Understanding the Common Hurdles Faced by Data Scientists in Their Day-to-Day Work
As the world embraces the vast potential of Artificial Intelligence (AI) and Machine Learning (ML), it is essential to acknowledge the challenges that data scientists face in their daily work. While the excitement surrounding AI and ML is justified, it is crucial to address the common problems that hinder progress in this field. By recognizing and solving these challenges, data scientists can enhance their effectiveness and achieve greater success.
Deciding which business problems are best solved in the data science way
Managers often fall victim to the hype surrounding machine learning and attempt to apply data science solutions to every business problem. However, this approach is not always appropriate. Some issues can be resolved through process improvement, additional staffing, or IT application modifications, without the need for complex machine learning models. It is crucial to identify which problems truly require data science solutions to optimize cost and effectiveness.
Bad quality of data
Data scientists frequently encounter low-volume data, missing values, outliers, and junk values. These challenges necessitate significant effort in data preparation. Well-prepared data leads to better insights and more accurate models. Additionally, historical data used for building supervised classification models often suffers from class imbalance, requiring data scientists to address this imbalance through oversampling or undersampling techniques.
Encountering new classes during prediction
In some cases, the training dataset may not include a particular class of a feature variable. If this missing class appears during real-life prediction, the model fails. To mitigate this issue, data scientists often select larger training datasets and smaller test datasets, ensuring that all possible classes of feature variables are included for training. Alternatively, stratified sampling can be employed during the creation of the training dataset to ensure representation of all classes.
Selecting the most useful metric for model evaluation
Different regression, classification, and unsupervised modeling techniques require the consideration of multiple evaluation metrics before approving a model for production. However, models often excel in some metrics while falling short in others, making it challenging to make a final approval decision. To address this ambiguity, data scientists can rank order the metrics based on criticality and ensure that all top metrics meet the required standards. It is essential to establish acceptable values for each critical metric before building the baseline model.
Premature celebration in case of high technical performance
While technical performance is important, data scientists should always prioritize the real-life outcomes that the ML model brings to solving business problems. Successful outcomes may include better customer experiences, higher Net Promoter Scores (NPS), increased revenue, cost savings, increased product demand, or improved operational efficiency. Real-life performance should be the ultimate focus, rather than technical performance before implementation.
Effort estimation of data science projects
Estimating the time and effort required to deploy a data science solution in a live environment and achieve Return on Investment (ROI) can be challenging. The experimental nature of machine learning model building often involves exploring and discarding numerous options and models before arriving at the best solution. This uncertainty makes accurate effort estimation difficult.
Attributing a positive business outcome to a Data Science solution
Determining the portion of ROI attributable to a newly deployed data science solution versus other independent factors is a complex task. While the industry recognizes the value addition from AI and ML solutions, quantifying this value remains a challenge. Clearer methods for attributing positive business outcomes to data science solutions are needed.
High number of classes of a categorical feature variable
Models often perform poorly when faced with a high number of distinct values in a categorical variable. To address this issue, data scientists can group these distinct values into manageable classes through bucketing techniques. By reducing the number of classes, the model’s performance can be improved.
Conclusion:
Standardizing the execution process of data science projects through best practices can help mitigate the challenges faced by data scientists. However, it is crucial to strike a balance between standardization and allowing room for out-of-the-box thinking and innovation. Ultimately, innovation is the driving force behind the field of AI and ML, and it is through creative problem-solving that data scientists can overcome the hurdles they face.
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