Programmes and projects in an age of data applications
According to Scott Crawford, head of data science enablement at 84.51°, the technology business at retail giant Kroger, the future for many companies will be determined by their ability to collaborate, using diverse teams to help them to look for a needle in a haystack of data. Crawford believes that as data science expands in an organisation, that diversity of capabilities becomes critical.
IT leaders will also need to assess when a request from the business is a defined project with design, build, test and deploy phases – or when it is more of a programme of continuous development and improvement.
As Crawford points out, there are plenty of use cases that solve a particular problem which means that the data team can crunch the data, create the data model and move on. But there are also cases which build evolution into a data science programme to provide better estimates, using the most recent set of data. One example in retailing may be when the data team is asked to build a better model for forecasting instore product demand. While it may indeed be possible for the team to develop a superior data model, Crawford said: “Very little thought goes into what happens if the new model wins.” Potentially, the business may change they way it does something. This change in business could invalidate the data model, or at the very least, mean that it requires further refinement, leading to further adaption of the business process ad infinitum.