Like any project in an organization, a data science project needs to have boundaries regarding how many resources are allocated to it. This resource usage translates into a monetary cost that takes the form of a budget. So, even if many professionals in this field are not aware of it, budgeting plays an essential role in every data science project and provides the framework through which it can manifest.
Despite its similarities to other projects, data science projects differ in many ways. First of all, its return isn't clear or even guaranteed. A data science team may investigate a dataset for insights or predictive potential, but it may not dig up something worthwhile. The data in a company's databases may be useful for its day-to-day tasks but useless for anything data science-related. It's next to impossible to know if there is anything worthwhile beforehand, so when starting a project, you have to take significant risks. As for the project's time frame, that's also highly uncertain, especially if it's a new project. This uncertainty can veer the project off-course, and the risk of going over-budget is substantial.
When creating a budget for a data science project, several factors are considered to mitigate the risk of failure. First of all, you need to have a clear plan of what you expect from the data science team to find. If possible, you could have some ideas as to how you could translate these findings into a value-add, be it through a revenue stream, some improvement in the customer/user experience, or some enhancement in the organization's workflow efficiency.
What’s more, it's good to examine a data science project from various perspectives and ensure that the data scientists involved have peace of mind when working on it. It's not just up to them to make it work, since the other stakeholders have a responsibility in it too. For example, the data owners need to do their part and ensure that the data science teams receive all the data it needs promptly. The developers involved need to have sufficient bandwidth to help with any ETL and other project processes. Finally, the business people need to have realistic expectations of what the data science team can deliver and how to leverage their work.
Beyond the above factors that you need to consider, some additional considerations are useful to have when creating a budget. For instance, the cost of cloud computing involved is something that can get out of hand quickly, especially if you are not used to working at a particular scale of data. Sometimes it's more effective to have dedicated servers available to you instead of a leasing computing power in a virtual machine. Also, it would make sense to start with a proof-of-concept project to gain a better understanding of the problem at hand before going at it with full force.
You can learn more about this, and the other less technical aspects of data science work through one of the books I co-authored relatively recently. Namely, the Data Scientist Bedside Manner book delves into this topic and explores data science from a perspective few people consider. Using information from various sources, including some experienced professionals in the field, provides guidance both for the data-driven manager and the data science professional, bridging the gap between the two. Check it out when you have the chance. Cheers!
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.