Top 5 Challenges In Big Data & Analytics

With increasing amounts of data, the challenges associated with analyzing and interpreting is also increasing.

According to a survey, 95% of business owners have taken up a new project related to big data. But, less than half received good, measurable results.

As more and more organizations want to collect, evaluate, and utilize unstructured data, the challenges posed by such a big amount of raw information are too many. Some are not able to receive measurable results, and others are unable to even implement their big data projects effectively.

Hence, looking at the issues faced by many organizations, we have prepared a list of challenges that you may face.

Big Data and Analytics Challenges

1. Storing of Data

The obvious issue faced by organizations is managing unstructured data. Since this data is not structured, it can’t be saved in the database, which means that this data can’t be directly searched or analyzed.

For solving this issue, organizations can use software-defined storage or hyper-converged and converged infrastructure. Further, utilizing technologies such as deduplication, compression, and tiering can decrease costs and space requirements.

2. Timely Insights

Obviously, the end goal is not to store data but utilize it for actionable business insights. They want to make data-driven decisions, reduce cost overheads, innovate, and accelerate operations. And all this is only possible when these organization can ensure speedy results, which is often hard to achieve.

To achieve this, you can use analytics and new-gen ETL to decrease report generation time to a great extent.

3. Integrating Data Sources

Businesses collect data from various sources such as emails, social media, documents, enterprise applications, etc. Compiling all this data and creating actionable insights is extremely difficult. Many vendors offer ETL systems to solve this issue, but many organizations will agree that till now they have not been able to achieve integration successfully.

Hence, many organizations are spending specifically on integration tools made to simplify the problem.

4. Selecting the Right NoSQL Tool

We all know that RDBMS is not the right database structure for storing big data information. Most organizations need to shift to a non-relational SQL system so that they can capture, analyze, and process data. While there are many NoSQL tools available in the market, choosing one is often challenging. Businesses can’t find the right one because every tool has some benefits and shortcoming.

The only solution to this is to clarify your organization’s requirement of information and then, select the right tool.

5. Maintaining Data Security

Isn’t security loopholes the issue of every organization?

It just doesn’t seem to go away. When businesses collect such huge amounts of data from varied sources, security issues may arise. If any of this data is stolen, the company may have to face legal implications too.

Hence, to avoid legal issues, ensure a robust security structure. Reduce chances of data breaches and theft so that your customers’ data stay safe.


Since many organizations are not able to address challenges, they underutilize data. They are unable to put in place a comprehensive strategy that can help them achieve measurable results. Hence, it is essential to ensure the above challenges are identified and removed as suggested for a robust system.

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