Top Challenges in Data Science Today and How to Overcome Them
Data science domain is changing rapidly and is complex to deal with. Thus, it provides its own challenges and issues to deal with. Solving these problems requires skills which can be gained through free online data science courses.
Several free or paid data science certification courses have made it easier to upskill. However, some practical challenges still remain. By reading this article, you will explore three top data science challenges and their solutions.
Data science is transforming industries, but it also has some challenges. Professionals must overcome these challenges to use data science to its maximum potential. Now, in this part of the article, I will mention three main challenges in data science. I'm also going to tell you about their solutions.
Do you know that in 2020, we generated 64.2 ZB of data, more than the number of detectable stars in the cosmos? Not only this, but experts predict that these figures will continue to rise. By the end of 2024, we are expected to generate 14 ZB data.
Hence, we generate a huge amount of data daily. Organisations often find it challenging to manage and efficiently process such big datasets. Many conventional tools are insufficient to deal with such datasets, which are larger than terabytes or petabytes. This results in bottlenecks and inefficiencies.
Another challenge is processing and getting insights from such huge datasets on time. This process requires scalable infrastructure and mechanisms.
To solve this problem, companies should use distributed computing frameworks like Apache Spark and Hadoop. These platforms efficiently handle big data by breaking huge datasets into smaller chunks. Then, these are processed in parallel across many nodes.
Apache Spark has its in-memory processing capabilities. This allows it to deliver faster results. On the other hand, Hadoop has robust data storage because of its HDFS (Hadoop Distributed File System). Therefore, companies can better scale their data processing and analysis on time by using these frameworks.
The second most common data science challenge is poor data quality and integrity. This issue can derail or delay even the most advanced analytics projects. Why so? Because missing values, duplicate entries, and inconsistent formats lead to false predictions. This, in turn, generates wrong insights, which can hamper the project.
If companies fail to ensure data quality and integrity, then their decisions will be based on unreliable information. It will further impact their reputation and trust among the public. It can also lead a company to face legal or regulatory challenges.
The solution to the second problem is ensuring robust data cleaning and validation pipelines. These two things are foremost important to maintain data quality and integrity. Companies use tools like Pandas to handle these issues. It is a Python library that allows efficient manipulation and cleaning of structured data.
Another thing is using automated ETL (Extract, Transform, Load) processes to streamline these workflows. ETL tools automate repetitive tasks such as removing duplicates and standardising formats. Moreover, real-time data validation systems can be used to prevent errors even before they occur. These kinds of systems flag errors at the source. Thus saving time and resources.
The third challenge in data science is bridging the talent and skills gap. The demand for data science is growing, but there's also a shortage of skilled professionals. Many organisations find it difficult to find candidates who have both technical and domain-specific expertise.
This mismatch in skills is evident during placement in colleges and universities. Beginner-level professionals find it challenging to crack the interview process. The education industry has not been able to cope with the demands of the dynamic data science industry. This gap can slow innovation and limit the impact of data science in the long run.
Cross-functional collaboration among different teams and departments should be promoted to address the talent gap challenge. Diverse teams should be created wherein domain experts will work with data scientists. This can significantly reduce the gap between technical and industry-specific knowledge.
Additionally, AutoML (Automated Machine Learning) tools like H2O.ai and Google AutoML should be adopted in the industry. It will allow non-technical stakeholders to contribute to data science projects. That, too, without using extensive programming skills.
Moreover, businesses should also invest in upskilling programs for their existing employees. Companies should encourage employees to enrol in online data science courses if some employees find it challenging to join offline courses. These courses will allow them to learn crucial skills in machine learning, data visualisation, and statistical modelling.
Trusted and reputed platforms like Pickl.AI offer online data science courses which allow you to learn at your own pace. If you are worried about the cost or huge tuition fees, the institution also provides free data science courses to learn without worrying about financial constraints.
Data science is a booming industry and affects every aspect of our lives. However, there are several challenges which come across while implementing it. It is essential to solve these issues to unlock the true potential of data science.
Professionals should embrace diverse tools and techniques to solve such challenges. Moreover, professionals should enrol in online data science courses to upskill themselves for flexible learning. For those worried about cost issues, several platforms like Pickl.AI offer free online data science courses for beginners and professionals.
TIME BUSINESS NEWS
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Forbes
18-06-2025
- Forbes
Data products and services are playing a new role in business.
