5 Common Data Science Challenges and Effective Solutions

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Data science is the process of studying data to derive useful insights for decision-making. It covers everything from statistics and mathematics to artificial intelligence and computer engineering.

As important as data science is, several obstacles make it difficult for businesses to unleash its full potential. In this article, you’ll learn five main data science challenges you need to overcome to get the most out of data analytics and enhance business decision-making.

1. Handling Multiple Data Sources

Getting the right data for analysis is a daunting task, especially when you’re accessing data from various sources. That’s why, for effective data science, consolidating data from multiple sources is a must.

However, consolidating data from varying and semi-structured sources is a complex and time-consuming process.

A quick solution to this data science challenge is to use data integration tools or a data management system such as Informatica and Oracle. These software solutions will help you collect and aggregate data from various sources and filter it for ease of access.

They do this by acting as a centralized platform that integrates with the sources of the data. The result is that you gain a holistic view of all your data, allowing you to generate more accurate and meaningful insights.

You can also use business AI solutions to quickly analyze data and suggest helpful business decisions. While there are generative AI risks like AI hallucinations, these can be easily overcome with countermeasures such as fact-checking.

2. Not Enough Skilled Workers

The world is increasingly becoming dependent on data science for decision-making. A staggering 59% of businesses use data science in different ways to improve their performance. This has resulted in a high demand for skilled data science professionals that outweighs supply. Think about this: there are three times the number of data science job postings than there are job searches.

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But that’s not all. Even some of the existing data scientists don’t have the upgraded skills needed to handle data in the modern world. The traditional way of working with data is no longer applicable in today’s environment because of emerging technologies like generative AI. Then, there are two other developments that merit an upskilling or reskilling of data professionals: the explosion of data and advancement in compute capacity.

The upskilling and reskilling of existing data science experts aren’t limited to technical skills. Data science experts also need enhanced problem-solving and communication skills. With the massive amount of data now available come new challenges and problems that need to be addressed.

The solutions to these problems need to be properly communicated to team members and management, who may or may not have the expertise to interpret data on their own. We’ll explore this in more detail later.

To address the challenge of a smaller pool of data scientists relative to demand, you just need to stand out as a potential employer and attract some of those professionals who are part of that pool. So, offer competitive salaries and benefits. The average base pay for data scientists in the US is $146,422, according to Glassdoor, and if you can offer more, better.

Whether you hire data scientists or already have data professionals as employees, you need to invest in data science workshops and training. These can help ensure your team’s data science skills are attuned to the times and consider current practices and standards in the data science industry.

3. Data Privacy and Security

The transition to cloud environments has contributed to the increase in data security breaches in the 21st century. It’s estimated that 60% of corporate data is stored in the cloud. In 2020 alone, the FBI received over 2,000 cybercrime complaints daily. Ransomware, attacks on data systems, and data theft are some common forms of data security breaches.

As a result, businesses now employ cybersecurity experts, including ethical hackers who use ChatGPT for hacking, to ensure their client data remains secure. This ethical hacking helps them identify potential data security risks and fix the problem in advance.

With so much data that can fall into the wrong hands, entities such as the European Union have also taken action.

The General Data Protection Regulation, for instance, which took effect in 2018, aims to protect the data of people in the EU. It levies penalties and fines that can reach in the millions of euros on organizations that violate the GDPR’s privacy and security standards.

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As a business, then, you have to ensure the security and privacy, not just of your company but also of your consumers.

To effectively protect this data, you first need to know what data you have and where it’s currently located, a process called data discovery. You can use automated data discovery tools like Tableau and IBM Cognos Analytics to quickly identify the sensitive data you have.

Then, choose a reliable data storage solution to act as an additional layer of security. In addition, always back up your data so you can easily retrieve it in case of loss or corruption.

Make sure you have granular access controls. Whatever the nature of your business, it doesn’t really make sense to give everyone the same access control.

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Consider a software company as an example. The data the finance team needs for their daily operations would be very different from what the marketing department needs to execute their SaaS marketing strategies. Similarly, the sales team and customer support departments would need different sets of data to perform.

