Why industry leaders are choosing entity-centric data analytics strategies
Businesses across every major industry are adopting data-driven strategies. Those in the leading edge are adopting entity-centric data analytics.
The term entity-centric has yet to enter the mainstream lexicon. Entity-centric refers to an approach to data modeling or data analysis, in which information is structured around and referenced by the properties and relationships of real-world “things” or entities. Entity-centric modeling contrasts with the more conventional methods of table-centric data modeling and application-driven data analysis, in which information is structured around and referenced by tables, columns, and rows for specific applications.
Businesses that rely on table-centric data modeling and application-centric data analysis can only obtain the full history of a customer by referencing many different applications on which the data is scattered. Businesses that reorganize their data into entity-centric models enjoy the great benefit of being able to find everything they could possibly know about something of interest. Everything they might want to know about a customer, for example, has already been linked to the customer’s record. That reorganization process is called entity resolution.
There has been a long gap in the data management industry for entity resolution for analytical use cases. How has the data management industry failed to meet the demands of next-generation data analytics?
Master Data Management (MDM) solutions were designed to fulfill the needs of business operations and data governance – not data analytics. MDM solutions have conservative matching functions and rigid user interfaces. Those might be useful to the bureaucratic curation of essential business data, but are certainly counterproductive to self-service exploration and analysis by data scientists.
Data integration solutions such as Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) were not designed to perform the combinatorial process of entity resolution like MDM. They are not appropriate for tasks that require disambiguating and linking disparate representations of the same real-world objects.
Novetta Entity Analytics meets the demands of next-generation data analytics by creating a self-service environment in which data scientists can reorganize messy and disparate sources of data into rich graphs of entities and relationships that are free of noise. Novetta Entity Analytics aids the discovery of patterns and characteristics within data, the mapping of data sources to canonical entity models, and the resolution of many messy and disparate data sources into a single, authoritative reference source. And it scales effortlessly, processing billions of records and transactions in a single day.
You can schedule a demo today to see how Novetta Entity Analytics can help you lead the transition to entity-centric data analytics.