Data mechanics isn't an industry. Discussion in this space tends to gravitate around the notion of 'data science' as a more pleasing umbrella term. Perhaps borrowing half its name from the core practice of computer science (the label usually put on university studies designed to qualify software application developers who want to program), there is a constant push for development in the data mechanics and management space, even though we're now well over half a century on from arrival of the first database systems. Although data mechanics may not be an industry, it is (in various forms) a company. Data-centric cloud platform company NetApp acquired Data Mechanics back in 2021. Known as a managed platform provider for big data processing and cloud analytics, NetApp wanted Data Mechanics to capitalize on the growing interest in Apache Spark, the open source distributed processing system for big data workloads. But the story doesn't end there, NetApp sold off some of its acquisitions that work at this end of the data mechanics space to Flexera, which makes some sense as NetApp is known for its storage competencies and as the intelligent data infrastructure company, after all. Interestingly, NetApp confirmed that the divestiture of technologies at this level will often leave a residual amount of software engineering competencies (if not perhaps intellectual property in some organizations on occasions) within the teams that it still operates, so these actions have two sides to them. NetApp is now turning its focus to expanding its work with some major technology partners to provide data engineering resources for the burgeoning AI industry. This means it is working with Nvidia on its AI Data Platform reference design via the NetApp AIPod service to (the companies both hope) accelerate enterprise adoption of agentic AI. It is also now offering NetApp AIPod Mini with Intel, a joint technology designed to streamline enterprise adoption of AI inferencing - and that data for AI thought is fundamental. If there's one very strong theme surfacing in data mechanics right now, it's simple to highlight - the industry says: okay you've got data, but does your data work well for AI? As we know, AI is only as smart as what you tell it, so nobody wants garbage in, garbage out. This theme won't be going away this year and it will be explained and clarified by organizations, foundations, evangelists and community groups spanning every sub-discipline of IT from DevOps specialists to databases to ERP vendors and everybody in between. Operating as an independent business unit of Hitachi, Pentaho calls it 'data fitness' for the age of AI. The company is now focusing on expanding the capabilities of its Pentaho Data Catalog for this precise use. Essentially a data operations management service, this technology helps data scientists and developers know what and where their data is. It also helps monitor, classify and control data for analytics and compliance. "The need for strong data foundations has never been higher and customers are looking for help across a whole range of issues. They want to improve the organization of data for operations and AI. They need better visibility into the 'what and where' of data's lifecycle for quality, trust and regulations. They also want to use automation to scale management with data while also increasing time to value," said Kunju Kashalikar, product management executive at Pentaho. There's a sense of the industry wanting to provide back-end automations that shoulder the heavy infrastructure burdens associated with data wrangling on the data mechanic's workshop floor. Because organizations are now using a mix of datasets, (some custom-curated, some licenced, some anonymized, some just plain old data) they will want to know which ones they can trust at what level for different use cases. Pentaho's Kashalikar suggests that those factors are what the company's platform has been aligned for. He points to its ability to now offer machine learning enhancements for data classification (that can also cope with unstructured data) designed to improve the ability to automate and scale how data is managed for expanding data ecosystems. These tools also offer integration with model governance controls, this increases visibility into how and where models are accessing data for both appropriate use and proactive governance. The data mechanics (or data science) industry tends to use industrial factory terminology throughout its nomenclature. The idea of the data pipeline is intended to convey the 'journey' for data that starts its life in a raw and unclassified state, where it might be unstructured. The pipeline progresses through various filters that might include categorization and analytics. It might be coupled with another data pipeline in some form of join, or some of it may be threaded and channelled elsewhere. Ultimately, the data pipe reaches its endpoint, which might be an application, another data service or some form of machine-based data ingestion point. Technology vendors who lean on this term are fond of laying claim to so-called end-to-end data pipelines, it is meant to convey breadth and span. Proving that this part of the industry is far from done or static, data platform company Databricks has open sourced its core declarative extract, transform and load framework as Apache Spark Declarative Pipelines. Databricks CTO Matei Zaharia says that Spark Declarative Pipelines tackles one of the biggest challenges in data engineering, making it easy for data engineers to build and run reliable data pipelines that scale. He said end-to-end too, obviously. Spark Declarative Pipelines provide a route to defining data pipelines for both batch (i.e. overnight) and streaming ETL workloads across any Apache Spark-supported data source. That means data sources including cloud storage, message buses, change data feeds and external systems. Zaharia calls it a 'battle-tested declarative framework' for building data pipelines that works well on complex pipeline authoring, manual operations overhead and siloed batch or streaming jobs. 