5 Challenges Facing Data Scientists Today and Their Solutions

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Data science is an ever-growing field that improves with new technology and research. It’s an exciting time for any data scientist, as our work only improves with these improvements.

However, new things accrue new challenges. In this modern era, data scientists must face a few things we might not have previously.

This article will discuss the challenges and possible solutions for them.

What are they? Let’s explore it together.
 

1. Generative AI Overshadowing Traditional Models

 
We are in an era where Generative AI is at the forefront of everything. If you open up your social media, many posts will be about Generative AI model implementation and their derivative. Even businesses rush to implement as fast as possible for Generative AI.

The boom came around when the ChatGPT product was released, and everyone saw how beneficial the Generative AI model is. Then, people started to use the model even more for many applications and equated it with a silver bullet for any business problem. That should not be the case.

Only some business problems can be solved with Generative AI; even if it could, it would not be as efficient as traditional models.

Many business use cases that happen only need a simple traditional model implementation, such as automating the prospect customer detection or detecting fraudulent activities. These are often simple classification tabular models that do not need Generative AI to solve.

The solution to this challenge is to understand the Generative AI and traditional models even better. Understanding what they are and knowing where to use them would improve the efficiency of solving the business problem.
 

2. Data Quality Issue

 
With the advancement of technology, everything can now be stored as data and used for subsequent activities. The concept of Big Data then arises from the amount of data present.

However, only some data is ready-to-use or appropriate for the use cases. Some need to be corrected because we are inadequate in storing and preprocessing the data. There are also many cases in which the data source needed more quality control, and the resulting data were messy.

The above data quality and inconsistency challenge could affect the model performance and the insight given. That’s why we, as data scientists, need to pay extra attention to data quality,

To alleviate our data quality issue, we must work with business people and data engineers to ensure the highest-quality data source and storage. Data scientists could also use automation data preprocessing tools to detect and address data quality issues early. A robust data pipeline also helps ensure high-quality data is fed into the model and analysis.
 

3. AI Ethics and Bias

 
With the advancement of machine learning model technology, many decisions that previously required human feedback in the loop are being automated. Not only automate decisions, but a lot of insight and suggestions could also provided by the Generative AI models.

With so much machine-made output, the ethics and bias of the model have become a priority for many regulatory bodies. Significantly, if the output decision could affect people's lives and cause discrimination, it’s become the problem that is being highlighted by the authority and government,

As much as we want to have the best model, the best solution to address the ethical and bias challenge is to follow the regulations that have been stated. We can challenge them if we feel it isn’t right, but regulation is there to protect everyone.

Involve the data governance and regulations within the data project while constantly auditing our model to avoid bias. Use model explainability as much as possible to demonstrate the model's fairness and ability to detect bias.
 

4. Cost Management Problem

 
If you read the main points of our discussion, most of them were related to how advanced the current technology is and how easy it is to process big data to acquire our model. However, using technology isn’t necessarily free, as obtaining the benefit is always a trade-off.

Running experiments in the cloud platform, training the large language model, having real-time automation decisions, and many more are things we can do in the current modern era. They are helpful to the business but could incur a higher cost to the company if we don’t manage them right.

Cost management becomes essential when implementing machine learning model technology. In production, we can’t play around with cost management, as expenses could cause the whole business to collapse if they’re not treated right.

The first step is understanding if our model or solution is essential to the business problem. Additionally, are there any ways to improve the process without incurring additional costs, such as using smaller model parameters, using only batch prediction, minimizing the cloud platform usage, and more.

Discuss the cost with related financial departments and business people. Assess the necessity and budget the company allows for the data science technology.
 

5. Keeping Up with Technological Advancements

 
Lastly, the biggest challenge any data scientist faces in the current era is keeping up with technological advancements. There are so many papers and breakthroughs released daily that it is hard for data scientists to upskill.

Of course, not all technology would be necessary to your business or job. However, remember that new things will always emerge, no matter our situation. The world would just keep turning, so there is no better solution to keeping up with everything than setting aside your time to learn about them.

Manage your time well and try to focus on your learning. Stay informed through community involvement or subscribe to specialized article newsletters like KDnuggets.
 

Conclusion

 
Modern problems need modern solutions. Data scientists face many issues that haven’t shown up in the past. In this article, we discuss five different challenges and their solution. The problems are:

1. Generative AI Overshadowing Traditional Models
2. Data Quality Issue
3. AI Ethics and Bias
4. Cost Management Problem
5. Keeping Up with Technological Advancements

I hope it helps!
 
 

Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machine learning topics.