Organisations face a dilemma: the data they have gathered bears immense potential, yet leveraging it is a significant challenge. This blog post outlines an approach to solving this problem by combining graph technology with data orchestration.
In social network analysis, a conventional approach relies heavily on available metadata, allowing to match a virtual entity (social network account) to a real-world entity (person, company) in the network. However, a single person using multiple accounts for any reason obviously breaks the connection, forming multiple virtual entities in the network. Or multiple people can share their account, forming a single virtual entity in the network. If these cases are not taken into account, they can affect reliability of social network analysis significantly without any warning, possibly leading to misinformed decisions and further bad consequences.
Do you think there is no space for a graph database in your company? Or it would be a huge effort to integrate a graph database into your product? I have to tell you: You can use a graph database like Neo4j without touching your product, and you can use it for managing your company’s knowledge as well as to improve your software development process. So, even if your business problem is not inherently graphy (hard to believe in 2018), there are a few reasons why you should think about your environment as a graph.
The success of many enterprises greatly depends on their ability to gather useful information and process it in a timely manner. Automation is essential and so is presentation, giving tangible feedback, to decision makers. This is where technology reaches out to management, where science and design are combined to put the right people in the position of making better and more sustainable choices.