Big Data Analytics Challenges and Solutions
- Justin Reynolds
- February 3, 2020
In the data-driven world, business intelligence is in high demand. 97.2 percent of companies today are investing in big data and AI to drive growth and development.
Despite this, many organizations face big data analytics challenges and strategic, tactical level efficiency. According to Gartner, 87 percent of organizations have low BI and analytics maturity, meaning they’re mainly 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 real data-driven competitors are known for. Simultaneously, lacking these abilities makes it that much harder to make the best decisions—which puts them at a significant disadvantage.
Top Big Data Analytic Challenges
There’s certainly no shortage of data today. 5 quintillion bytes of data is produced every day across the world. This figure is likely to increase in the coming years as more connected systems and devices come to market.
It brings 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 are too much data and too many data sources for most businesses to handle. Big data has created many new big data analytics challenges 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up and modernize their systems to use their data effectively, as the bulk of yesterday’s tools and technologies are outdated and ineffective.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 the 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 cannot process, store, and manage all of it.
It is problematic because challenges of 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 analytics 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 reduce the pain of the data science skills gap using automated machine learning (AutoML), which 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
It can be challenging for many teams 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, leading to data quality issues.
A secure, centralized, and cloud-based analytics portal that brings all analytics assets in one place makes sharing and collaborating big data analytics much more manageable. 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 with mundane or irrelevant findings. In other instances, team members—even accomplished data scientists— may 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.
For instance, Tableau offers a wide range of curated templates that teams can use to create graphics. More skilled analytics professionals can also create their custom visualizations.
Challenge: Breaking Down Data Silos
Analytics professionals often need to locate data across different departments and applications, which is usually frustrating and time-consuming. Disparate systems often do not communicate with one another, making it harder to access critical data when it’s needed.
Datameer Spotlight is 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. It can save countless hours of searching for data manually in various repositories.
Overcoming Big Data Analytics Challenges with Datameer Spotlight
Datameer Spotlight is a virtual analytics hub that can help your company eliminate data silos and make it much easier to collaborate on analytics assets. With Datameer Spotlight, your team can find, combine, share, collaborate, and report on analytics assets—from one central location.
Learn more about how Datameer Spotlight can help your team get more out of your data by scheduling a 1:1 Demo