- 06 Mar 2022
- 1 Minute to read
- Updated on 06 Mar 2022
- 1 Minute to read
A single data point Explorium is collecting from an external data source. Examples for attributes: phone number, revenue, number of employees.
External source of data Explorium is collecting data from, processing and serving to the user. Examples for data sources: FullContact, CreditSafe, Similarweb.
The processed data Explorium is serving to the client. Examples for enrichments: Company insights, Companies financial report, Website traffic trends over 3 years
The data is organized in the context of the real-world entities they represent.
for example: ORGANIZATION is an entity we can ask multiple questions about, like what is the revenue of the organization? where its HQ are located? How many people are working in this organization?
The answers are represented in the data, all connected to the entity it's describing.
The percentage of values that are not None or not missing for a specific attribute.
A calculated attribute, based on one or more other attributes, raw or calculated. examples for signals: fraud score, unified revenue range.
Signals are interesting features that you extract out of data to help you make a decision. For example, maybe you are trying to predict stocks, or trying to predict the price of a house. The goal is that this is the signal that is going to drive the actual decision, a key indicator.
Pipeline is a process taking few enrichments, making all kind of manipulations and outputs a signal, or a unified attribute.
Dynamic enrichment vs Static enrichment
Dynamic enrichments, are enrichments fetching an external source of data once getting an input from the client. For example: once passing an email address, we send it to one of our data providers, getting a result, parsing and processing it - and returning it to the project.
Static enrichments are enrichments calling our internal DBs to get results - no need to get out of our systems.
The percentage of queries that returned any information (not null) out of the total number of queries sent to the service.
Data Classifications (Ontology)
|The types of input value(s) required to run an enrichment. Examples of data classifications (ontologies): URL, company name, email address. Click here for more information about data classifications (Ontology).|
Estimated Coverage (per signal)
|The percentage of queries that returned any information (not null) out of the total number of queries sent to the service.|
Estimated Max Coverage (per dataset)
The maximum estimated coverage that was calculated from the list of signals in the dataset.