The most common interactions we have with recommendations today involve social, the people we may know, and retail, the products we may also like, but some of the more interesting recommendation engines are the ones that operate internal to an organization providing business
increased efficiencies for how subject matter experts on the business domain should prioritize their daily efforts as well as helping the organization transition to being a more data driven enterprise with these insights guiding internal business use cases that go deep in offering a business-based direction on a holistic data view.
Business Recommendation Engines Guide Engagement
Whether it involves leveraging direct or indirect customer feedback through social media platforms, business supply chain details from the manufacturing plant to the logistics network, or inferring relationships according to an activity utilizing the network to determine the confidence in that assertion, the Neo4j graph database offers the significant advantages when it comes to making an enterprise data driven through business recommendation engines.
A known strategy for business recommendations internal to business is the design of pattern-based recommendation algorithms. Such recommendations are dynamic in nature as data flows through the system and aid business analysts that need help to prioritize their time to filter data by reviewing them in order of those that rank highly enough to be focused on first.
For instance, if you’re intent of finding insurance, medical, or financial fraud, there are a number of understood patterns associated with fraud within the data, which can be used to proactively pause transactions and rank them by priority for closer examination. Such detection is effective for
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