Understanding connected data analytics basics is essential as organizations adopt graph databases. These organizations already have large amounts of data available that needs connected in context and as that connected data grows it will drive the need for analytics to leverage the connected data as a core component of their analysis. Most organizations today only utilize about 10% of their data for analysis. The key to unlocking new insights is to leverage the connectedness of the data as part of a graph analytics solution. Through graph analytics enterprises have gained competitive advantages because they are now discovering the cause, effect, and influence of certain patterns present in their organizations data.
We must start by understanding the connected data analytics basics. When it comes to exploring how graph analytics can be used in solving problems, it boils down to its ability to compare “many to many to many.” For example, it makes it possible to not only ask about “friends” of a person, but also friends of their friends as well with details include beyond the fact they’re connected. Building up on such scenarios allows you to see influencers within a network. Graph analytics can infer paths via complex relationships to determine connections that aren’t easy to find and surface these to human analysts for confirmation, validation and action.
One aspect where graph analytics is an advantage is data discovery. It allows you to see patterns within data when you have no idea what question you want to ask. This makes it possible to find a needle in a haystack. As patterns start emerging from data sets, you are able to surface a clear picture of the precise elements and patterns influencing business outcomes, so they can be addressed properly.
Through this, you can start determining the contextual impact of data on your business — how all data elements gathered from different sources and applications interrelate and impact events as well as business relationships. For businesses, being able to derive intelligence from data will heavily depend on the flexibility of the platforms employed. Graph analytics and its value will also rely on the ability to offer insights for your enterprise that weren’t discoverable in the first place.
One main advantage of graphs is the ease in which new data sources and relationships can be added. Its simple on-boarding of new data is essential when dealing with Big Data. The ability to swiftly add new sources of data or relationships in data when required to support a new line of questioning is important for discovery, and the GraphGrid Data Platform is naturally well suited to support such data ETL requirements for flowing data continuously into ONgDB (Open Native Graph Database).
These connected data analytics basics can be used on a wide range of data source inputs to unearth insights on relationships between operations, products, and customers. The connected data allows you to make use of patterns that are critical in addition to the quantified data points typically used.
When all data is connected and loaded into GraphGrid Connected Data Platform, it makes it possible to see connections via multiple hops and patterns in common compared to when it was disconnected and difficult to surface the meaningful connections. The primary aim of connected data analysis is to see useful information that can be acted on to improve business operations.