Organizations across the globe want to organize and process the value of the overwhelming amounts of data they generate and transform them into high-value business insights. Hence, hiring data scientists who are professional experts in the field has become of utmost essence. Today, there is virtually no business function that cannot require them. The Harvard Business Review has labeled data science as the “sexiest” career of the 21st century.

However, no career is without its challenges, and this does not exempt data scientists. According to the Financial Times, many organizations are failing to make the best use of their data scientists by being unable to provide them with the necessary raw materials to drive results. In fact, according to a Stack Overflow survey, 13.2% of the data scientists are looking to set out in search of greener pastures – second only to machine learning specialists.

Here are some of the major challenges that could be attributed to this noble career and how they can be solved:

1) Data Preparation

Data scientists spend nearly 80% of their time cleaning and preparing data to improve its quality. This makes it accurate and consistent, before utilizing it for analysis. However, 57% of them consider it as the worst part of their jobs, labeling it as time-consuming and somewhat cumbersome. They are required to go through terabytes of data, across multiple formats, sources, functions, and platforms, on a day-to-day basis, whilst keeping a log of their activities to avoid duplication.

One way to solve this challenge is by adopting emerging AI-enabled data science technologies like Augmented Analytics and Auto feature engineering. Augmented Analytics automates manual data cleansing and preparation tasks and enables data scientists to be more productive.

2) Multiple Data Sources

As organizations continue to make use of different types of apps and tools and generate different formats of data, there will be more data sources that the data scientists need to access to produce meaningful decisions. This process requires manual entry of data and time-consuming data searching, which leads to errors and repetitions, and eventually, poor decisions.

Organizations need a centralized platform integrated with multiple data sources for instant access to information from multiple sources. This would enable the effective assemblage and control of data which would in turn improve its utilization and save huge amounts of time and efforts of the data scientists.

3) Data Security

As organizations make transit into cloud data management, cyberattacks have become increasingly common. This has caused major problems like the vulnerability of confidential data. Moreover, regulatory standards have evolved, as a response to repeated cyberattacks, which have extended the data consent and utilization processes adding to the frustration of the data scientists.

Organizations should employ advanced machine learning-enabled security platforms and instill additional security checks to safeguard their data. They must also ensure strict adherence to the data protection norms to avoid time-consuming audits and expensive fines.

4) Understanding The Business Problem

Having an in-depth understanding of the business problem before performing data analysis and building solutions is very essential for data scientists. Most data scientists usually dive into analyzing data sets without clearly defining the business problem and objective.

Therefore, data scientists have to follow a proper workflow before starting any analysis. The workflow must be created after collaborating with the necessary stakeholders and consist of well-defined checklists to improve understanding and problem identification.

5) Collaboration with Data Engineers

Since organizations usually have data scientists and data engineers working on the same projects, then there has to be effective communication amongst them to ensure the best output. However, the two usually have different priorities and workflows, which might sometimes cause misunderstanding and hinder the sharing of ideas between both parties.

Management should take active steps to enhance collaboration between them to foster open communication. This can be achieved by setting up a common coding language and a real-time collaboration tool.

Conclusion

Data scientists are considered to be the most in-demand professionals in the market despite all the challenges stated above. With the development of data science at a great pace, being a successful data scientist is not just about having the right technical skills but also about having a clear understanding of the business requirements, collaborating with different stakeholders, and convincing business executives to act upon the analysis provided. These would enable a better platform to crush the challenges and their implications to the concerned persons and/or organizations.