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Developing a Robust Data Strategy

As the cloud opens up new possibilities for incorporating machine learning and artificial intelligence into your offerings, it is rapidly becoming essential to develop a robust data strategy. Deep learning and new breakthroughs in AI are extremely powerful, but they require large data sets in order to fully leverage them.

So what is a robust data strategy? A data strategy requires a plan for sourcing and storing data, ideas for putting data to work, and perhaps most importantly, a process for evaluating which ideas are and are not effective. Companies that successfully implement a robust strategy, and that continually evaluate and evolve their data use, can reap the rewards. Let’s take a look at what that means in practice.

Build A Data Infrastructure

In the world of machine learning and AI, you’re only as good as your data, so gathering as much high-quality data as possible is extremely important. Data is essential to training new models, evaluating them once deployed, and contextualising them within your larger business goals. In a robust data strategy, you’ll need to collect as much data as possible for as long as possible.