Challenges for Modern Data Scientists and How to Address Them

Data science is an innovative industry that shapes industries and drives decision-making through analytics. However, over time and with the growth, challenges have emerged through which data scientists fail to make significant gains in their analyses. Thus, data science professionals need to harness the full potential of data science to understand these challenges and their possible solutions.  

Challenges Faced by Data Scientists 

Understanding these challenges and solutions is vital for professionals to conquer in their respective worlds.  

In today’s digital era, everything is stored as data is used for subsequent activities with the help of technological advancement. The concept of Big Data develops from the available data. However, not all the data is ready for application use. Adapting some of them requires correction because it is simply imperfect in how data is processed and stored.  

Solution: To solve the data quality problem, data scientists employ data preprocessing automation tools to check for data quality weaknesses. Using a strong data pipeline also assists in feeding the model and analysis only with high-quality data.  

Before they set out to analyze it, data scientists reported spending roughly 80% of their time cleaning and preparing data. However, 57% consider it the worst part of their jobs, describing it as time-consuming and repetitive. They are expected to sort through terabytes of data in different formats, sources, functions, and platforms daily while maintaining a record to ensure no work is repeated.  

Solution: An effective way to address this challenge is leveraging new AI-based data science trends such as Augmented Analytics and Auto feature engineering. Augmented Analytics removes specific manual data preprocessing steps and helps data scientists be more efficient.  

Data scientists are faced with the issue of how to deal with these large datasets as organizations produce significant data. Historical methods of processing data and technology consumption cannot handle big data and may slow down performance.  

Solution: To overcome big data analysis problems, data scientists can use various tools developed specially to work with significant amounts of data. These include Hadoop, Spark, and other distributed computing systems, as well as data storage and cloud computing paradigms.  

Security and general compliance are key issues arising from organizations dealing with large information volumes. Since data scientists must work with sensitive data, finding a balance between protection and usability is critical. It forces compliance with data protection laws, including GDPR, while still allowing data scientists full access to data is complex.  

Solution: With regards to a firm’s data security concern in data science, there is a need to address this through sound and effective data governance frameworks. These comprise data access control, data encryption methods, and data anonymization. Data catalogs serve the same purpose as managing data access, where the admin can control data accessibility for each dataset for roles and permissions, allowing only authorized data scientists to use the data they require without compromising on data security and privacy.  

Data scientists work with input from management, who may not understand the analysis details and the language used. If an executive, a stakeholder, or the client cannot comprehend the proposed models, their solutions will not be implemented. 

Solution: This is something that data scientists can do. They can introduce concepts such as ‘data storytelling’ to provide direction on communication and a compelling story to insights and charts.  

Data scientists and data engineers typically work in the same organizations. This implies that there must be a communication flow across them to produce the best results. However, their goals and tasks are often different and have different processes, leading to misunderstanding and preventing knowledge exchange.  

Solution:  Supervisory teams should make demonstrable efforts to improve collaboration between data scientists and engineers. It can introduce direct communication by establishing a shared coding language and enabling the utilization of an instant messaging application. Further, when Google assigned one officer, the Chief Data Officer manage both departments, it helped them work harmoniously.  

Data scientists work in all industries, including healthcare, finance, and retail. Understanding this aspect of data and its implications is tricky without any particular domain knowledge. 

Solution: Collaborating knowledge from domain experts reduces this gap significantly. To address this, ongoing training through specified industry courses and data scientist certifications equipped them to frame analyses appropriately. Creating cross-functional teams leads to problem-solving and systematically addressing various issues.  

Conclusion 

Despite the numerous obstacles data scientists encounter, it is only possible to overcome them through proper strategy and tool usage. Understanding data discovery, data quality, big data analysis, data security, and communication issues enables organizations to optimize their data assets and deliver tangible values to their data scientists and business units.