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 its 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.
- ML & AI 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.
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, from which source and how to collect it and what possible values it may have. These all specify a MeData Definition.
One important concept for a MeData Definition is its Value Type. 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). A powerful function of the DataSapien Platform is that it allows you to define your MeData, to fit with your exitsing data structure and taxonomy conventions - thus create new MeData Definitions.
DataSapien provides a common set of MeData Definitions out of the box but you can define your own in Orchestrator. You can use customisable MeData categories to organize them.
DataSapien provides consultancy and a through analysis of your data journey to help decide MeData Definitions you may need to boost your business. Contact us.
MeData Collection
DataSapien Mobile SDK periodically checks and fetches MeData Definitions from the DataSapien Mobile Backend. Collection of MeData is executed by the Mobile SDK according to the source of MeData Definition. Source can be a question displayed to user or it can be a script that consumes an external API via a Managed API. The Mobile SDK will take correct action (eg. execute the script) to collect the MeData value and store it on-edge.
Longitudinality
The DataSapien Mobile SDK collects and stores MeData values longitudinally.
Having a historical, temporal record of each MeData item (not just singular latest value) adds another axis that can be exploited for insight generation and audience creation. It is easy to create aggregations like average / max / minimum value for a period. For instance you can target your customers with their average daily step count over the last 2 weeks.
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.