Nearly six years ago I started reading about this thing called Graph Data. Now unlike almost everyone in the modern world of Graph, I am not a coder. I was once upon a time but it has been many years since I typed a line of code. No I do not have a GitHub login. As an enterprise architect I often joke that I do powerpoint for a living. But I could see how this re-envisioning of data and how it could be used would transform enterprises. It is not just a technical change. It changes everything. People. Organisation. Knowledge. Processes. Products. Services. And yes, technology too.
I rapidly realised that this new way of working with data solves in a real way a major problem faced by every enterprise - how to bring together all the data and interpret it in many different ways. What we now know as a knowledge graph is exactly this. Once I realised this was possible, it did not take much to extrapolate that it could do the same for any data or knowledge domain. This was going to be huge.
Over the successive years I have had opportunity to promote Graph Data technologies in a range of different environments within large enterprises. Now as we know, Graph Data is not a best choice for all computing problems - but what I did learn is that it is a strong even best candidate for much more than where it was being used. The biggest challenge at the enterprise level was not technical capability - but a combination of technical vision and genuinely understanding the underlying problem space. So this is where I chose to focus. Developing data strategies and identifying the problems best solved with Graph Data as well as articulating the real business benefits of doing so.
Graph Data is now undeniably mainstream. If your organisation does not have a graph database right now, it will very soon, one way or another. Every major database player has a graph database. Enterprise tools are embedding graph databases within them - from Network Management to CRM systems, ERP systems, next generation “smart” Data Lakes, even Machine Learning with TensorFlow has a Graph Database built into it. Every enterprise needs new insight and ability to connect within and across its data. The company that started the commercialisation with an oddly named Neo4j is now one of (the?) hottest database companies on the planet.
It has been an exciting, eventful and at times quite challenging journey. But every organisation has at its core a most valuable unsolved business problem, and I am yet to find that this problem is not actually a Graph Data problem.
Having had the challenge of showcasing how Graph Data demonstrably delivered benefits elsewhere from what I knew to be theoretically possible, I had to become a bit of an expert in finding other companies that had done interesting things with this technology. Time and time again, I found one company consistently at the front and (somewhat annoyingly) possibly even a little ahead of my own thinking. I still vividly remember nearly two years ago explaining to long suffering friends that “I really need to one day work at a company like GraphAware”.
Well, that day finally is today. I am fortunate enough to contribute to Graph Data’s future by doing two things. The first is expanding GraphAware to have a full-time presence in the APAC region, based initially in Australia. The second is providing the first advisory based consulting for Graph Data - enterprise architecture and data strategy. You know. The powerpoint stuff. Which is extremely important. Enterprises need from business and technology vision all the way through to strategic planning, execution and governance - end to end insight into not just how but also why this technology transforms their enterprise, what is now possible and what more they can achieve.