Challenges in Big Data Analytics and Some Solutions to Tackle Them

In the data-driven world, business intelligence is in high demand. In fact, 97.2 percent of companies today are investing in big data and AI to drive growth and development.

Despite this, many organizations struggle to effectively use data on a strategic and tactical level. According to Gartner, 87 percent of organizations have low BI and analytics maturity, meaning they’re largely relying on spreadsheet-based management systems while lacking data guidance and support. 

Companies in this position are typically unable to act with the speed and agility that true data-driven competitors are known for. At the same time, lacking these abilities makes it that much harder to make the best decisions—which puts them at a major disadvantage.

Top Challenges in Big Data Analytics

There’s certainly no shortage of data today. In fact, 5 quintillion bytes of data is produced every day across the world. This figure is certain to increase in the coming years as more connected systems and devices come to market. 

This begs the question: Why are so many businesses struggling to use big data when everyone knows how important it is? 

The main overarching problem is that there is simply too much data and too many data sources for most businesses to handle. Big data has created many new challenges in analytics knowledge management and data integration. As a result, many companies need to catch up and modernize their systems to use their data effectively, as the bulk of yesterday’s tools and technologies are outdated and ineffective. 

Believe it or not, less than half of structured data is actively used in business decision-making today. And less than 1 percent of unstructured data is analyzed or used. 

In other words, the vast majority of information is going to waste because companies lack the ability to process, store, and manage all of it. 

This is problematic because challenges in big data analytics create challenges in predictive analytics as well. After all, it’s impossible to glean critical insight about the future with missing or incomplete data sets. 

Here’s a rundown of some of the common challenges that businesses are experiencing with big data analytics.

Challenge: The Data Science Skills Shortage


It’s well-known that there is a skills shortage for data scientists. Closing this gap, however, is proving to be extremely difficult. It’s not just a matter of training people to work with big data solutions, either. Due to a confluence of factors, it’s a gap that could take many years to close. 

“The data science field has an experience shortage,” explains Daniel Zhao, a senior economist at Glassdoor. “There are plenty of recent grads who can throw a hodgepodge of models at a data set, but there’s a serious shortage of experienced and qualified workers who have the full combination of technical skills, business expertise, and domain knowledge.”


Many organizations are reducing the pain of the data science skills gap using automated machine learning (AutoML)—a process that involves automating repetitive tasks. With AutoML, data scientists can use their time to focus on business problems instead of getting bogged down with code. 

AutoML isn’t the complete answer to the data science skills crisis. But it can help analytics teams accomplish more when they lack experienced personnel. 

Challenge: Sharing and Collaboration


For many teams, it can be difficult to share and collaborate on big data analytics projects due to accessibility, security, transparency, and data transfer issues. The problem is even harder for remote teams that need to collaborate over distances—which often leads to data quality issues. 


A secure, centralized, and cloud-based analytics portal that brings all analytics assets in one place makes sharing and collaborating with big data analytics much easier. By taking this approach, teams can prevent large pools of data from going offline or getting altered in transit, and they don’t have to spend anywhere near as much time searching for analytics assets. 

Challenge: Poor Visualization


In many cases, interesting data can get overlooked when it blends together with mundane or irrelevant findings. In other instances, team members—even accomplished data scientists— may simply lack the skills or creativity needed to string together data in a way that is visually pleasing and compelling. 


Data visualization tools like Tableau and Microsoft Power BI can help teams create effective visuals that lead to action. These tools can integrate with different data sources, providing a flexible and powerful way to present and share insights. 

Tableau, for instance, offers a wide range of curated templates that teams can use to create graphics. More skilled analytics professionals can also create their own custom visualizations.

Challenge: Breaking Down Data Silos


Analytics professionals often need to locate data across different departments and applications in what’s often a frustrating and time-consuming process. Disparate systems often do not communicate with one another, making it harder to access key data when it’s needed. 


An easy to use SaaS solution that can provide access to data from multiple enterprise locations, giving analytics teams quick and easy access to all enterprise data and enabling them to connect to and discover trusted analytics assets and collaborate with other team members to generate comprehensive and actionable insights. This can save countless hours of searching for data manually in various repositories. 

Overcoming Challenges in Big Data Analytics with Neebo

Neebo is a virtual analytics hub that can help your company eliminate data silos and make it much easier to collaborate on analytics assets. With Neebo, your team can find, combine, share, collaborate, and report on analytics assets—from one central location.

Learn more about how Neebo can help your team get more out of you data by scheduling a 1:1 Demo

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