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Top 5 Challenges Of Data Science Technology

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In a single day, 2.5 quintillion bytes of data are generated online. This is important for several reasons. The first and foremost is to support business decision-making. Other benefits include monitoring the health of an organisation, finding solutions to complex problems, creating benchmarks and baselines, ensuring evidence-based practices and helping you be strategic in your approaches. When business outcomes are data-driven, it boosts revenue and establishes a brand’s leadership position in its industry.

You can consider the Data Science Architect Program offered by GeekLurn to be able to understand how data works and build a career in this field. The course includes 320+ hours of interactive live sessions, a research project scholarship up to ₹2 lakhs and a 100% placement guarantee. One-on-one career mentorship, easy financing option via EMIs, a cohesive learning network and live workshops and webinars make it an excellent course for both beginners and experienced professionals. At the end of it, you will be eligible for jobs roles like data analyst, data scientist, data architect, research analyst and machine learning engineer.

However, to be successful in this field, there are a few data science challenges you need to be aware of. It will help you address them with various tools and techniques.

1. Multiple data sources 

Collecting information from unstructured and structured datasets is a complex process. It becomes all the more difficult since businesses now use heterogeneous applications like CRMs and ERPs to manage data. So, data scientists may have to perform manual data entry and data searching, which is quite time-consuming. These further lead to unreliable decision-making and irreversible mistakes. You can start looking for a centralised platform that allows data integration from multiple sources. This will save plenty of effort and costly errors, and you will finally be able to run the algorithms without hiccups.

2. No clarity on business issues

Getting a crystal-clear picture of business complexities is necessary for effective problem solving. It helps data scientists craft a workflow and ensure proper problem identification. Otherwise, it becomes difficult to build solutions and create a mechanical approach to get started with analysing data sets and defining business objectives and problems. So, data scientists must make it a point to collaborate with stakeholders and make a well-defined checklist. Data science implementation will then be more aligned with the business’ end goals.

3. Data Engineer collaborations

The roles and responsibilities of data scientists and engineers overlap, yet each one has different workflows and priorities. This may often become a cause of misunderstanding and a data science challenge, which will ultimately hamper overall productivity and efficiency. Further, data engineers might be given tasks that are easier said than done, while data scientists may have to work with information that does not match their expectations. In short, it can impair the process of blending maths with machine learning, business tools and algorithms, eventually impacting business strategies. 

Data scientists must ask their organisations to ensure a robust collaborative process. This can be done with real-time tools and improved communication. You can also set up a common coding language to ensure minimal issues between the two teams.

4. Undefined KPIs and Metrics 

Business metrics can be of multiple types, depending on the industry. Each small or large business needs proper KPIs and defined metrics to measure the accuracy of analyses and reports generated by data scientists, otherwise, it becomes extremely difficult to boost business growth.



Business Metrics Usage
Tracks insightsEvaluates performancesCompares different results


Metrics to Remember
Number of production deploymentsReusable artefactsSolid vision and goalsReturn on Investment


Learning data science from a well-established organisation like GeekLurn will help you gain a keen understanding of other practices that are mainly a mix of KPIs and metrics. It will save time, expense and effort, and help take appropriate operational and strategic actions.

5. Data Security 

This is one of the key problems with data science due to rampant malicious attacks on data systems. In the first 3 months of 2022 alone, India witnessed more than 18 million threats and cyber-attacks at an average of about 200,000 each day. In fact, our country ranks 4th in the list of countries that are victims of cybercrime. Sensitive data travelling over a network is a cause for concern. So, companies need to keep the three elements of security – integrity, confidentiality and accessibility – in mind. Data scientists, on the other hand, can use methods like deep penetration, privacy policies and data encryption for high-end protection.

The bottom line:

Data science problems can be in plenty, just like any other discipline. But, there are approaches, such as those discussed above, that can help you overcome them. Most industries today depend on data scientists to improve their corporate performance and make critical decisions. Knowing the tips and tricks to reduce the intensity of the issues can help in lowering time-consuming audits and costly fines.

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.

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Neel Neeraj

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.
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