At this point, data science has reached every field and business on earth. Many companies have realized that data science can provide competitive advantages, so they want to implement data projects in their companies.
As much as we want things to go smoothly, we must overcome challenges. In data science, there are a few unique challenges you might not find in another field, so only if you work as a data scientist that you know how to solve them.
What are these challenges? And how to overcome it? Let’s explore them together.
1. Model Interpretability and Explainability
Businesses want to be confident when using our model. However, there have been many times when data scientists have provided the prediction output without explaining why our model provides the output. Usually, it would be like, “The customer would churn with 75% probability.”
What would a business do with that? What does it mean by churn 75%? Do the business need to do something? Why does the model provide that output? These are the questions businesses would ask.
Therefore, we need the model interpretability and explainability to explain the model output. We can do that using techniques like SHAP or LIME. It’s also possible to use a simple model that is easy to interpret. However, the best explainability is combining the technical explanation with the business language. Discuss the result with your business counterpart to create confidence in your result.
2. Data Lineage in Complex Pipelines
In the working data science pipeline, our model would have multiple data pipelines that run through systems for data collection and processing before they are made into the model for prediction. The standard pipeline would need us to track all the data origin and journey, which we call data lineage.
The problem with data lineage is that maintaining a good lineage becomes more challenging with a more complex pipeline, and if we don’t do it right, we could accrue massive technical debt.
In this case, we need to rely on many existing data lineage tools, such as Apache Atlas, while keeping standard documentation with a standard template for everyone. Managing the metadata separately with governance frameworks would also help the data lineage process.
3. Cross-Disciplinary Collaboration Barriers
A data science project isn’t one in which you can just develop a model and expect the output to do by itself. We need to collaborate with the business users as the experts in the problem we want to solve and other supporting departments to ensure the data project runs well.
In that sense, data scientists need to understand both the domain business we are trying to solve while also balancing the technical aspect. We need to efficiently communicate both business and technical so there would be a better understanding in between.
To overcome misunderstandings and keep the project running, we need to develop interdisciplinary teams that include the data scientist and business—at the same time, establishing a clear business goal the project would need to solve. We can successfully run a data science project by having both parties meet in the middle.
4. Ethical Challenges in Bias Prediction
Unique to the data science project is that our prediction output would have ramifications for business decisions. For example, credit default or fraud detection projects directly impact people. But sometimes, the bias present in the data can be reflected in the output, causing ethical problems.
Imagine that our model predicts someone’s fraud because of their living location, age, gender, and any social measurement. It would not be a good reason, and the company would be scrutinized for allowing biased thinking from the model.
Addressing bias in the data science project is complex because it can be present in the pipeline at various stages, from data collection and preprocessing to model selection and deployment. That’s why we need to minimize bias by ensuring all the processes are as bias-free as possible and making things fair. Employing bias detection and constraints would also help reduce bias in the project.
5. Machine Learning Impact on the Environment
The machine learning model must be trained on the dataset to learn the pattern and provide values. The bigger the data and the model we use, the bigger the resources we need to reserve for the training process. More extensive resources also mean more considerable energy consumption, contributing to increased carbon emissions.
In the current environment, putting a significant burden on the environment is unwise. Businesses also have many initiatives to become more green than ever. So, as a data scientist, we need to think about the model we need to build with the impact on the environment.
As data scientists, we can optimize the model to reduce its size without sacrificing much of its performance by using pruning, quantization, and any other model optimization technique. We could also use more energy-efficient architectures and monitor energy consumption.
Conclusion
Data science presents various challenges you would not find in other fields. These include Model Interpretability and Explainability, Data Lineage in Complex Pipelines, Cross-Disciplinary Collaboration Barriers, Ethical Challenges in Bias Prediction, and Machine Learning’s Impact on the Environment. As each challenge is unique, we also require unique ways to solve the problem, which we discuss in this article.
I hope this has helped!
This post was originally published on here
