10 Major Challenges Faced by Data Scientists

Data science is essential and is broadening its branches around the world. The invisible hand of data science in places such as the ranking algorithm governs the news streams, feeds, and the recommendation engines that decide the content we see on Netflix, Amazon Prime, and YouTube. Data science is a blend of various algorithms, tools, and machine learning principles that operate with the goal of discovering hidden patterns from raw data.


Data science experts work in the realm of the unknown. Some of the data science techniques they use are regression analysis, classification analysis, clustering analysis, association analysis, and anomaly detection.

Major Challenges

But the job of a data scientist is not easy. There are many challenges that they face on a daily basis, which they need to overcome. In this article, I will guide you on some of the significant obstacles faced by data scientists.

1. Issue identification

The prime challenge faced by data scientists while examining a real-time problem is to identify the actual issue. Not only do they have to understand the data, but they also need to make it readable for the layman. The information from the analysis should remove the significant bottlenecks in the business.

 2. Quality of Data

Algorithms are highly skilled at learning to do exactly what they are taught to do, but the problem occurs when data is poorly curated. Machine language is both a boon and a bane. Machines have the immense power to learn things rapidly, but they will be able to reproduce only what they have been taught by humans. Hence, data quality is of prime importance, and data scientists are faced with the major task of curating data.

3. Enormous Data Quantity

For a data scientist, the development of a robust model is a top priority. He needs to deal with enormous amounts of data. However, the more the model parameters, the more will be the data requirement. Also, it is quite difficult to find quality data to train such models. Even unsupervised learning algorithms demand a huge amount of data to derive a meaningful output.

4. Multiple and Vague Data Sources

Big data allows the data scientist to reach a vast and wide range of data from various platforms and software. But handling such extensive data poses a challenge to the data scientist. This data will be most useful when it is appropriately utilized.

5. Prediction

Many times in data science, unexpected results are obtained, which may or may not be the end with the rightful conclusions. In such a challenging situation, a data scientist should relay on supervised learning for future exploration, model selection, and appropriate selection of algorithms.

As revealed by a study conducted on a sample of 16000 data professionals and concluded the ten most difficult challenges faced by them in their profession. The challenges vary based on the job descriptions, and some of the challenges that were concluded based on the study were:

  • Lack of data science professionals.
  • Company politics.
  • Insights that are not used by the governing body.
  • Privacy issues.
  • Inaccessible data.
  • The organization not being able to afford a data science wing.

6. Data Cleansing

Big data is expensive for generating more revenue as data cleansing creates troubles for operating expenses. It is indeed a nightmare for every data scientist to work with databases that have inconsistencies and anomalies. Such unwanted data will only lead to unwanted results. For this reason, data scientists work with large amounts of data and spend a lot of time in cleaning the data before analyzing them. To solve this issue, data scientists can use data governance tools to improve data formatting and overall accuracy.

7. Lack of Professionals

It is one of the biggest misconceptions to think that all data scientists will be good at high-end tools and mechanisms. The fact is that data scientists must also possess subject depth and sound knowledge. Data scientists are known to bridge the gap between the IT department and the top-level management due to the domain expertise needed to convey the needs of the top-level management to the IT department and vice versa. To overcome this challenge, data scientists can master statistical and technical tools that will help them master the requirements of a business.

8. Issues with data security

Data security is a major issue they face as data is extracted from many interconnected channels, nodes, and social media, and hence there is an increased vulnerability of hacker attacks. Data scientists face problems with data usage, extraction, and building models or algorithms. The process of obtaining consent from users is a long-drawn process that results in major delays and cost overruns. Data scientists can rectify this by following the standard global data protection norms. Cloud platforms can be used for data storage.

9. Choosing the Suitable Algorithm

This is a subjective challenge as there is no one algorithm that works well on a dataset. In the case of a linear relationship between the feature and target variables. A data scientist must be aware of the models they need to use here, such as linear regression or logistic regression, and for a non-linear relationship, they must use models such as decision tree, random forest, etc. As there are many algorithms available, a data scientist may be confused, and hence, it is necessary for him to be well-versed in the same.

10. Communication of Results

Often, a company’s stakeholders or managers will be ignorant of the working structure of the models and the tools used. So, a data scientist must be able to help them make key business decisions based on the charts and graphs they present. As not everyone might not understand technical terms, it is important for a data scientist to be able to explain in layman terms.


Though a data scientist may face a wide array of problems or challenges, one should never compromise on the quality of data. Some of the recommended solutions for overcoming these challenges are:

  • In case of a specific problem, they must make a dataset using a Mechanical Turk.
  • Clustering the data naturally and labelling them collectively.
  • Making use of data archives that have been properly collected.

Another option is for data scientists to design and create meta-algorithms that can help data from similar yet different datasets. Data science experts can also cluster, map, and adapt different data sets and data types in an unsupervised way.

To learn more about data science certifications and become a data science expert, check out Global Tech Council.