6 Data Science Challenges in 2021 and How to Address Them

data science challenges

Data has become the new fuel for businesses. It is now an integral part of all the decision-making processes. Today, most industries are resorting to data and analytics to underscore their brand’s position on the market and increase revenue.

As the adoption of analytics methods like data science and big data analytics has increased, so have the challenges in data science that come with it. Most DS (data science) issues are not company-specific. These challenges may include finding the right talent or solving basic issues revolving around getting the raw data organized, unknown security vulnerabilities, and more.

In this blog post, we will discuss some of the key data science challenges in 2021 and solutions to address them.

1. Multiple Data Sources

Companies have started using various software and mobile applications like ERPs and CRMs to collect and manage information related to their customers, sales or employees. Data consolidation from disparate, unstructured or semi-structured information can be a complex process. This leads to non-uniformed formats as each of the tools collect information in their own ways. Moreover, this also means that there are a variety of sources to handle and extract data from.

Heterogeneous sources often make it difficult for data scientists to understand and gather meaningful insights. Hence, they end up spending more time on filtering it, which leads to errors and unreliable decision-making. In such cases, it is crucial to standardize data for accurate analysis. To have an understanding about what format to use for DS, you need to have insights on the essentials of big data. Therefore, it is important to know the 4 Vs of big data:

  • Volume: people often ask, is big data a problem? No, it’s not. Even with the data exchange growing exponentially, one can handle it with the help of technology. You’ll just have to find the right technology vendor to help you cope with it.
  • Velocity: with volume, the speed at which the information is transferred, also matters. The exchange happens in real-time. So, it is essential to analyze these data sets in real-time, too.
  • Variety: data comes in all shapes and sizes. They can be structured, unstructured or semi-structured. As discussed above, setting a standardized format is a perfect way to handle the variety of data.
  • Veracity: people ask how much can your data be trusted? Before starting big analysis, it is crucial to choose the right data relevant to your business case.

In addition to this, another solution to this problem is to list the data sources that a company uses and look for a centralized platform that allows integrating data from those sources. Next step is to create a data strategy and quality management plan as the data gathered from these sources will be dynamic. Prioritizing and integrating datasets in a centralized system saves time and effort as well as it helps in aggregating data at a single location in real-time. This ultimately helps in running algorithms efficiently.

2. Data Security

Data science in business is used to identify business opportunities, improve overall business performance and drive savvy decision-making. However, data security remains one of the top issues in data science that concerns businesses all over the world. Data security is an umbrella term that includes all security measures and tools applied to analytics and data processes. Few of the data security breaches involve:

  • Attack on data systems
  • Ransomware
  • Theft
86% of breaches in 2020 were financially motivated. – 2020 Verizon data breach investigation report.

Information theft is the most common data security concern, especially for organizations that have access to sensitive data like financial information or customers’ personal information. With the increase in the amount of information exchanged over the Internet, the threat to data travelling over the network has increased exponentially. Hence, companies need to follow the three fundamentals of data security:

  • Confidentiality
  • Integrity
  • Accessibility

data security

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Using secure systems to access and store data is the first step towards ensuring the confidentiality of the accumulated information. With methods like data penetration testing, data encryption and pseudonymization as well as privacy policies, businesses can make sure that their information remains protected. DS services are not designed for granular access. This means only required personnel or team should have access to sensitive information, while the purpose of the data should be determined.

3. Lack of Clarity on Business Problem

First, one should study the business challenge for which you want to implement data science solutions. Opting for the mechanical approach of identifying datasets and performing data analysis before getting a clear picture of what business issue to solve, proves to be less effective. This is especially unsupportive when you are applying DS for effective decision-making. Moreover, even with a clear purpose in mind if your expectation from data science implementation is not aligned with the end-goals, the efforts are futile.

Strategizing a flawless workflow is a winning solution to identify the right use case to solve. To create a workflow, it is important to collaborate with all the departments and design a checklist that enhances problem identification. This helps in identifying a business issue and its effects in a multidisciplinary environment.

