Big Data

Most Common Challenges of Big Data Analytics for Businesses

Big Data Analytics

Data has become one of the most valuable assets in the world today. The economics of data is based on the idea that data value can be extracted through analytics. While Big Data and analytics are still in their early stages of growth, their importance cannot be understated. As Big Data continues to expand and grow, the importance of big data analytics will continue to increase in personal and business lives. Moreover, the amount and size of data are growing rapidly, making it essential to address the complexities of Big Data analytics.

With great potential and opportunities come significant challenges and hurdles. Companies must effectively address these challenges to unlock the full potential of big data analytics and gain a competitive advantage. When data analytics challenges are addressed in an organized and efficient manner, the success rate of implementing big data solutions increases significantly. Furthermore, addressing these challenges is essential as big data solutions become increasingly integrated into companies worldwide. Partnering with IT Consulting Los Angeles experts will help you to address these challenges efficiently.

This article will explore the most common challenges to big data analytics for businesses.

1. Lack of Knowledgeable Professionals

Most companies require highly-skilled data professionals to manage the latest technologies and extensive data tools. These experts include data scientists, analysts, and engineers who can make sense of massive data sets. However, one of the most significant challenges companies face is needing more capable data professionals. This is often because data handling tools have advanced rapidly, yet professionals still need to keep pace. To bridge this gap, engage IT Support San Diego professionals and stay up-to-date with the latest technologies.

However, companies are now investing more money in recruiting skilled professionals and training programs for the existing staff to get the most out of them. Additionally, many organizations are now turning to knowledge analytics solutions powered by artificial intelligence. These Data Tools can be implemented even by professionals who are not data science experts but have basic knowledge. It helps businesses to save a considerable amount of money on recruitment costs.

2. Inaccurate Analytics

There is nothing more devastating to businesses than relying on inaccurate analytics. To ensure good results, it is essential to implement a data management system and a comprehensive data validation process that covers every step of your ETL process. This will help you to detect errors and ensure that any changes in one area are immediately reflected across the board, resulting in clean and accurate data. Doing so will ensure your analytics’ accuracy and promote your business’s success.

In addition, system defects in data flow can occur when the system’s requirements still need to be fully met or omitted due to human error in development, testing, or verification procedure. Therefore, performing high-quality testing and verification during development is essential to reduce such problems. However, even with top-quality data, it is possible to receive inaccurate analytics results. In such cases, it is necessary to inspect the system and verify that data processing algorithms are correctly implemented.

3. Security and Privacy of Data

Once businesses discover the potential of Big Data, they can open up a world of possibilities and opportunities. However, it also brings with it the potential risk of data privacy and security. With Big Data tools used for analysis and storage, data from disparate sources is collected, increasing the risk of data exposure and making it vulnerable. This rise of voluminous data further magnifies privacy and security concerns. To ensure the safety of their data, businesses must take the necessary steps to protect and secure their data.

To effectively address the Big Data challenges faced by companies, business owners should organize a comprehensive corporate training program in Big Data. Such a program should not only focus on the technical aspects of Big Data but also equip participants with the necessary skills and knowledge to manage, analyze, and interpret Big Data effectively. 

4. Data Growth Issues

One of the most significant of Big Data issues is storing massive amounts of data correctly. The amount of unstructured data accumulated is growing exponentially with the rapid growth of data collection and storage in data centers. This data comes from various sources, such as documents, videos, and more, and is not easily searchable in databases. As a result, big Data analytics could only be improved with proper storage and significantly delay a company’s growth. Consequently, it is essential to find solutions for these Big Data storage challenges as soon as possible.

In order to handle large volumes of data sets efficiently and cost-effectively, many companies are turning to modern techniques such as compression, tiering, and deduplication. Reduction reduces the number of bits in the data, resulting in smaller file sizes. Deduplication eliminates duplicate and unnecessary data from the data set, allowing for greater storage capacity. Finally, data tiering will enable companies to keep data in different storage tiers, choosing the best storage space based on size and importance. These tiers typically include public cloud, and private cloud.

5. Long System Response Time

When the system takes too long to analyze the data, even though the data is available, the report is required now. Such delay can be costly for real-time systems, where time is of the essence. In addition, your data may be organized in such a way that makes it challenging to work with. Therefore, checking if your data warehouse is correctly designed to meet your current use cases is essential. If not, re-engineering can reduce the time it takes to analyze your data and generate the report you need.

Problems of big data analytics and resource utilization can occur when your hardware infrastructure needs to be improved. The easiest solution here is upscaling, i.e., adding more computing resources to your system. It’s good if it helps improve your system within a reasonable budget and the resources are adequately utilized. However, a wise approach from a strategic viewpoint would be to split the system into separate components and scale them independently.


Companies are facing many challenges in big data analytics. While these challenges may be daunting, addressing them effectively is essential. Business analytics can significantly increase a company’s competitive edge, prevent fraud, help retain customers, and anticipate future business demands. In the last decade, big data has advanced considerably, and overcoming these challenges will be a fundamental goal of the big data analytics industry in the coming years.

Post courtesy: Steven Truong, Marketing Coordinator at I.T. Responsive

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