Data
B2B trade show that venture capitalists are attending in search of startups to invest in . Some of those startups will be exhibiting , but others may simply be present as attendees to check out the competition . With better data , you could identify those startups ahead of time and provide insight to the VCs about who might be an investment target .
Network graphs : a new way to structure data Mark tells me a trick to making better use of data – ‘ network graphs ’. These are interconnected networks of data , very different to the lists most of us are familiar with . There are two basic units in a network graph : nodes and edges . A node is a thing – a company , person or product . An edge is a connection between nodes – person A engaged with content B , for example .
These graphs are perfect for the messy stacks of data that most organisers are sitting on . Normally , the most laborious process in data analytics is ‘ cleaning ’ – sorting data into a consistent format . “ With the best will in the world , most [ organiser data ] will never be transformed into something structured and usable ,” Mark explains . Instead , organisers can arrange their data into network graphs and learn quickly who is engaging with who and with what . Communities can be divided into subcommunities , or ‘ clusters ’, and these clusters can be interlinked according to behaviour .
Some of the things you can achieve with network graph data :
• Cluster nodes based on similarity so people , content and companies that are similar are ‘ near ’ each other in graph ‘ space ’.
• Understand the connections between clusters – which clusters are interested in each other ( e . g . the venture capitalist + startup example ) and what content each cluster will be
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