'Declarative pipelines hide the complexity of modern data engineering under a simple, intuitive programming model. As an engineering manager, I love the fact that my engineers can focus on what matters most to the business. It's exciting to see this level of innovation now being open sourced, making it more accessible,' said Jian Zhou, senior engineering manager for Navy Federal Credit Union. A large part of the total data mechanization process is unsurprisingly focused on AI and the way we handle large language models and the data they churn. What this could mean for data mechanics is not just new toolsets, but new workflow methodologies that treat data differently. This is the view of Ken Exner, chief product officer at search and operational intelligence company Elastic. 'What IT teams need to do to prepare data for use by an LLM is focus on the retrieval and relevance problem, not the formatting problem. That's not where the real challenge lies,' said Exner. 'LLMs are already better at interpreting raw, unstructured data than any ETL or pipeline tool. The key is getting the right private data to LLMs, at the right time… and in a way that preserves context. This goes far beyond data pipelines and traditional ETL, it requires a system that can handle both structured and unstructured data, understands real-time context, respects user permissions, and enforces enterprise-grade security. It's one that makes internal data discoverable and usable – not just clean.' For Exner, this is how organizations will successfully be able to grease the data mechanics needed to make generative AI happen. It by unlocking the value of the mountains of (often siloed) private data that they already own, that's scattered across dozens (spoiler alert, it's actually often hundreds) of enterprise software systems. As noted here, many of the mechanics playing out in data mechanics are aligned to the popularization of what the industry now agrees to call a data product. As data now becomes a more tangible 'thing' in enterprise technology alongside servers, applications and maybe even keyboards, we can consider its use as more than just information; it has become a working component on the factor floor.
Forbes
30-05-2025
- Forbes
Securing The Future: How Big Data Can Solve The Data Privacy Paradox
Shinoy Vengaramkode Bhaskaran, Senior Big Data Engineering Manager, Zoom Communications Inc. As businesses continue to harness Big Data to drive innovation, customer engagement and operational efficiency, they increasingly find themselves walking a tightrope between data utility and user privacy. With regulations such as GDPR, CCPA and HIPAA tightening the screws on compliance, protecting sensitive data has never been more crucial. Yet, Big Data—often perceived as a security risk—may actually be the most powerful tool we have to solve the data privacy paradox. Modern enterprises are drowning in data. From IoT sensors and smart devices to social media streams and transactional logs, the information influx is relentless. The '3 Vs' of Big Data—volume, velocity and variety—underscore its complexity, but another 'V' is increasingly crucial: vulnerability. The cost of cyber breaches, data leaks and unauthorized access events is rising in tandem with the growth of data pipelines. High-profile failures, as we've seen at Equifax, have shown that privacy isn't just a compliance issue; it's a boardroom-level risk. Teams can wield the same technologies used to gather and process petabytes of consumer behavior to protect that information. Big Data engineering, when approached strategically, becomes a core enabler of robust data privacy and security. Here's how: Big Data architectures allow for precise access management at scale. By implementing RBAC at the data layer, enterprises can ensure that only authorized personnel access sensitive information. Technologies such as Apache Ranger or AWS IAM integrate seamlessly with Hadoop, Spark and cloud-native platforms to enforce fine-grained access control. This is not just a technical best practice; it's a regulatory mandate. GDPR's data minimization principle demands access restrictions that Big Data can operationalize effectively. Distributed data systems, by design, traverse multiple nodes and platforms. Without encryption in transit and at rest, they become ripe targets. Big Data platforms like Hadoop and Apache Kafka now support built-in encryption mechanisms. Moreover, data tokenization or de-identification allows sensitive information (like PII or health records) to be replaced with non-sensitive surrogates, reducing risk without compromising analytics. As outlined in my book, Hands-On Big Data Engineering, combining encryption with identity-aware proxies is critical for protecting data integrity in real-time ingestion and stream processing pipelines. You can't protect what you can't track. Metadata management tools integrated into Big Data ecosystems provide data lineage tracing, enabling organizations to know precisely where data originates, how it's transformed and who has accessed it. This visibility not only helps in audits but also strengthens anomaly detection. With AI-infused lineage tracking, teams can identify deviations in data flow indicative of malicious activity or unintentional exposure. Machine learning and real-time data processing frameworks like Apache Flink or Spark Streaming are useful not only for business intelligence but also for security analytics. These tools can detect unusual access patterns, fraud attempts, or insider threats with millisecond latency. For instance, a global bank implementing real-time