The Data Science Maturity Model

Over the past year we've refined this simple model to help map, evaluate and improve our clients' data science capabilities, it might work for you too. At Applied AI, most of our client projects lean very technical - we're a very technical team with experience in insurance, machine learning, quant finance, software development and more - and data science is still a fairly new field with high demands on mathematical and engineering capability. That said, we always encourage our clients to undertake projects as part of a larger, more holistic approach to improving their data science maturity: using a statistical approach when deciding strategy and embedding'data products' within their day-to-day operations. So we cooked up the following Data Science Maturity Model. It's purposefully very simple - a familiar 2x2 matrix - and describes a clear path for organisations to improve their capability in terms of the Analytical Complexity and Operational Implementation of new data sources, statistical modelling, products, processes and teams.