Overcoming 8 Challenges that Data Science Projects Face

In the previous blog, we looked at 8 challenges that data science Training projects face in the real world. We excluded ‘technical competence’ as a challenge and worked with the assumption that the brains behind the projects are technically sound. Instead, we looked at practical issues like no management buy-in, incompatible workflow, etc. In this blog, we will examine how you can overcome these challenges with some of the viable solutions.

No management buy-in

Often cited as the reason most ideas don’t take off, getting a management buy-in is as important for data science projects as it is for any strategic business decision. The inability to secure management buy-in could be attributed to several reasons. Let’s examine how one can go about unlocking this challenge.

First, start by understanding the mission of the company. Then, figure out whether your project is of significance to the mission. The project need not be an immediate answer to a problem; it could be an R&D initiative too. As long as it is viewed as something of strategic importance, the management will be on board with it. Now that the first hurdle is out of the way, let’s look at some best practices:

Take the initiative and explain what you actually do to the management. Don’t get into jargon slinging. Keep it simple and relevant. And most importantly, contextualize it. As long as the management is able to view this from the pov of their business model, they are more likely to warm up to it. In case the management feels that data science isn’t the answer to their problems, do some research and get back to them with use cases and whitepapers where data science has helped in solving similar problems. If the management is taking too much time to decide or seem to be resistant, keep them updated with developments in your project. This is a good reinforcement strategy.

Lack of Scalability

Scalability issues can derail your project, making it redundant to adaptation when required in further iterations. To manage scalability issues, you must consider the following:

  • Plan for a broader roadmap when building the PoC or the first version
  • Talk to the end-users and gather actionable feedback from an unbiased sample
  • Outline and acquire suitable resources and budget
  • Don’t lose sight of expectations
  • Be ready to adapt

Not ‘problem’ driven

Start with a problem statement and work your way forward. This is a key step if you want your project to be successful. Identify the problem by talking to key stakeholders. Next, look at your data and understand whether it is the ‘ideal’ data Science Training to address the problem statement. If not, seek other data resources.

Incompatible workflow

You must always deign workflows with the end-user in mind. Therefore, start by defining the end-users and the target audience. Also, understand who isn’t a part of this group. It may sound obvious but knowing who your end-users don’t help in streamlining the project.

Overfitting

Build models that ‘generalize’. In simple terms, build models that make the model usable outside the original data Science Training set. The model must adapt and apply well to data that it did not see at the time of training. A good model represents a genuine relationship between variables. It will make more accurate predictions with more favorable r-squared values and regression coefficients.

Doesn’t solve Business Problems

To ensure that your project solves business problems, start with a clear understanding of the business problems. If you follow the first step as prescribed, you will have the management on your side at this point. Talk to the various stakeholders and understand business challenges from their perspective. List out the impact on three areas, namely: business impact, feasibility, and urgency. Rope in the management if needed. This approach will help you understand the biggest problem facing the business. Start by building solutions to address that problem.

Too many cooks

Clearly define roles and responsibilities. There is no other way around this. Taking a structured approach towards dividing responsibilities is crucial. Understanding the strengths and weaknesses of each individual contributor would be immensely helpful is defining roles and responsibilities. In case you are working on the project alone, figure out who you wish to consult with, if at all. Pace yourself well and make steady progress.

Lack of domain knowledge

Artificial intelligence, machine learning, and deep learning by themselves are an ocean of possibilities that no one man can completely master. Even if you study and research at the best data science institute, you are still scratching the surface. To compliment all that with domain knowledge could be too much of an ask. While no one can be an expert in all the domains out there, it is possible to develop reasonable expertise in one or two domains if you’ve been working on them long enough. However, in case you don’t have the required expertise, collaborate with subject matter experts to enhance your knowledge. This approach will help you to close the gap faster.

Hopefully, this blog was helpful to you. If you encounter any problem that isn’t listed here, please share the same for the benefit of our audience. Cheers!

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