It’s all very well having great ideas, well-defined business outcomes, the latest cloud modern data stack services
, products and tooling. However, if you can’t describe your data, articulating the business priority, and it’s business value, it makes it challenging to capitalise on making the data available.
Include the initial challenges around getting at the data for modelling, sourcing, issues with timeliness; the result is that often we can find it challenging to transition to a new architecture.
It brings challenges in shaping the direction of a programme, managing stakeholders, delivering against timelines and expectation, and delivering within budget. Ultimately it means unhappy data scientists, analytics users, shareholders and can lead to failure of a data platform.
How do you go about tackling the challenges to unlock the value in your data and enable the creation of great data products to service data science and analytical needs? The problems aren’t new and have resolved in countless programmes.