Graphs as a New Way of Thinking by Emil Eifrem.
From the post:
Faced with the need to generate ever-greater insight and end-user value, some of the world’s most innovative companies — Google, Facebook, Twitter, Adobe and American Express among them — have turned to graph technologies to tackle the complexity at the heart of their data.
To understand how graphs address data complexity, we need first to understand the nature of the complexity itself. In practical terms, data gets more complex as it gets bigger, more semi-structured, and more densely connected.
We all know about big data. The volume of net new data being created each year is growing exponentially — a trend that is set to continue for the foreseeable future. But increased volume isn’t the only force we have to contend with today: On top of this staggering growth in the volume of data, we are also seeing an increase in both the amount of semi-structure and the degree of connectedness present in that data.
He later concludes with:
Graphs are a new way of thinking for explicitly modeling the factors that make today’s big data so complex: Semi-structure and connectedness. As more and more organizations recognize the value of modeling data with a graph, they are turning to the use of graph databases to extend this powerful modeling capability to the storage and querying of complex, densely connected structures. The result is the opening up of new opportunities for generating critical insight and end-user value, which can make all the difference in keeping up with today’s competitive business environment.
I know it is popular rhetoric to say that X technology is a “new way of thinking.” Fashionable perhaps but also false.
People have always written about “connections” between people, institutions, events, etc. If you don’t believe me, find an online version of Plutarch.
Where I do think Emil has a good point is when he says: “Graphs are…for explicitly modeling the factors…,” which is no mean feat.
The key to disentangling big data isn’t “new thinking” or navel gazing about its complexity.
One key step is making connections between data (big or otherwise), explicit. Unless it is explicit, we can’t know for sure if we are talking about the same connection or not.
Another key step is identifying the data we are talking about (in topic maps terms, the subject of conversation) and how we identify it.
It isn’t rocket science nor does it require a spiritual or intellectual re-birth.
It does require some effort to make explicit what we usually elide over in conversation or writing.
For example, earlier in this post I used the term “Emil” and you instantly knew who I meant. A mechanical servant reading the same post might not be so lucky. Nor would it supply the connection to Neo4j.
A low effort barrier to making those explicit would go a long way to managing big data, with no “new way of thinking” being required.
I first saw this at Thinking Differently with Graph Databases by Angela Guess.