Enriching supply chain data
Examples described herein enrich supply chain data. According to an aspect, a computer-implemented method includes receiving, by a processing device, supply chain data and identifying, by the processing device, a party associated, via a supply chain, with an entity based at least in part on the supply chain data. The method also includes collecting, by the processing device, additional data about the party and analyzing, by the processing device, the additional data about the party to determine a potential disruption to the entity. The method further includes implementing, by the processing device, a corrective action to mitigate the potential disruption.
1 . A computer-implemented method for enriching supply chain data, the method comprising:
receiving, by a processing device, supply chain data from a plurality of data sources distributed across a network;
identifying, by the processing device, a party associated, via a supply chain, with an entity based at least in part on the supply chain data, wherein the identifying is performed using a unique identifier of a plurality of unique identifiers for the entity and the unique identifier is selected using a priority-based selection that is determined to have a highest degree of confidence of being unique in a compressed storage format, wherein the unique identifier is a hash and is determined as part of a multi-phase process comprising a plurality of extract, transform, and load (ETL) phases, and wherein separate processing of entities having a source system identifier and missing the source system identifier is performed in one of the ETL phases prior to processing the unique identifier in a subsequent one of the ETL phases;
creating, by the processing device, a data structure with the plurality of unique identifiers to link multiple source data sets from the data sources as normalized dynamic data streams;
flowing, by the processing device, updates from the source data sets based on data feeds of the data sources to the data structure through inheritance properties such that the updates are available at a predetermined interval;
collecting, by the processing device, additional data about the party;
analyzing, by the processing device, the additional data about the party to determine a potential disruption to the entity; and
implementing, by the processing device, a corrective action to mitigate the potential disruption.
2 . The computer-implemented method of claim 1 , wherein the additional data comprises commerce data relating to the party.
3 . The computer-implemented method of claim 2 , wherein the commerce data identifies one or more of: a port, a shipping container, a transport from the port to another port, contents of the shipping container, harmonized system code data, and company information about a shipper and a consignee.
4 . The computer-implemented method of claim 1 , wherein the additional data comprises cyber data relating to the party.
5 . The computer-implemented method of claim 4 , wherein the cyber data identifies software usage of the party.
6 . The computer-implemented method of claim 1 , wherein the hash is based at least in part on information about the entity.
7 . The computer-implemented method of claim 1 , wherein the hash is based at least in part on a uniform resource locator associated with the entity.
8 . The computer-implemented method of claim 1 , wherein the supply chain data is received from a plurality of sources.
9 . The computer-implemented method of claim 1 , wherein the corrective action is selected from a group consisting of selecting an alternative party and adjusting an operation of the entity.
10 . The computer-implemented method of claim 1 , wherein collecting the additional data about the party comprises crawling and scraping publicly available information associated with the party.
11 . A system comprising:
a memory comprising computer readable instructions; and
a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing device to perform operations comprising:
receiving supply chain data from a plurality of data sources distributed across a network;
identifying a party associated, via a supply chain, with an entity based at least in part on the supply chain data, wherein the identifying is performed using a unique identifier of a plurality of unique identifiers for the entity and the unique identifier is selected using a priority-based selection that is determined to have a highest degree of confidence of being unique in a compressed storage format, wherein the unique identifier is a hash and is determined as part of a multi-phase process comprising a plurality of extract, transform, and load (ETL) phases, and wherein separate processing of entities having a source system identifier and missing the source system identifier is performed in one of the ETL phases prior to processing the unique identifier in a subsequent one of the ETL phases;
creating a data structure with the plurality of unique identifiers to link multiple source data sets from the data sources as normalized dynamic data streams;
flowing updates from the source data sets based on data feeds of the data sources to the data structure through inheritance properties such that the updates are available at a predetermined interval;
collecting additional data about the party;
analyzing the additional data about the party to determine a potential disruption to the entity; and
implementing a corrective action to mitigate the potential disruption.
12 . The system of claim 11 , wherein the additional data comprises commerce data relating to the party.
13 . The system of claim 12 , wherein the commerce data identifies one or more of: a port, a shipping container, a transport from the port to another port, contents of the shipping container, harmonized system code data, and company information about a shipper and a consignee.
14 . The system of claim 11 , wherein the additional data comprises cyber data relating to the party and the cyber data identifies software usage of the party.
15 . The system of claim 11 , wherein the hash is based at least in part on information about the entity and/or the hash is based at least in part on a uniform resource locator associated with the entity.
16 . The system of claim 11 , wherein the supply chain data is received from a plurality of sources, and the corrective action is selected from a group consisting of selecting an alternative party and adjusting an operation of the entity.
17 . The system of claim 11 , wherein collecting the additional data about the party comprises crawling and scraping publicly available information associated with the party.
18 . A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising:
receiving supply chain data from a plurality of data sources distributed across a network;
identifying a party associated, via a supply chain, with an entity based at least in part on the supply chain data, wherein the identifying is performed using a unique identifier of a plurality of unique identifiers for the entity and the unique identifier is selected using a priority-based selection that is determined to have a highest degree of confidence of being unique in a compressed storage format, wherein the unique identifier is a hash and is determined as part of a multi-phase process comprising a plurality of extract, transform, and load (ETL) phases, and wherein separate processing of entities having a source system identifier and missing the source system identifier is performed in one of the ETL phases prior to processing the unique identifier in a subsequent one of the ETL phases;
creating a data structure with the plurality of unique identifiers to link multiple source data sets from the data sources as normalized dynamic data streams;
flowing updates from the source data sets based on data feeds of the data sources to the data structure through inheritance properties such that the updates are available at a predetermined interval;
collecting additional data about the party;
analyzing the additional data about the party to determine a potential disruption to the entity; and
implementing a corrective action to mitigate the potential disruption.