Big data can be a revolutionary tool for businesses across all industries, but like all tools, its effectiveness depends on how well it is used—and big data has proven particularly difficult for many organizations to wield. To remain competitive in an increasingly data-centric landscape, businesses must learn how to capitalize on big data’s potential. This article looks at the challenges of big data and explores why so many big data projects fall short of expectations. It also presents the seven most common obstacles faced by enterprises and offers a roadmap to overcome them and make the most of big data.
Table of Contents
What Is Big Data?
Big data is more than just information in large quantities—more specifically, it’s data too large and complex to manage or process with conventional methods. Processing even a fraction of the millions of terabytes of data generated daily takes considerable computing power and storage capacity. It also takes data quality, data management, and data analytics expertise to maintain all that data and unlock its potential.
Even a minor amount of data can be helpful to businesses that know how to use it to learn more about customer behavior, product performance, and market trends, for example—but small volumes of data also provide limited reliability. Just as a larger sample size ensures scientific experiments are more representative of the real world, big data provides a better look into actual events and trends.
The Big Data “3 V’s”
The “big” in big data covers three primary categories, known as the Three V’s—volume, velocity, and variety:
- Volume. This is the most straightforward of the three, as big data naturally involves huge amounts of data. The sheer scale of information in these datasets renders conventional storage and management systems effectively useless.
- Velocity. Big data is also big in its velocity, or how fast new information is gathered and processed. Processing must be rapid to keep up with the pace of information.
- Variety. information in these data sets comes in multiple formats from numerous sources—industrial devices, social media channels, emails, for example—and can include text, sales data, videos, pictures, or sensor information, to name just a few. This rich variety provides a more complete picture of what the business wants to understand.
These three dimensions provide a useful way to think about big data and the challenges of working with it. It involves unthinkably huge amounts of data coming in like a firehose at blistering speeds in too many shapes and sizes to easily manage.
Challenges of Big Data
This volume, velocity, and variety of data can push businesses further than ever before, but the majority of big data projects fail. Here are seven of the most common reasons why, and solutions to help overcome these obstacles.
1. Cybersecurity and Privacy
Security is one of the most significant risks of big data. Cybercriminals are more likely to target businesses that store sensitive information, and each data breach can cost time, money, and reputation. Similarly, privacy laws like the European Union’s General Data Protection Regulation (GDPR) make collecting vast amounts of data while upholding user privacy standards difficult.
Visibility is the first step to both security and privacy. You must know what you collect, where you store it, and how you use it in order to know how to protect it and comply with privacy laws. Businesses must create a data map and perform regular audits to inform security and privacy changes and ensure that records are up to date.
Automation can help. Artificial intelligence (AI) tools can continuously monitor datasets and their connections to detect and contain suspicious activity before alerting security professionals. Similarly, AI and robotic process automation can automate compliance by comparing data practices to applicable regulations and highlighting areas for improvement.
2. Data Quality
Data quality—the accuracy, relevance, and completeness of the data—is another common pain point. Human decision-making and machine learning require ample and reliable data, but larger datasets are more likely to contain inaccuracies, incomplete records, errors, and duplicates. Not correcting quality issues leads to ill-informed decisions and lost revenue.
Before analyzing big data, it must be run through automated cleansing tools that check for and correct duplicates, anomalies, missing information, and other errors. Setting specific data quality standards and measuring these benchmarks regularly will also help by highlighting where data collection and cleansing techniques must change.
3. Integration and Data Silos
Big data’s variety helps fill some quality gaps, but it also introduces integration issues. Compiling multiple file types from various sources into a single point of access can be difficult with conventional tools. Data often ends up in silos, which are easier to manage but limit visibility, limiting security and accuracy.
Cloud storage and management tools let you shift information between databases to consolidate them without lengthy, expensive transfer processes. Virtualization can also make integration easier—data virtualization tools let you access and view information from across sources without moving it, which increases visibility despite big data’s volume and velocity.
4. Data Storage
Storing big data can be a challenge—and a costly one. Businesses spent $21.5 billion on computing and storage infrastructure in the first quarter of 2023 alone, and finding room to store big data’s rapidly increasing volumes at its rising velocity with conventional means is challenging, slow, and expensive.
Moving away from on-premise storage in favor of the cloud can help—pay for what you use and scale up or down in an instant, removing historical barriers to big data management while minimizing costs. But the cloud alone won’t be sufficient to keep pace. Compression, deduplication, and automated data lifecycle management can help minimize storage needs, and better organization—also enabled by automation—allows faster access and can reveal duplicates or outdated information more readily.
5. Lack of Experience
Technical issues may be the easiest challenges to recognize, but user-side challenges deserve attention too—and one of the biggest is a lack of big data experience. Making sense of big data and managing its supporting infrastructure requires a skillset lacking in many organizations. There’s a nationwide shortage of jobseekers with the skills being sought by enterprises, and it’s not getting any better.
One solution? Rather than focusing on outside hires, foster data talent from within existing workforces. Offer professional development opportunities that pay employees to go through data science education programs. Another is to look for low-code or no-code analytics solutions that don’t require skilled programmers—similarly, off-the-shelf software and open source big data solutions are more common than ever, making it easier to embrace big data without extensive experience.
6. Data Interpretation and Analysis
It’s easy to forget that big data is a resource, not a solution—you must know how to interpret and apply the information for it to be worth the cost and complexity. Given the sheer size of these datasets, analysis can be time consuming and tricky to get right with conventional approaches.
AI is the key here. Big data is too large and varied to analyze quickly and accurately manually. Humans are also likely to miss subtle trends and connections in the sea of information. AI excels at detail-oriented, data-heavy tasks, making it the perfect tool for pulling insights from big data. Of course, AI itself is just a tool and is also prone to error. Use AI analytics as a starting point, then review and refine with human expert analysts to ensure you’re acting on accurate, relevant information.
7. Ethical Issues
Big data also comes with some ethical concerns. Gathering that much information means increased likelihood of personally identifiable information being part of it. In addition to questions about user privacy, biases in data can lead to biased AI that carries human prejudices even further.
To avoid ethical concerns, businesses should form a data ethics committee or at least have a regular ethical review process to review data collection and usage policies and ensure the company doesn’t infringe on people’s privacy. Scrubbing data of identifying factors like race, gender, and sexuality will also help remove bias-prone information from the equation.
While size is one of big data’s strongest assets, consider whether you need all the information you collect—not storing details that don’t serve a specific, value-adding purpose will minimize areas where you may cross ethical lines.
The Bottom Line: Eliminate Challenges to Succeed with Big Data
Big data is a complicated issue. The sheer volume and variety of the data and the speeds at which it collects poses technical challenges to enterprises looking to establish the infrastructure to process, store, and analyze it. The nature of the work also demands expertise that’s not always easy to come by. As a result, most big data projects fail. But the payoffs are also big, and enterprises that approach big data strategically and prevent or overcome common obstacles can capitalize on the promise of big data.
Read The Future of Big Data to learn about the trends shaping this field and how they will affect the way enterprises work moving forward.