Challenges in the stages of a data science project: an insider’s guide to success

Unravelling the realities of data science project flow: from initial client meetings to final deployment

Most people envision a data science project as a neatly organised process comprising equal parts of data cleaning, data analysis, and deployment. However, reality seldom conforms to this utopian view.

Nevertheless, understanding the major stages of a data science project can help to manage expectations and allocate resources more effectively.

The Importance of Data Cleaning

Data science projects often commence with a substantial amount of data that needs cleaning and wrangling. This process can be time-consuming and will often overlap with the data analysis portion of the project.

The tendency, therefore, is to underestimate the time required for deployment.

The Challenge of Deployment

Also integral to every data science project is the deployment stage. It requires consistent dialogue between the technical and business teams — a process that can be fraught with organisational politics. As such, the deployment phase might extend far beyond its anticipated completion time.

The Critical First Step: Securing Data

The success of a data science project is predicated on the availability of data. The initial stages of the project often include:

• Gaining the client’s trust
• Signing a Non-Disclosure Agreement (NDA)
• Accessing the data
• Navigating the client’s systems
• Identifying key stakeholders

These initial steps can often require about a month to complete. However, they are essential to ensure the project does not stumble on the blocks of unavailability of data.

Read the full article about “The Key stages in a data science project” here.

Overcoming Data Access Challenges

Data access can pose considerable challenges, primarily when data is protected by stringent regulations. Therefore, gaining access to a company’s internal data requires a substantial degree of trust between the client and the data science consultancy.

The Solution: Thorough Planning

To preempt potential issues that may delay the project, thorough planning is paramount:

• Send a list of requirements to the client one month before the project start date
• Arrange a kickoff meeting for a week after the email, ideally securing some — if not all — of the requirements
• Continually correspond with stakeholders to ensure everything is in place for the project’s inception

By undertaking these steps, the project can progress without any data-related hindrances.

The Bottom Line

Countless obstacles can hinder a data science project, foremost of which is the lack of data. Expenses bourne due to undue delays can be mitigated with meticulous planning and proactive communication.

For more guidance on planning your data science project, visit the Fast Data Science website.