It can often be challenging to start planning your data science project. This handy 4-step guide breaks things down for you to make it easier to understand and to give you the best chance of creating a successful plan. But first...  

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What Is a Data Science Project Plan?

A data science project plan is a document that serves as a guide for your Data Scientists and the rest of the team involved. It should detail the challenge, expected outcomes, timeline, resources required and any risks that may arise.  

Essentially, these plans are created at the start of the project to act as a roadmap or blueprint.

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Creating a Data Science Project Plan

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1. Crafting a Compelling Story

By providing contextualised information, you create a strong foundation for your data science project.

Outlining the 'what’ and the ‘why’

Start your plan by establishing the business context and the current challenges that you are facing or can foresee. Then, define the problem or opportunity that the project aims to address, the value it will have on the business and why it matters. Why are you bothering with this project?

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Defining the ‘How’

Once you have outlined the specific problem that your data science project aims to address, you now need to consider how data science might be used and why this is the best method for this project. How will data science help to solve this problem?  

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Articulating the aims

To conclude this step, ensure the desired aim of the project has been outlined, include what the end state of the project looks like and the positive changes that the project seeks to bring together.  

Ensure to emphasise how the successful completion of the project will result in the desired outcomes and how this impact aligns with your organisation’s broader strategic goals. Where would you like to get to and how will this help your business move forward?

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2. Envisioning the Outcomes

Look to the end of the project and consider all of the potential impacts and the anticipated effects on the business, both tangible and intangible. Discuss the potential financial, cultural, digital, and transformative impacts and the impacts on stakeholders, team members, customers and the organisation as a whole.

Once you have outlined the impacts, these can be translated into concrete, quantifiable objectives using the SMART framework (Specific, Measurable, Achievable, Relevant, Time-bound). Define clear metrics and targets to track your project’s progress and evaluate overall success.

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3. Identifying Potential Risks

Recognising and anticipating possible risks during project execution is something else to consider when putting together your data science roadmap.

Risks may be issues with the quality of your data, technological limitations, stakeholder misalignment or even resource constraints. Once you have identified the risks, consider which strategies can be implemented to minimise the risks or to mitigate the hurdles if they do occur.  

By outlining proactive measures such as robust testing or alternative data sources, you can ensure that there is a strategy in place to smooth things out if and when any challenges do arise.  

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4. Assessing Digital Readiness

Assessing your digital readiness ensures you have the necessary data, technology, and skills at your disposal to execute the project.

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Data

Starting with your data set, you should evaluate the availability, accessibility and quality of the data that you require. In order to get started, you can ask yourself questions such as:

• Is the data we have suitable?

• Have we used this data before? If so, how?

• Is this data outdated?

• Is this data trustworthy?  

• Do we need to collect additional data?

• Do we need to pre-process our data?

• Is our data in the correct format for our project?

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Technology

Consider the technology you already have access to and whether your existing tech infrastructure is suitable for the project. You may need to outline a budget for any new hardware, software or cloud computing solutions.  

Here it is also important to think about scalability and security requirements of your existing or chosen technologies.  

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Skills

Whilst assessing your digital readiness, skills and expertise may need to be considered. There may be skill gaps within the team, and necessary upskilling training to be done before your during your data project. There may also be the need to collaborate with partners or bring in external parties to support your data science project. Ensure this is all highlighted within your plan and implemented early on where required for smooth sailing.

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Final Thoughts

By crafting a compelling story, outlining your goals, identifying possible risks and assessing your digital readiness, you will be setting the foundations for a successful data project, and it will be easier for you and your team to get started.

If you would like to learn more about some of the data science projects that our Data Scientists at the Hartree Centre North East Hub have completed, have a look at the case studies on our insights page.  

To learn more about our data science projects, or to chat about a possible data science project that you might have in mind, get in touch with our team at: hello@hartreenortheast.uk

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