In 2018, every organization has a data strategy. But what makes a great one?
How to Decide Which Data Science Projects to Pursue
Here’s how most companies decide which data projects to pursue: Management identifies a set of projects it would like to see built and creates the ubiquitous prioritization scatterplot. One axis represents a given project’s value to the business and the other axis represents its estimated complexity or cost of development. Management allocates the company’s limited resources to the projects that they believe will cost the least and have the highest business value. This is not wrong, but it is a recipe for the mediocre data strategy. An excellent data strategy, by contrast, starts with a centralized technology investment and well-selected and coordinated defaults for the architecture of data applications. It is specific in the short term and flexible in the long term. Moreover, an excellent data strategy takes into account the fact that data science projects are not independent from one another. With each completed project, successful or not, you create a foundation to build later projects more easily and at lower cost.