Have we lost control of data?
The face of data within an organisation has expanded over the years and continues to evolve rapidly. Almost certainly, you’ll recognise that where data is stored within your business has changed. As well as how you use it.
It’s hard to keep track.
The accumulated volume of data, storage architecture and the different processes surrounding it have created data chaos. The current data architecture is now monolithic, centralised and has lost its way. But organisations are only ever a mindset shift away from regaining control. And there has been plenty of talk that the answer could be the hottest trend in data right now – data mesh.
Data mesh can be thought of as the sequel to big data, although the approach is still very much in its infancy. But what is data mesh? It is the idea that rather than having all your data in one place with no clear ownership, it is instead a federated model which serves data-as-a-product and is linked by a web of universal data standards that can harness collaboration across multiple data domains.
But it’s perhaps more useful to focus on the ‘why?’ and the ‘how?’ of data mesh than being distracted by the ‘what?’ and getting bogged down by a technical explanation of its architecture. Ask yourself ‘why do I want to change data management in my organisation, and how can we go about it?’.
Unless you’re working at a heavily-funded startup, it’s unlikely that you’ll be starting any data optimisation project with an architectural blank sheet, and a blank cheque to match. In reality, most companies are not in a position where they can simply pivot to a data mesh – years of complexity and legacy acquisition prevent an entire rebuild of the aeroplane mid-flight. So focusing too much on exactly what a cutting-edge data mesh architecture would look like is largely a waste of time.
Fortunately, however, the benefits of data mesh architecture can be achieved with a strategic plan and creative, collaborative thinking. This means you shouldn’t be looking at data optimisation as a predominantly technical problem to solve but also as a cultural issue.
In the same way that cloud success is not predicated on being a native adopter, the cultural model around the complex software stack is where the true value of data lies. The same level of transformation and frictionless collaboration that has been achieved in managing the modern software stack needs to happen with data.
And as with any cultural reboot, securing internal buy-in is a prerequisite. Achieving this requires the ability to provide a compelling demonstration of the value of data.
To turn the opportunities that data can provide into tangible outcomes (which you can then demonstrate internally to secure buy-in) you will need a model for establishing data maturity.
