8 Major Challenges Faced By Data Scientists (1)

8 Major Challenges faced by Data Scientists

Organizations across the globe are looking to organize, process and unlock the value of the torrential amounts of data they generate and transform them into actionable and high value business insights. Hence, hiring data scientists – highly skilled professional data science experts, has become super critical. Today, there is virtually no business function that cannot benefit from them. In fact, the Harvard Business Review has labeled data science as the “sexiest” career of the 21st century.

However, no career is without its own challenges, and being a data scientist, despite its “sexiness” is no exception. 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 jump ship in search of greener pastures – second only to machine learning specialists. Having helped several data scientists solve their data problems, we share some of their common challenges and how they can overcome them.

Challenges faced by Data Scientists

1. Data Preparation

Data scientists spend nearly 80% of their time cleaning and preparing data to improve its quality – i.e., make 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 highly mundane. 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 prevent 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.

Learn More: Augmented Analytics – Everything You Need To Know

2) Multiple Data Sources

As organizations continue to utilize 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 to instantly access information from multiple sources. Data in this centralized platform can be aggregated and controlled effectively and in real-time, improving its utilization and saving huge amounts of time and efforts of the data scientists.

3) Data Security

As organizations transition into cloud data management, cyberattacks have become increasingly common. This has caused two major problems –

  1. Confidential data becoming vulnerable
  2. As a response to repeated cyberattacks, regulatory standards have evolved which have extended the data consent and utilization processes adding to the frustration of the data scientists.

Organizations should utilize advanced machine learning enabled security platforms and instill additional security checks to safeguard their data. At the same time, they must ensure strict adherence to the data protection norms to avoid time-consuming audits and expensive fines.

4) Understanding The Business Problem

Before performing data analysis and building solutions, data scientists must first thoroughly understand the business problem. Most data scientists follow a mechanical approach to do this and get started with analyzing data sets without clearly defining the business problem and objective.

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

5) Effective Communication With Non-Technical Stakeholders

It is imperative for the data scientists to communicate effectively with business executives who may not understand the complexities and the technical jargon of their work. If the executive, stakeholder, or the client cannot understand their models, then their solutions will, most likely, not be executed.

This is something that data scientists can practice. They can adopt concepts like “data storytelling” to give a structured approach to their communication and a powerful narrative to their analysis and visualizations.

Learn More: Use Data and Analytics to Tell a Story

6) Collaboration with Data Engineers

Organizations usually have data scientists and data engineers working on the same projects. This means there must be effective communication across them to ensure the best output. However, the two usually have different priorities and workflows, which causes misunderstanding and stifles knowledge sharing.

Management should take active steps to enhance collaboration between data scientists and data engineers. It can foster open communication by setting up a common coding language and a real-time collaboration tool. Moreover, appointing a Chief Data Officer to oversee both the departments has also proven to have improved collaboration between the two teams.

7) Misconceptions about the role

In big organizations, a data scientist is expected to be a jack of all trades – they are required to clean data, retrieve data, build models, and conduct analysis. However this is a big ask for any data scientist. For a data science team to function effectively, tasks need to be distributed among individuals pertaining to data visualization, data preparation, model building and so on.

It is critical for data scientists to have a clear understanding of their roles and responsibilities before they start working with any organization.

8) Undefined KPIs and metrics

The lack of understanding of data science among management teams leads to unrealistic expectations on the data scientist, which affects their performance. Data scientists are expected to produce a silver bullet and solve all the business problems. This is very counterproductive.

Therefore, every business should have:

  1. Well-defined metrics to measure the accuracy of analysis generated by the data scientists
  2. Proper business KPIs to analyze the business impact generated by the analysis

Conclusion

Despite all the challenges, data scientists are the most in-demand professionals in the market. With the data world changing at a rapid pace, being successful data scientists 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.

If you’re a data scientist facing any of these challenges and would like to learn more about overcoming them, please feel free to get in touch with one of our data science and business intelligence experts for a personalized consultation. You might also be interested in exploring how we’re helping data scientists across the world with our BI and analytics solutions.

Further Insights:

If you’d like to learn more about this topic, please feel free to get in touch with one of our AI and digital workplace consultants for a personalized consultation.

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