How to Successfully Running a Data Analytics Project

In the world of data analytics, whether you’re working in a corporate environment, freelancing, or managing your own consultancy, you’ll inevitably find yourself at the helm of a data project. These projects can range from building predictive models and answering critical business questions to developing dashboards. No matter the specifics, running a data project successfully requires a structured approach. Through my experience, I've identified several key steps that are crucial to ensuring the project's success. Here's a detailed guide to help you navigate your next data project.

1. Define the Business Problem

The foundation of any successful data project lies in a clear understanding of the business problem. Your first task is to take the brief from the client and then replay it back to them. This step is critical, it ensures that both you and the client are on the same page.

Ask yourself:

• What exactly does the client want to achieve?
• Are there any specific outcomes or KPIs they’re looking for?

If the client confirms that your understanding is correct, you're on the right track. However, if there's any discrepancy, it’s better to address it upfront. Misunderstandings at this stage can lead to significant issues later in the project. Whether you discuss this over email, face-to-face, or in a kickoff meeting with all stakeholders, clarity at this point is non-negotiable.

2. Plan How to Access the Data

Data access is often underestimated in terms of time and complexity. While it may seem like a simple task, getting the data you need can involve multiple steps:

• Identifying the correct contact who can provide access
• Securing the necessary logins or approvals
• Extracting the data in the required format

To account for these potential delays, I recommend allocating 5-10 working days in your project plan just for accessing the data. This timeline may seem lengthy, but from experience, even seemingly minor hurdles can extend this phase. Including this buffer in your plan ensures that expectations are managed on both sides. You don’t want to be in a position where you’re expected to start analysis immediately after a kickoff meeting but are still waiting for data access a week later.

3. Prepare the Data

Once you have the data, the next step is preparation. This involves:

• Cleaning the data to remove any inaccuracies or inconsistencies
• Transforming the data into a usable format
• Joining tables if you’re working with multiple data sources

Data preparation is crucial because the quality of your analysis depends on the quality of your data. I usually allocate 2-5 days for this phase, depending on the complexity and volume of the data. Even if you’re familiar with the data source, never skip this step—it’s better to be thorough now than to encounter issues later.

4. Conduct an Exploratory Data Analysis (EDA)

Before diving into the main analysis, it's essential to conduct an Exploratory Data Analysis (EDA). This step might seem redundant, but it serves a critical purpose: understanding the data and ensuring it aligns with the business's existing reports and expectations.

For example, if you’re analyzing transactional data, verify that your findings, such as the number of transactions over a specific period, match the business’s records. Any discrepancies here could indicate issues with your data understanding or preparation, which would undermine the entire project if left unaddressed.

Allocate about two weeks (10 working days) for EDA. This time allows you to ensure that the data you’re working with is reliable and that your analysis will yield accurate and actionable insights.

5. Run the Analysis

With a solid foundation in place, you can now proceed to the main analysis. Whether you're building a dashboard, developing a predictive model, or conducting another form of analysis, this phase is where the core work happens.

The timeline for this phase will vary depending on the project's specifics. For example, building a churn model or another predictive model might take 4-8 weeks. Base your estimate on past experiences and the complexity of the task at hand. Remember, this is a bespoke part of the project plan, so adjust your timelines accordingly.

6. Schedule a Feedback Session

Before you consider the project complete, it’s essential to present a near-final draft to the client for feedback. This step ensures that the final product meets the client’s expectations and allows for any necessary adjustments before the final handover.

Timing is key here:

• Don’t present too early, as there will still be elements you’re working on.
• Don’t wait until the final moment, as making changes at that stage can be time-consuming and frustrating for all parties involved.

I recommend scheduling this session face-to-face whenever possible. This allows you to walk the client through the analysis or model, explain your findings, and clarify any uncertainties. Scheduling this meeting in advance (ideally during the initial planning stage) also gives you a clear deadline to work towards, helping to avoid procrastination and ensuring the project stays on track.

7. Handover and Finalize the Project

Finally, once all feedback has been incorporated and the project is ready, it’s time for the handover. However, simply sending the final product via email isn't enough. A formal handover session is crucial for a few reasons:

• It provides a clear endpoint for the project, preventing an endless loop of revisions and changes.
• It ensures that the client knows how to use the final product, whether it’s a dashboard, model, or analysis.
• It allows you to formally close out the project, with any further requests or changes becoming part of a new project.

During the handover session, walk the client through the final product, answer any remaining questions, and ensure they’re confident in using it. This session marks the official end of the project and ensures a smooth transition from development to implementation.

Final Thoughts

Running a data analytics project requires a combination of technical skills, project management, and clear communication. By following these steps, defining the business problem, planning data access, preparing the data, conducting EDA, running the analysis, scheduling a feedback session, and formalizing the handover, you can ensure that your projects are not only successful but also efficient and aligned with client expectations.

If you found this guide helpful, feel free to share it with your network or reach out at [email protected] if you have any questions or insights to add.

Thank you for reading! If you enjoyed this article, please consider liking and sharing it with your connections. I’m always here to discuss more about data analytics, let’s connect and if you have any analytical projetcs you need help with contact us at [email protected].

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