Episode 6 - Avoiding the pitfalls of data projects part 2

In this episode, Collin and I continue to explore a digital product development project. Our project is heavily dependent on data and is to be delivered by an outside consulting firm. Our consulting firm says that they are are using Agile practices and principles to deliver the project, but as we learned in part 1, this team is in fact using a much more sequential “black-box” approach to  delivery.

For Collin and I, this is a realistic scenario. It is something that we have seen multiple times in our careers. It is also a high risk project, not simply because of the heavy dependence on data, but also because of the proposed ways of working combined with the organisational and power dynamics of the situation. We feel that such dynamics are present in all real-world digital product development situations, but often not taken into consideration when talking about ways of working.

Our goal in this exploration is to give listeners tangible actions that they can use when faced with a similar situation.

In this episode, we explore further Collin’s idea of latent assumptions, which he introduced in episode 1. These assumptions are baked into projects, unrecognised as assumptions. Collin and I feel that this is particularly true for projects with a heavy reliance on data.

Dependent upon the ways of working, teams may not learn that their assumptions were incorrect until very late in the project, often when the budget is nearly exhausted. 

We delve into the mechanisms that Collin is using to assess the risks within such a project and how to bring these risks into the light early to prove or disprove them? 

We spend a lot of time in this session exploring how do you get people with the right level of authority to recognise the risks of such a project, particularly when many of those people aren’t equipped to see such risks?

We hope you enjoy it. As always please let us know what you think. And, if you liked the session, we would be very appreciative for a positive rating.