fraud detection used Big Data to correlate millions of transaction streams, identifying anomalies faster than traditional rule-based systems could react. Compliance frameworks are ever-evolving. Big Data platforms now include built-in auditability, enabling automatic checks against regulatory policies. Continuous Integration and Continuous Delivery (CI/CD) for data pipelines allows for integrated validation layers that ensure data usage complies with privacy laws from ingestion to archival. Apache Airflow, for example, can orchestrate data workflows while embedding compliance checks as part of the DAGs (Directed Acyclic Graphs) used in pipeline scheduling. Moving data to centralized systems can increase exposure in sectors like healthcare and finance. Edge analytics, supported by Big Data frameworks, enables processing at the source. Companies can train AI models on-device with federated learning, keeping sensitive data decentralized and secure. This architecture minimizes data movement, lowers breach risk and aligns with the privacy-by-design principles found in most global data regulations. While Big Data engineering offers formidable tools to fortify security, we cannot ignore the ethical dimension. Bias in AI algorithms, lack of transparency in automated decisions and opaque data brokerage practices all risk undermining trust. Thankfully, Big Data doesn't have to be a liability to privacy and security. In fact, with the right architectural frameworks, governance models and cultural mindset, it can become your organization's strongest defense. Are you using Big Data to shield your future, or expose it? As we continue to innovate in an age of AI-powered insights and decentralized systems, let's not forget that data privacy is more than just protection; it's a promise to the people we serve. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Business Wire
14-05-2025
- Business Wire
Informatica Expands Partnership with Databricks to Help Customers Migrate to a Modern, AI-Powered Cloud Data Management Platform
REDWOOD CITY, Calif.--(BUSINESS WIRE)--Informatica (NYSE: INFA), an AI-powered enterprise cloud data management leader, today announced an expanded partnership with Databricks, the data and AI company, that enables customers to modernize their on-premises, Hadoop-based data lakes with a powerful combination of Informatica's Intelligent Data Management Cloud™ platform and the Databricks Data Intelligence Platform, providing a solid foundation for analytics and AI workloads. The announcement was made today at Informatica World in Las Vegas. Informatica also announced its CLAIRE Modernization Agent, a first-of-its-kind AI Agent to automate the migration of on-premises Data Engineering workloads to Informatica's Cloud Data Management platform. This is a key milestone that enables Informatica Big Data Engineering and Data Engineering and Integration customers to seamlessly transition from expensive and complex Hadoop-based data lakes and modernize to Informatica's platform and the Databricks Data Intelligence Platform. Adam Conway, Senior Vice President of Products at Databricks, said, 'Enterprises today are looking to build data intelligence — AI that reasons on their data. But with such vast amounts of data, managing and migrating it efficiently is a growing challenge. Our continued partnership with Informatica brings the power of GenAI agents to streamline migrations, boost data productivity and drive faster business results.' Key Benefits of the Informatica CLAIRE Modernization Agent Include: AI-powered, self-service automation to assess and convert Data Integration Engineering workloads to the Informatica Intelligent Data Management Cloud platform, eliminating risky manual migrations. The Informatica Intelligent Data Management Cloud platform provides deep and native cloud ecosystem integration, including support for new integration patterns, new file/open table formats, data types and transformations. Repointing of workloads from Hadoop-based compute and data lakes to modern cloud-based compute and data platforms. Maximum reuse of existing workloads and business logic provides faster time-to-value. Cloud-managed security/patch updates lower operational costs. 'We're excited to partner with Databricks for the launch of our CLAIRE Modernization Agent and program as we continue to expand our automated tools to help our customers modernize to the cloud,' said Krish Vitaldevara, EVP, Chief Product Officer at Informatica. 'The data lake workloads targeted are some of the largest, most complex Hadoop-based data lakes out there. This is another example of how the Informatica Intelligent Data Management Cloud platform is helping customers move to the cloud, and we are pleased to align with Databricks on this initiative.' Availability The CLAIRE Modernization Agent and modernization program is currently expected to be available in the fall of 2025. For more information, visit About Informatica Informatica (NYSE: INFA), a leader in AI-powered enterprise cloud data management, helps businesses unlock the full value of their data and AI. As data grows in complexity and volume, only Informatica's Intelligent Data Management Cloud™ delivers a complete, end-to-end platform with a suite of industry-leading, integrated solutions to connect, manage and unify data across any cloud, hybrid or multi-cloud environment. Powered by CLAIRE® AI, Informatica's platform integrates natively with all major cloud providers, data warehouses and analytics tools— giving organizations the freedom of choice, avoiding vendor lock-in and delivering better ROI by enabling access governed data, simplify operations and scale with confidence. Trusted by 5,000+ customers in nearly 100 countries—including over 80 of the Fortune 100—Informatica is the backbone of platform-agnostic, cloud data-driven transformation.
