Gartner and DataChemist Agree: Graph Databases Are Set For Growth


It’s always nice when one of the world’s leading tech analysts backs up your view of the world.

That’s what happened for DataChemist recently when Gartner identified the top 10 data and analytics technology trends for 2019, ahead of the Gartner Data and Analytics Summit in Sydney, Australia.

We count at least 6 of these trends that are directly relevant to DataChemist - confirmation that we aren’t the only ones who see the world the way we do.

Graph Databases

From our perspective, Gartner’s prediction that the graph database market will double in size every year to 2022 is probably the headline news. With a conservative estimate of the market in 2018 at $500m, that means a market size of $8 billion by 2022.

Whilst that may seem like a huge number, let’s remember that Oracle alone is a $40bn revenue company. Data is big business, and graph is “the coming man” of that world. Gartner themselves comment “graph analytics will grow in the next few years due to the need to ask complex questions across complex data, which is not always practical or even possible at scale using SQL queries”.

That’s pretty much the very same hymn sheet we’ve been singing off.

At DataChemist we believe that data has to be organised, controlled and structured in a way that reflects the real world, and in particular the real world of complex connections, relationships and dependencies.

Other Emerging Trends In Data

But there’s more. A lot more. Consider Trend 4, “Explainable AI” and Trend 7 “Conversational Analytics”. Ultimately these both relate to the need to humanise data and AI. If the end goal of data is insight (something that is too often forgotten), then as an industry we need to be always aware of how best to deliver that insight to decision makers - whether human or otherwise.

At DataChemist we believe that data has to be organised, controlled and structured in a way that reflects the real world, and in particular the real world of complex connections, relationships and dependencies.

To do that our customers create models that mirror those real-life relationships. That in turn makes data more understandable, and makes it possible to verify that the rules relating to those relationships are not broken, and cannot be broken. That’s a huge step forward from the current ‘black box’ approach to big data that has a tendency to spit out ‘answers’ without understanding.

Additionally, look at Trend 6, “Data Fabric”. As described by Gartner, “data fabric enables frictionless access and sharing of data in a distributed data environment”, and we are again right in the specific areas and challenges addressed by the DataChemist approach.

Precisely by structuring and controlling data in a meaningful way, we enable new services and interfaces to be built on this data quickly and easily.

Most of us will be familiar with the common situation in which existing data is so inconsistent, and has been ‘tweaked’ to support existing requirements so much, that building anything on it is almost impossible.

DataChemist resolves that issue. The DataChemist platform automatically standardises data, and most importantly puts a structure on that data that enables services from across the entire organisation (and beyond) to access and use it.

We believe our approach is truly transformative. It’s nice to see others agree!

Written by

Kevin Feeney


Posted

March 4th, 2019

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