Page. You can use customisable MeData categories to organize them.

DataSapien Mobile SDK periodically checks and fetches MeData Definitions from the DataSapien Orchestrator.

Data Discovery and Mapping Consultancy

DataSapien provides consultancy and a through analysis of your data strategy to help decide MeData Definitions you may need to boost your business. Contact us.

MeData Collection​

MeData Definitions must be referenced by a Journey to trigger collection. Depending on the data source, the SDK handles collection and storage in the on-device data vault as follows:

  • Native Sourced:
    • No Permission Required (e.g., Device Brand): Collected automatically at every SDK initialization. Available instantly within the Journey.
    • Permission Required (e.g., Health Data): The Journey prompts the user via a system permission UI. Upon approval, the value is collected and saved.
  • Question & Answer Sourced: The Journey displays the defined question to the user and saves their response directly to the vault.
  • Script Sourced: The SDK executes the designated script. If the script requires user interaction (e.g., a web API authentication screen), the UI is rendered within the Journey. The script's calculated output is then saved to the vault.
  • Inferred Sourced: The SDK does not automatically collect the value. Instead, the host application or a previous Journey must provide it. The active Journey should gracefully handle cases where no value is currently available.

Key Rule: Regardless of the source type, all processed or inputted MeData is securely stored locally within the on-device data vault.

Longitudinality​

The DataSapien Mobile SDK stores collected MeData values longitudinally. Rather than overwriting the previous entry, the SDK maintains a configurable history of past values for each data point.

This temporal, historical record unlocks a powerful axis for insight generation and audience segmentation. Teams can easily run local time-series aggregations—such as calculating averages, maximums, or minimums—over a defined window.

Example: Instead of checking a single snapshot, you can dynamically target users whose average daily step count over the last two weeks exceeds a specific threshold.

Beyond simple aggregations

The possibility of using historical MeData values is not limited to simple aggregations, you can implement any algorithm on MeData values in scripts that you author.