Top 4 Challenges of Data Science & Simple Solutions For Them in 2023

Data science is one of the most exciting fields at present that are empowering companies to enhance their business. With so much data constantly being produced by network servers, IoT sensors, official social media pages, databases, and company logs, it has to be handled and cannot be ignored. Data scientists collect these data sets, remove the unwanted data and then, analyse it.

This analysis helps in understanding where the business currently stands and the areas where the company can improve. But, understanding data is not so easy. Data scientists and data analysts encounter problems, such as accumulating data, security issues and the lack of proper technology.

Challenges of Data Science

1. Identifying the data problem

One of the toughest challenges of data science is identifying the problem or the issue. Data scientists mostly start off with a huge data set that is often unstructured. They have to understand what they have to do with this data.

For example, they might have to analyse this data to solve a business problem, such as the loss of a specific pool of customers. Or, they might have to analyse business data to understand where they have suffered a loss in the past few years.


Before analyzing any data set, the best approach is to understand the problem that needs to be solved. Understanding the business requirement will help the data scientist to prepare a workflow. A checklist can also be created that can be checked off as the data is analyzed.

Read: Data Scientist Salary in India

2. Finding the most appropriate data

As companies produce huge amounts of data every second, it is a daunting task to get your hands on the right data for analysis. This is because the correct data set will be crucial for developing the most appropriate data model. The right data having the right format will take less time to clean and analyse.

For example, for analyzing the business performance of a company, you need the data set containing the financial data of the current year or the past few years. The amount of data is also important. Too much data is as harmful as insufficient data.

There may be a situation where you may have to access data from various sources, including customer logs and employee databases, which can be difficult. 


If you are a data scientist, you have to communicate with company officials for data. This ensures that you have all the required data sets for tackling the problem. Data management systems and data integration tools have to be handled as well. Data tools, such as Azure Stream Analytics, help in collecting data from different sources, aggregating them and filtering it.

Tools like these help in connecting all data sources and preparing a workflow.

Learn more: Top 7 Data Science Use Cases in Finance Industry

3. Lack of skilled workforce 

As more and more companies are becoming dependent on data science, the demand for skilled data professionals is increasing. This is one of the major challenges of data science at this hour. The traditional methods of working with data have changed. But, the fact is that many employees have not been able to keep up with the pace of developments.

Many data science professionals are just starting out as juniors without much experience. He/she might have the statistical and technical skills to play around with the data. But, the lack of experience and domain knowledge will not get him the results he/she requires. 

It is the responsibility of the higher officials of the company to enrich their workforce.


Companies must begin by investing more in the recruitment of data scientists, data analysts and data engineers. If required, they must create new job positions. Another step is to arrange for data science training and workshops for existing employees. Seminars can also be held to ensure that all employees have a basic understanding of data analysis.

Another innovative step taken by many companies is to buy modern data analytics software that runs on artificial intelligence. This software can be operated by employees who do not have a data science expertise but have the basic domain knowledge. This helps organizations cut down on their hiring and training costs.

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4. Data cleansing

Data cleansing or removing unwanted data from a data set is one of the pressing challenges of data science. It is observed that companies lose almost 25% of their revenue as cleaning bad data is costly. Working on data sets consisting of many inconsistencies and unwanted information can create havoc in a data scientist’s life!

As these professionals have to work with terabytes of data, inconsistent data can take many man-hours to cleanse. Also, these types of data sets can lead to unwanted and incorrect results.


Data governance is the best solution to this problem. It refers to the set of procedures for managing data assets within a company. Data professionals must use modern data governance tools to cleanse, format and maintain the accuracy of data sets they handle.

Best data governance tools are:

  • IBM Data Governance
  • OvalEdge
  • Collibra
  • Truedat
  • Informatica
  • Alteryx
  • Talend

Another important step that organizations need to take is to employ professionals for looking after data quality. As it is an enterprise issue, data quality managers must be present in every department to ensure the quality and accuracy of data sets.

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Wrapping up

Handling huge data sets and tackling the challenges of data science is a difficult task. Data science professionals are an integral part of large corporations in this present day. Apart from using the skills and expertise of data scientists, companies can also seek professional advice. Data science consultants can save the day by providing valuable insights on how to handle an organization’s data.   

Learn data science courses from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.

What type of challenges are faced by data scientists and data analysts?

Before applying data science algorithms and techniques, the foremost challenge that data scientists and data analysts face is to understand the data. There are several issues associated with it including identifying the issue with the data set, finalizing the right approach and techniques, and finding appropriate data sets.
These problems can be overcome using effective solutions such as understanding the business requirement and using modern tools.

How can one identify the problem related to the dataset and what is its solution?

The initial challenge faced by most of the specialists is to identify the problem with the dataset. Initially, the data is unstructured so it is hard to deal with tons of unstructured data for data scientists.
The best way to approach this issue is to figure out the problem that needs to be solved. You can also create a checklist that can be checked off as the data is analyzed.

Why is there a lack of skill force in the data science field and how can we overcome it?

With the tremendous rise in data generation, the demand for data scientists is growing much faster than their supply. Due to this huge demand-supply gap, the data science industry has been facing a lack of skill force.
In order to overcome this problem, companies should invest more in the recruitment processes. They could also organise training workshops on data science. A short term approach could be to hire the data science tools that run artificial intelligence.

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