The data science project backlog

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Are you wondering what a data science project backlog is? Or maybe you are more interested in learning about what data points you should include in a data science project backlog? Well either way, you are in the right place!

In this article, we tell you everything you need to know about the data science project backlog. First we discuss what a data science project backlog is and why it is important to maintain a data science project backlog. After that, we discuss who should maintain and contribute to a data science project backlog. Finally, we provide tips on how to structure a data science project backlog and what information should be included in a data science project backlog.

What is a data science project backlog?

What is a data science project backlog? A data science project backlog is a document where you keep track of ideas for data science projects that have been proposed by your team or your stakeholders. This document should contain a list of proposed projects along with key data points that can help your team and your stakeholders understand the priority of each project.

What is the purpose of a data science backlog?

What is the purpose of a data science project backlog? The main purpose of a data science project backlog is to serve as an organized list of project ideas that the data science team can refer to when they have capacity to work on new projects.

Having all of the data science project ideas organized into one document helps to ensure that no important project ideas are forgotten when it comes time to decide what to work on next. If your backlog document includes a few data points that help to inform the priority of each project, the document will also make it easier to compare different projects and determine which project has the highest priority.

What to include in a data science project backlog

What sections to include in a data science project backlog

The first thing to think about when determining how to structure a data science project backlog is what sections you should include in the backlog. Different sections can be used to keep track of different types of projects that are in your backlog. For example, one option would be to include different sections for projects that serve different sets of stakeholders. Another would be to include different sections for projects that require different skill sets, such as projects that require data engineering skills and projects that require machine learning skills.

For best results, we recommend creating different sections in your backlog for projects that serve different purposes. At minimum, we recommend including the following three sections in your backlog.

  • Tech debt and foundational work. The first section that you should include in your project backlog is a section for technical debt. This includes projects that address technical debt that has been accumulated by the team and projects that aim to make improvements to the foundational data assets that the team uses.
  • Projects with immediate value. The next section you should include in your project backlog is near term products. This section should contain stakeholder-facing products that could provide value to the business stakeholders your team partners with in the near term. This section should specifically contain projects that could provide value to the company in its current state. It should not include projects that may be valuable in the future, but are not as valuable right now.
  • Projects with future value. The final section we recommend including in your data science project backlog is a section of long term projects, or projects your team might work on in the future. This section should contain projects that will be more valuable to the company in the upcoming years, even if it is not the right time to work on them. For example, this section might contain projects that will be more valuable after the company scales or after an upcoming change is made to the way the company operates.

What data points should you include for individual projects?

After you have decided what sections you want to include in your data science project backlog, the next step that you should take is to determine what data points you will collect for each project. Here are some examples of data points we recommend including for each project.

  • Name. First, you should include the name of the project at hand. This should be a short, descriptive name that includes no more than a few words. The main purpose of this data point is to make it easier to skim through the document and find the project you are looking for.
  • Description (optional). Next, you should include a brief description of the project that is no more than a sentence or two long. This provides an area to give a little more context on projects that can not be sufficiently described by short project titles. If the project title is sufficiently descriptive, there is no need to include a full project description.
  • Problem solved. You should also include a brief description of the business problem that is solved by this project. This helps to ensure that the only projects that are getting put in the project backlog are projects that solve real business problems.
  • Business metrics (optional). It is also useful to list the business metrics that will be moved by this project. This helps to contextualize which projects will impact the most important business metrics and which projects will impact less important metrics.
  • Rough timeline estimates (optional). It is sometimes useful to include a rough estimate of the time that it will take to get to a minimum viable solution for each project. These estimates can be very rough. It is useful to understand whether a project will take a few days, a few weeks, or a few months to complete when making decisions about what projects to commit to.
  • Dependencies on other projects. It is important to note any dependencies that a project has on other work that is going to be completed by your team or another team. If a project is blocked by dependencies on another project that will not be completed for months, this is important information to know when it comes time to prioritize projects.
  • Stakeholders. It is also useful to create a list of stakeholders who would benefit from a given project. This makes it easier to understand which projects would only serve one stakeholder group and which projects benefit multiple stakeholder groups across the company.

How to populate a data science project backlog

Are you wondering how to come up with project ideas that can be used to populate a data science project backlog? Here are just a few examples of places where you can come up with project ideas to populate a data science project backlog.

  • Incoming requests from stakeholders. One potential source of project ideas that can be used to populate a data science project backlog is incoming project requests from stakeholders. You do not have to include every incoming request in the project backlog, but it is a good idea to include strong project ideas that you believe will provide value to the business.
  • Stakeholder interviews. Another way to generate ideas for potential data science projects is to interview your stakeholders to better understand their needs. We recommend asking them questions about what the most difficult or time consuming part of their job is. This can help you identify opportunities for automation or data-driven guidance. We also recommend asking questions related to data points they wish they knew about their customers or the operational processes they oversee. This will help identify opportunities where better data collection, reporting, or predictive models could be of assistance.

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