Now: Machine/AI augmented analysis
A modern data ecosystem looks to combine the power of humans with that of advanced technologies such as artificial intelligence and machine learning. Your ecosystem should constantly monitor and analyse how the data within it is changing, moving and being used in order to inform decision making about how the ecosystem itself should evolve. This doesn’t mean automating everything, rather making the best use of people, alongside advanced technology, to deliver on the most important outcomes for the organisation.
Then: Multiple, disparate data platforms
Data platforms were introduced to fulfil specific tasks, rather than integrated into the ‘bigger picture’ of the organisation’s data landscape. This often resulted in labour-intensive process to integrate them or, at worst, solutions remaining un-integrated.
Now: Data ecosystems
A modern data ecosystem brings disparate sources, platforms, people and technologies together, making data from multiple sources available to a wide range of evolving applications. It seeks interoperability between data from across the organisation (and even between organisations) leading to greater agility and reducing the time to value for new data outcomes.
Then: Expert configuration
To extract commercial value from data – from analysis and reporting to automations – any part of the organisation would need expert skills to fulfil the project. This reduced the agility and speed with which the organisation could react with data.
Now: Self-service
One of the greatest benefits of a modern data ecosystem, self-service enables your organisation to become truly self-sufficient with data. Teams are empowered to use data to deliver on their objectives, not view it as an abstract IT issue.
Then: On premise solutions
Physical, on-premise storage and platforms made data systems slow to scale and difficult to change. As organisations began to sporadically move aspects of their architecture to the cloud, they created fragmented, poorly architected and ungoverned solutions, with a mix of legacy on-premise and new cloud capabilities creating further complications.
Now: Cloud ecosystems
A modern data ecosystem is built on cloud capabilities, not just replicating their on-premise capabilities in the cloud, but evolving them to take advantage of the scalability that cloud brings. This makes it easier to add, remove and change components – much like renovating a house. But cloud only increases agility and speed if it is part of a well-organised and well-integrated ecosystem.
How can you apply these factors to your own ecosystem?
When assessing your existing data landscape against the flexible, scalable ecosystem you want to create, you need to consider whether you have existing technologies that can fulfil the capabilities that your organisation needs. Look at how the business is organised – where does ownership of data sit, are there bottlenecks that will limit scalability? Consider also whether modern approaches like Data Fabric or Data Mesh will work for your organisation and, if so, invest in the foundational capabilities that will being these to life. For example, metadata management is a fundamental capability in delivering lasting value from a fabric or mesh architecture.
Once you have evaluated your data landscape, organisational structure, and assessed the technology you have, you can start to identify the gaps you need to fill or the areas you need to ‘upgrade’ to create a modern data ecosystem that adapts and scales with your business’ needs.
To learn how we’re helping our clients to approach building a modern data ecosystem, download our guide: Implementing a Modern Data Ecosystem
Download Guide: Implementing a Modern Data Ecosystem