Whether it’s Amazon, Google, Airbnb or Uber, the list of digital upstarts who’ve totally disrupted their market grows every day. Clearly, we are in an era of unstoppable online revolution, and information is the factor shaping the market.
It’s also why graph technology is about to go mainstream, since the only convincing way to manage the huge network of connections that power information disruption is by using this proven data technology.
The analysts agree, confirming 2016 will be adecisive year for graphs. Forrester says, for example, that over a quarter of enterprises will be using this form of data utility by 2017, while Gartner reports they are the fastest-growing category in database management systems, predicting 70% of leading companies will be piloting a graph database project of some significant kind by 2018.
For those of us in the field, we know graphs are long overdue mass attention, but we also know CIOs have been quietly taking advantage of them for some time.
For example, early graphs came to fruition from in-house work by the big social web giants like Google, which exploited the connections in Web documents to rank search results as part of its proprietary algorithm. Move ahead on the timeline, and these custom-built, pioneered technologies are now available to the wider marketplace.
That’s letting a growing number of enterprises use graphs to build the infrastructure to power highly personalized product and service offers – or indeed, super-charged search results that draw on huge volumes of data and in real-time.
There are telecom providers diagnosing network issues, or enterprises re-imagining their master data, identity, and access models with it, to Fortune 100 firms that are seeing graph databases as a top way to model, store and query data.
What’s wrong with SQL?
But haven’t we had data software for decades that does the job perfectly well? Yes, RDBMS are great, and have served us well for a long time.
The problem is that the kind of high volume, highly-interconnected datasets the Internet generates are hard for SQL to parse as well, or as fast, as we would like. And it isn’t just the Internet generating more data either; as consumers and digital natives, we are generating more data and spawning more connections at an exponential rate.
Graphs work so well because they are adept at working with not just single points of information, but these evolving webs of relationships.
The relationship aspect of graphs is probably best demonstrated by an important graph database use case: fraud detection. Today’s sophisticated scams and fraud rings are notoriously hard to spot early, especially via traditional approaches.
Analyst group Gartner’s proposed solution to the growing problem of online fraud, “Entity Link Analysis,” for instance, works by leveraging connected data in order to detect organized fraud – which is another way of saying, look at the relationships – which isby definition a form of graph analysis.
Native graph database from a major DBMS player
Many other business problems in today’s connected world also prove the worth of graph analytics. After all, with the arrival of Big Data and IoT, we are no longer talking in Megabytes or Gigabytes; and we’re not talking about structured information any more either – making graphs perfect for those large, disordered and connected datasets global businesses have more and more of.
Remember, as graphs are very adept at showing recommendations or connections between people, places, or things, they’re a natural fit for many industries. Even better, as understanding the connections between data and the meaning of these links doesn’t need new data, you can pull new insights from existing information resources, simply by reframing the problem and looking at it in a graph.
Graphs will also prove invaluable because the lines between analytical and operational repositories are blurring. That means graphs can help enterprises get data at super-speed in a way that just wasn’t possible with older data warehouses and relational databases.
Allied to this is the rise of data query languages like Cypher, which has attracted strong interest from major software players like Oracle and Spark, and may become the standard language that allows graph databases to be searched, regardless of the product involved.
I predict a fully native, sharded graph database from a major vendor no later than 2017.
Finally, healthcare, media, and government are likely to be the biggest users of graphs in 2016. Networks are inherent to healthcare in general, after all, with relationships between doctor and patient, multiple patients involving large and complicated relationship datasets and the investigation of diseases and cells are also particularly applicable for graph databases.
The media, meanwhile, has a complex data structure in which no single individual or asset exists in isolation – a level of interconnectedness that fits perfectly with graphs. As for government, the security community is a prime growth sector for graphical analytics, plus politics involves lots of networks, from donors to voters, that graphs are also going to be great at mapping and leveraging.
2016 is shaping up to be the most significant in the entire NoSQL database story so far. It’s time to think beyond relational – it’s time to think graph.
(About the author: Emil Eifrem is co-founder and CEO of Neo Technology, which develops the leading graph database, Neo4j)
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