If you’re looking to modernise your data ecosystem, it’s important to stop and take stock of the features and capabilities you have in your existing architecture, and whether they can be integrated in a flexible, forward-thinking ecosystem.
Here, we look at some of the core differences between a traditional and modern data ecosystem: a ‘then vs now’ to help you review your software and systems. Remember, every ecosystem will be different, but you can find some of the core features you’re likely to need in our guide to Implementing a Modern Data Ecosystem.
Then: Central control
Data management, analytics and even data science practices were often controlled by a central data and IT team. This pooled data skills in a central resource, making it difficult for every department to have autonomy and ownership of their data outcomes.
Now: Federated ownership
A modern data ecosystem democratises data, making it widely accessible and available to different departments and functions. As a result, each business domain is in control of its own data and analytics outputs, achieving greater agility and scalability.
Then: Data as a corporate asset
Data was seen as a, sometimes intangible, asset belonging to the organisation; a valuable asset, but with complex or undefined ownership, often resulting in low quality, poorly managed data. While central ownership works in theory, and can be the right choice for certain organisations or categories of data, it can also result in complexity. In reality, bloated data models that are bent out of shape to cater for everyone’s needs, end up satisfying very few, with data teams acting as go-betweens trying to negotiate solutions to incompatible requirements from multiple parties.