4. Undefined KPIs and Metrics

Data scientists can design machine learning models and get accurate results with the help of it. However, there are chances that the metrics used do not serve the purpose of implementing DS. Learning data science includes not only knowing development of algorithms, but also requires a keen understanding of other practices. This consists of a mix of metrics and KPIs that boost business growth.

undefined kpis and metrics

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Some of the methods to identify key metrics are:

  • Clear goal and vision: a realistic goal, articulated enough to bring the success to the project. The goal should be quantifiable and should allow you to track the project’s progress. This helps specialists rectify any errors before it is too late.
  • Reusable artifacts: reusability is a boon. It helps  improve the overall productivity of the DS-based project. Also, if you leverage reusable artifacts, you save a lot of time and gain lucrative benefits. Few of the artifacts that can be re-used include frameworks, open-source software, artificial intelligence models, etc.
  • Number of production deployments: after experimenting and creating the proof of concept, you’d want to deploy your ML models into production. If the models do not perform as expected, there are multiple iterations and modifications required to be done to ensure you get the desired results.  It’s okay if you make small changes in production. This will help you gain insights into the bottlenecks at the end-process in the early stages of production.
  • Delivering actionable insights: a successful DS-based project helps you get actionable insights that include improving processes like inventory, sales, production and others. They should guide you and take fact-based decisions that meet the end goal.
  • Return on Investment (ROI): while investing in DS projects, you’d want to know if the results will maximize your ROI or at least minimize the loss. If the returns from your DS module implementation is not exceeding or at par with your investments – time and cost, then it is better to re-evaluate the entire process.

5. Difficulty in Finding Skilled Data Scientists

Talent shortage is another issue in data science that companies are facing. Businesses often struggle to find the right data team with in-depth knowledge and domain expertise. Along with a deep understanding of ML and AI algorithms, specialists are required to also know about the business perspective of DS. Ultimately, a DS project is successful when it enables organizations to tell their business story through their data. Hence, an important skill to look for in analysts and scientists is the art of storytelling through data, along with problem-solving capabilities.

While not all the departments understand the language of data, the expert team should be able to communicate with other teams, and do it efficiently. As different teams have different priorities and workflows, it is important for all of them to be on the same page. Professionals should be able to explain the technical complexities in a comprehensive way, so business owners can understand them easily. However, to find such a team is difficult. Reaching out to a data science company is a viable option as they not only have the technical expertise required but also understand the business aspect of the project, and are ready to commit to it.

6. Getting Value Out of Data Science

Data experts believe that to support a business, the data analytics process needs to be more agile and in-sync with business during the decision-making process. Implementing DS allows you to build a culture of collaboration amongst team members and most importantly, empowers your employees to make better decisions.

Getting Value Out of Data Science

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DS can be used for various purposes like:

  • Understanding customers
  • Targeting the right customers
  • Improving the quality of products
  • Making teams more effective

Depending on the business case, right datasets as well as robust ML and AI models, you can get abundant value out of your DS project.


In this era of digitalization and big data competition, it becomes necessary for companies to adapt to the changing market needs and develop a data science strategy in accordance with the business needs. When pursuing your analytics goals, professionals can be confronted by various types of DS challenges that hinder your progress. If you follow a well-planned workflow that allows you to strategize your business, analytical and technological capabilities, these problems can be efficiently addressed. Below are the summarized solutions that can help you with successful DS implementation:

  • Create a list of possible initiatives with clear objectives
  • Select a business use case that needs to be solved
  • Analyze in-house capabilities
  • Make a list of tech requirements
  • Seek third-party expertise
  • Prepare a realistic timeline.

A comprehensive plan helps you to tackle data science blues. Also, consulting with data science experts allows you to gain insights, which lead to a successful implementation of the project.

Author Bio:

Ripal Vyas is the Owner of Softweb Solutions Inc – An Avnet Company. Having solid experience in bringing the latest technologies to the Midwest, he is now raising awareness on the importance of IoT, deep learning, AI, advanced data analytics, and digital experiences across the U.S.

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