How to Solve the Most Common Data Problems in Retail Access AI

In the retail business, big data is poised in the coming years to open up huge opportunities in the way stores (both physical and online) fundamentally operate and serve customers. Given the incredibly small margins, Big Data will also provide much-needed efficiency improvements – from tighter supply chain management to more targeted marketing campaigns – that can make a big difference to a retail business of any size. Making data-driven decisions is no longer about learning from the past; it means making changes to the business constantly based on real time input from all data sources across the organisation. Making predictions and applying machine learning is based on traditional data but also on new and innovative sources like connected Internet of Things (IoT) devices and sensors or, going a step further with deep learning, unstructured data from things like static images or cameras monitoring stock in warehouses. Consumers can be fickle, so being able to accurately anticipate what they will do next and quickly react is what puts the most innovative and successful retailers above the rest. Dataiku, recently explored the types of data problems facing retail, the problems they solve, and the steps that any retail organisation can take to become more data driven.