In my last two blog posts, I’ve written about how Novetta Entity Analytics resolves entity data from multiple sources and formats, and why its speed and scalability are so important when analyzing large volumes of data. Today I’m going to discuss how analysts can achieve much better results than ever before by utilizing entity-resolved data in analytics applications.
When data from all available sources is combined and entities are resolved, individual records about a real-world entity’s transactions, actions, behaviors, etc. are aggregated and assigned to that person, organization, location, automobile, ship or any other entity type. When an application performs analytics on this entity-resolved data, the results offer much greater context than analytics on the unlinked, unresolved data most applications use today.
Analytics that present a complete view of all actions of an individual entity are difficult to deliver today as they can require many time-consuming and expensive manual processes. With entity-resolved data, complete information about each entity’s specific actions and behaviors is automatically linked so applications can perform analytics quickly and easily. Below are some examples of how applications, such as enterprise search, data warehouse and link analysis visualization, can employ entity-resolved data from Novetta Entity Analytics to provide more powerful analytics.
Retrieve More Relevant Information In Search Results
Analysts who use entity-resolved data within enterprise search applications will find more relevant information in their search results. Not only are the records richer, analysts are also able to quickly refine search criteria and generate precise results without missing critical information.
For example, without entity-resolved data, analysts in a large bank with many account holders won’t initiate general searches for information about customers with common names, such as Mike Smith, because the search will return too much information. Constraining the search criteria to look only for Mike Smiths of a specific age in a specific city would help them limit search results, but the data would still be spread across multiple sources and likely to be incomplete. With Novetta Entity Analytics, the data supplied to the enterprise search applications already includes all elements from all data about a specific Mike Smith within one entity record, so the search engine could instantly access all records with a single search.
Novetta Entity Analytics creates a multidimensional index as it resolves entities. This index is a small, thin table containing entity IDs and the record IDs associated with each entity. Each record ID is labeled with the entity ID, so any record associated with a specific entity can tie back to everything else linked to that entity. This approach allows the search application to present data in a more organized and consistent way.
Perform Complex Data Warehouse Queries Quickly
When data warehouses contain resolved and aggregated entity data from multiple sources, analysts can rapidly perform complex queries on the data. They benefit from more comprehensive answers and new perspectives about entities because complete information is analyzed instead of a subset.
Once query results are returned, they can be easily viewed and additional queries performed to filter out entity information, classify observations or identify something new about an entity. For example, complex queries could be applied to locate a large bank’s high value customers who have credit card, savings and checking accounts, but don’t have loans or investment accounts. The bank could then reach out to those customers and present them with offerings for the additional services. Complex queries could also be used to identify individuals who travel frequently, such as those who spent at least $1,000 at a hotel in city A, $500 at a hotel in city B, and $300 at a hotel in city C, and might be interested in offers for hotel or airline promotions targeted at business travelers.
Novetta Entity Analytics provides entity-resolved data to a data warehouse in a multidimensional index, which minimizes the amount of data that must be added. One Novetta Entity Analytics deployment has a data warehouse with a 100GB index it uses to link to 10TB of data stored outside of the warehouse.
Drastically Simplify Link Analysis Visualizations
Link analysis visualization applications, such as those from Palantir, Centrifuge and IBM, are great at documenting connections between groups of records, but are more challenged at providing simplified views of the data. These applications usually organize information about the same entity into many nodes that then have to be arranged manually into concise charts showing relationships between records. When link analysis tools are able to access entity-resolved data, all information about a specific entity can be aggregated within a single node to greatly simplify visualizations, minimize noise and help eliminate missed connections.
For example, companies A and B regularly conduct business with one another, and company A has five bank accounts and company B has three. Depending on the type of business being transacted, company A sends money to one of company B’s three accounts from one of its five accounts. In a link analysis visualization without entity-resolved data, there are many nodes and links created to represent the transactions between these two companies. With entity-resolved data from Novetta Entity Analytics, there is one node for company A, one node for B and a single link between the nodes containing all of the information about the aggregate transactions between the two companies.
Novetta Entity Analytics publishes the entity-resolved data as a web service that link analysis visualization applications access. All records associated with a specific entity can be quickly aggregated around single nodes and concise charts showing relationships between entities can be automatically viewed.
The entity-resolved data Novetta Entity Analytics delivers enables many analytics applications to provide clearer pictures of the data they are analyzing. As a result, analysts save time, immediately gain new insights and are able to better determine how best to hone their analysis.