Whether it’s Amazon, Google, Airbnb or Uber or the rise of the fintechs (companies that use technology to make financial services more efficient), the list of digital disruptors grows each day. Clearly, we are in an era of unstoppable online revolution, with information the currency of the day.
How has this come to pass, and so quickly? Managing and exploiting data connections is what these disruptors excel at and what other enterprises need to get better at.
It’s also why graph database technology is about to move into the mainstream - since the best way to manage the huge network of connections that power information disruption is via this supremely powerful technology.
The analysts are also of the same opinion, and believe that 2016 is a milestone year for graph database technology. Forrester says, for example, that over a quarter of enterprises will be using this form of data utility by 2017, while Gartner reports it is the fastest-growing category in all database management systems, predicting 70 per cent of leading companies will be piloting a graph database project of some serious kind by 2018.
For those at the front line of the data wars, this isn’t news. We’ve known that graphs have been making significant in-roads to business for at least a decade. Initially, the big social web giants like Google, Facebook and LinkedIn mined data connections to seed their success: Google with web documents, Facebook and LinkedIn with social connections, and PayPal with its payment network.
The good news for those working in the data trenches is that these web-scale technologies, developed by dedicated engineers, have moved in to the mass market - and are available to one and all. That’s letting a growing number of enterprises build the infrastructure for highly personalised product and service offers or super-charged search results that draw on huge volumes of data, in real-time.
Telcos are diagnosing network issues, enterprises are re-imagining their master data, identity, and access models with it, while Fortune 100 firms recognise graph databases as the best way to model, store and query data.
RDBMS has limitations
Relational database management systems (RDBMS) is, of course, the data infrastructure that has been in place for the last 20 years. They do certain jobs, such as work with discrete data, very well.
However, in the age of exponentially larger and irregular datasets, generated by our social interactions, mobile usage and website visits, we are now presented with large highly-connected datasets which are a challenge for SQL to parse. After all, we’re no longer talking about structured information any more, and graphs are perfect for those big, messy and connected data sets that global businesses have increasingly more of.
Graphs also prove their worth not simply in handling networks of relationships, but in managing very large big data size data volumes. With the arrival of big data and internet of things (IoT), we have surpassed megabytes or gigabytes and sit at the peta-level of data - and beyond even that.
Graphs will also spread because the lines between analytical and operational repositories are blurring. That means graphs can help enterprises get data at super-speed, i.e. in real-time, in a way that just wasn’t possible with older data warehouses and relational databases. This is critical when you think about the master data, identity and access models the data wars are about. What’s more, the highly personalised product and service offers we expect from our online shopping experience (think, ‘Hey, I see you bought that, you may like this’) clearly need to happen in real-time and are easily managed using graphs.
Allied to all this is the rise of data query languages like openCypher, which is attracting strong interest from major software players like Oracle and Spark, and is the standard language for graph databases to be searched, regardless of the product involved. A full native, sharded graph database from a major vendor seems to be on the horizon.
Rolling all this together, it’s plain to see that the potential benefits for endless markets beyond the ones we’ve been talking about, like healthcare (the investigation of diseases and cells), media (complex data structures) and government (security plus networks of donors to voters), where complicated relationship data sets abound and are huge. It won’t be long before RDBMS will get left far behind and the data wars will have a new winner: graph.