More importantly, granular access controls will prevent unauthorized access and reduce the risk of infringing on your customers’ data privacy and security. This is essential because organizations and data experts need to balance between keeping clients’ confidential data private while sharing the necessary data sets with relevant team members. Consider using a data catalog to help you restrict sensitive data while granting data experts the access they need to relevant datasets.

4. Data Cleansing

Removing unwanted data from your datasets is one of the key challenges you’ll face. Bad data is costly to businesses, with some losing up to $12.1 million yearly because of it. It’s every data scientist’s nightmare to work with data that is inaccurate, duplicated, inconsistent, or inappropriate. It can lead to incorrect conclusions, resulting in wrong decisions.

As a business, it is essential to know the four Vs of big data to help you with data cleansing. They include:

• Velocity - This is the speed at which data is transferred. Since the transfer happens in real-time, you need to analyze these datasets in real-time as well.
• Veracity - You need to choose the data that is relevant to your business so people know they can trust the decisions that result from it.
• Volume - Data exchange is growing greatly by the day. This means that you’ll need to use technology to help you cope with it.
• Variety - There are many forms of data you will encounter, including structured, unstructured, and semi-structured data. It is essential to set a standardized format to help you with data variety.

Considering the vast volumes and variety of data that you need to work on, having to cleanse inconsistent data can take you hours to complete.

Consider using data governance as a way to solve this data science issue. This refers to the procedures set by a company to manage its data assets. There are modern data governance tools that will help you cleanse, format, and maintain the accuracy of your datasets. IBM Data Governance, OvalEdge, and Collibra are good examples of data governance tools.

Additionally, employ data professionals whose job will be to look after the data quality in every department. That will help you get high-quality datasets to work on while saving time and money.

5. Reporting to Non-Technical Stakeholders

Increasing the capacity of an organization to make informed decisions is a major objective of data science. These decisions should be aligned with the company’s business plan. That’s the only way the business can achieve its business goals.

We briefly mentioned this a while ago. Since data science is a highly technical field, it can be challenging to communicate the findings of data scientists to managers and business executives who don’t speak the technical language. Many managers and organizational leaders are unfamiliar with the tools and machine learning models used in data science.

Then, there’s the fact that some organizations don’t have clearly defined business terms and KPIs. That can be a challenge for your data scientists when it comes to reporting. If each department interprets business terms differently and uses different measures to calculate KPIs, then your data scientists will have a lot to do.

They will have to explain the impact of their work as it relates to the specific KPIs of each department. As a result, it might be difficult to come up with a holistic business decision that will redound to the benefit of each department.

The solution to these major challenges? We mentioned one: to reskill and upskill your data scientists so they can hone their communication skills. You can train them in data storytelling for their audience’s effective visualization of findings. Data storytelling ensures data analysis is easily understandable. It can be used to convince the audience of why the business decision arrived at is fitting.

Another solution is to give non-technical personnel–the data scientist’s audience—a good foundation in data science.

You should also define your organization’s KPIs clearly and ensure all departments have a common understanding of each business term. This makes it easier for data scientists to communicate key insights from their analysis.

One way you can ensure this consistency is, again, by using a data catalog. It acts as a single source of truth for your business terms and KPIs, ensuring everyone has the same interpretation of what they mean.

Conclusion

To wrap up, many data science challenges keep emerging as businesses continuously adopt technology to get things done. Multiple or unreliable data sources make it difficult for data scientists to extract actionable insights from large amounts of data. There is also a talent gap that makes it difficult to find skilled data science experts with hands-on experience.

Data privacy and security concerns continue to make it challenging for businesses to access the data they need to analyze. Data cleansing takes lots of time and money as organizations try to identify and discard bad data. Finally, it can be difficult to report to non-technical stakeholders since data science is a technical field.

To solve these data science challenges, offer competitive salaries to attract modern data scientists from a seemingly small talent pool relative to demand. Upskill and reskill your data professionals so they can keep up with the changing technologies and emerging data science demands. Train your other employees so they have a basic understanding of data science. Also, consider using tools like data catalogs and data governance software as well.

Follow these tips and you’ll unleash the full potential of data science for your business and uncover exciting opportunities.