Top 8 Data Science Challenges Data Scientists Face and How to Fix Them?

Following the digital revolution, we now live in a data-driven world. This has made data analytics an important part of businesses looking for new ways to organize, process, and unlock their data’s full potential for actionable insights. However, executing data analytics is not easy as it may seem since only a limited percentage of businesses have been able to leverage its true potential. Some of the problems causing this discrepancy are not company-specific but rather common issues most organizations face. These data science challenges that data scientists face may include not being able to hire skilled data engineers, problems organizing raw data, security vulnerabilities, and more.

This blog post will discuss some of the everyday data science challenges that data scientists face and what you can do to fix them.

8 Day-to-Day Challenges Data Science Engineers or Data Scientists Face and How to Fix Them?

The volume of data, lack of skilled professionals, and domain itself can cause challenges for data science engineers or data scientists. Here are some of them:

Top 8 Data Science Challenges Data Scientists Face and How to Fix Them 1

Multiple sources of data

Companies use various tools and mobile applications to collect and manage employees, customers, or sales information. Now, collecting data from heterogeneous sources may lead to disparate, unstructured, or semi-structured information at hand. With non-uniform formats to tackle, data scientists may find it challenging to analyze and derive meaningful insights.

As a result, considerable time is spent filtering the correct information. Since this is a manual effort, it becomes time-consuming, error-prone, repetitive, and leads to unreliable decision-making.

Data security

The growing importance of big data has inspired several organizations to pull their sleeves up and focus more on storing, managing, and analyzing the data. However, while working on this aspect, they overlook the security concerns associated with large data sets.

And as data security is of utmost priority, especially in organizations that handle sensitive data or customers’ personal information, the entire effort could become counterproductive.

Some of the common data security infringements include

  • Cyberattacks on data systems
  • Ransomware
  • Theft

Lack of understanding of the business problem

Many data scientists would jump right into identifying datasets and performing data analysis without having a clear picture of the business problem to solve. This results in ineffective decision-making that renders the entire goal of data analysis futile.

Also Check: How to Solve 6 Biggest Data Integration Challenges

Shortage of skilled data scientists

Organizations still struggle to sustain and hire skilled talent to handle their data and analyze it for meaningful insights. Recruiting the right data team with in-depth knowledge and domain expertise is difficult.

While companies are still trying to equip their staff with the best tools and techniques available. So, a large gap still needs to be covered.

Lack of defined KPIs and Metrics

If the key performance indicators (KPIs) and metrics have not been clearly defined. Management teams may have unrealistic expectations of the data scientists. However, some data scientists can still design machine learning (ML) models and generate accurate results. You can hire dedicated ML engineers for such data analytics requirements.

Quality of Data

Another frequent mistake most data scientists make is in inputting incorrect data. The entire data analytics exercise becomes flawed if the input data is wrong. So, this is primarily caused due to manual errors. Similarly, these discrepancies can also arise when changes are made in one system without implementing them in others, leading to asymmetric data.

Data visualization

Besides analyzing data, the reports generated should be clear, precise, and convey a story. Only then would you be able to make informed decisions to increase the ROI. This makes it vital for you to have appropriate data visualization tools at hand to represent data for actionable insights in a single glance. But this can become challenging if your team is not skilled enough to select the right tool for the business requirements.

Data preparation

A major chunk of data scientists’ time goes into making the data accurate and consistent before the final analysis; some even label it as the worst part of their jobs. Hence, daily terabytes of data across multiple formats, sources, functions, and platforms could also be challenging.

Also Check: Who Is A Business Data Analyst?

Measures to Fix Data Science Challenges in 2022

With these challenges at hand, data science enginners or scientists can avail of the full benefits of data analytics with the following measures:

Centralized platform

A centralized platform allows for data integration from multiple sources by converting available datasets into a unified format for meaningful decisions. Furthermore, as the data used is dynamic, you can supplement it with a data strategy and quality management plan.

Secured systems

Using secured systems helps to maintain data confidentiality. Other than this, using methods such as data penetration testing, data encryption, and pseudonymization and privacy policies can help businesses prevent unauthorized access to sensitive information.

Proper workflow inter-department

This involves creating an effective strategy for all organizational departments to collaborate in identifying the business problem. This helps create proper workflow and judicious data analytics for business decisions.

Hire skilled data scientists

A data science project is successful when it equips an organization to convey its business story through data. Therefore, look for data scientists who excel in the art of storytelling through data along with problem-solving skills.

Also Check: The Benefits Of DataOps In Enterprise Data Science and Machine Learning Projects

Well-defined KPIs and metrics

A business should have well-defined metrics to verify the accuracy of the analysis. Also, there should be carefully selected KPIs to assess the impact the analysis made on the business.

Expert help with tools

Seek help from professionals who have an understanding of working in your domain. They would be the ideal sources for selecting the right tools for your business. You can also pre-access these tools by using the trial versions to evaluate their features.

Automating the data collection process

You can automate the data collection process with drop-down fields and data validations, and this eliminates the possibility of human errors. And the problem of asymmetric data can be resolved through system integration. So the changes made at one place would reflect wherever you will use the data.

Start with easy data visualization tools

Start with easy-to-learn data visualization tools like Power BI, Tableau, and Google Data Studio. These tools come with various drag and drop features, intuitive graphs and charts, and many options to help you visualize your data.

Automating the data cleaning and preparation

One can automate the data preparation task before the final analysis using emerging technologies such as augmented analytics and auto feature engineering.

Also Check: How Can Robotics and AI Assistance Help with Fintech and Data Science?

Wrapping Up

Big data is the need of the day, making it imperative for companies to adopt it quickly. However, to ensure success, there’s a need to develop a data science strategy that aligns with the business needs. The lack of defined KPIs and metrics may render the analysis vague. However, data science engineers or scientists can address these challenges effectively with a well-defined workflow, proper strategies, and analytical and technological capabilities.

Other solutions to employ while ensuring a successful data science project includes clear objectives, defining business use cases to be solved, evaluating in-house capabilities, using third-party support where necessary, and many more.

Finally, you can start using the solutions by knowing the most common challenges data science enginners or data scientists face and how to fix them. Once you do, you will realize that no challenge is big enough to limit the capability of big data.

Also Check: 7 Machine Learning And Data Science Startup Ideas

Disha Prakash

Author’s Bio: Disha Prakash is a writer with around eight years of experience writing in diverse domains. Besides, she holds a few research papers in computer vision and image processing published in international publications. She loves to read books, do yoga, and meditate in her free time.

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