The Challenges of Big Data Storage in Business
In our increasingly data-centric world, businesses must store, process, and analyze copious amounts of information effectively.
According to Fortune Business Insights, the global big data analytics market has a projected value of $348 billion in 2024 and is expected to grow to $924 billion by 2032.1 Industries like healthcare, manufacturing, and retail have become increasingly reliant on big data in recent years. Technology like artificial intelligence will continue to fuel the need for quality data. This focus on data-driven business decisions offers many opportunities but can lead to data storage challenges.
So, what is big data storage? And how can businesses overcome challenges to take advantage of the benefits?
What is Big Data Storage?
Big data storage is the architecture businesses rely on to collect, manage, and store large amounts of data. Storage solutions should keep the data collected safe while still allowing professionals to access it for analytical purposes, as well as other business functions.
Proper big data storage improves efficiency, helps provide better customer service, and aids in many other business practices. Even small businesses require a data storage solution to run their operations. Unfortunately, not all organizations that need to store data have the resources or the skills to do so successfully.
Challenges of Big Data Storage
Organizations face numerous challenges when storing, processing, and analyzing massive amounts of data. Here are some of the challenges organizations face when working with big data.
Storing
- Volume: As the name suggests, big data requires storing large quantities of collected data. It can become easy to overwhelm an organization’s infrastructure with substantial quantities of data if the existing architecture is not up to the task.
- Cost: Storing substantial amounts of data safely can be a costly endeavor, requiring up-to-date systems and professional expertise to succeed.
- Quality: Data stored haphazardly may not have the same quality as properly stored data, leading to issues when processing and analyzing.
- Security: It’s vital that the data is secure, as much of the data collected by organizations is sensitive, such as health or financial information. Secure data storage requires knowledge and resources.
Processing
- Data Cleaning: It can be challenging to clean and prep large volumes of data. Organizations must have ways of removing duplicates and organizing the data in a way that readies it for analysis.
- Scalability: A database must handle increasing amounts of data as organizations grow without jeopardizing the quality of the data.
- Fault Tolerance: A system must continue functioning if there is a failure or malfunction within the database.
Analyzing
- Data Complexity: Data may come from multiple sources with different structures and sizes. This can make it difficult to analyze the data effectively.
- Pattern Recognition: Systems must recognize when incoming data shares similar attributes with existing data to organize and analyze it effectively.
- Visualization: A challenge when working with big data is how to represent data visually, using charts, graphs, or other elements that help communicate the findings.
- Skill Gap: Data analysis relies on knowledgeable professionals to work with it. Currently, there’s a gap between what companies need and what professionals know. Programs like 51ÉçÇø’s online master’s degree in business analytics help educate future professionals to lessen this gap.
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Big Data Storage Solutions
Looking at the list of challenges can feel overwhelming, but businesses are not without solutions. Every day, organizations are perfecting technologies that professionals can use to meet the needs of their businesses and data. Explore the types of solutions that can address the issues organizations face.
Distributed File Systems
Distributed file systems are one solution for managing big data. These systems span across multiple file servers. This allows files from multiple locations to be available to users regardless of location. Some examples include:
- Ceph
- Hadoop Distributed File System
- GlusterFS
- Lustre
According to cloud technology company Appvia, a distributed file system is useful when you have multiple instances of an application running concurrently because ensures that all instances have access to the same data.2
Cloud Storage
Cloud storage allows you to store data remotely on an off-site server, making it widely accessible among multiple users. The data is available through the internet. Examples include:
- Amazon S3
- Google Cloud Storage
- Azure
Google describes the benefits of cloud storage as enabling organizations to store, access, and maintain data without having to operate their own data centers, making the system scalable.3
Data Warehouses
Another big data storage solution is a data warehouse. A data warehouse is a centralized repository of data collected from a variety of sources. Examples of data warehouse solutions include:
- Amazon Redshift
- BigQuery
- PostgreSQL
Data warehouses provide organizations with the sole place to store collected data to help them make decisions. According to Analytics8, a consulting firm, data warehouses provide quick access to data, scalability, centralized data storage, and security.4
In-Memory Databases
In-memory databases store data in a computer’s main memory. Examples include:
- Apache Ignite
- Memcached
In-memory databases can return information quickly, so they work best for organizations that need to access data quickly and see a lot of traffic. Amazon Web Services points out that the major advantage of an in-memory database is that you can make data-based decisions in real-time.5 This solution might be especially useful for gaming organizations, for example.
Object Storage
As its name suggests, object storage manages data as objects instead of files. Examples include:
- MinIO
- Swift
These databases are designed to handle substantial amounts of unstructured data, as they do not require a clear organizational structure when processing. IBM suggests that an object storage solution is best for those requiring a cost-effective option for unstructured data or looking for a long-term storage solution for data that does not change frequently, such as music or video files.6
No matter what solution an organization chooses, professionals must be armed with the expertise to execute big data storage effectively. Earning a master’s degree can help you take the next step in your career and harness the benefits of big data.
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Become an expert in interpreting data and telling its story with 51ÉçÇø's online Master of Business Analytics. We take a different approach to analytics. Your education will focus on practical application and practice-based learning. Learn to use tools you already have, like Excel, in addition to analytics and visualization software.
Because a previous technical background is not required, our comprehensive course progression is designed to help you build foundational knowledge early in your program. As you progress through the program, you'll gain additional skills through advanced learning opportunities.
Build the knowledge you need to store and manage big data properly. Start the business analytics program at 51ÉçÇø today.
Sources
- Big Data Analytics Market Size, Share & Industry Analysis. Fortune Business Insights Retrieved August 26, 2024, from .
- Making the Right Choice: Selecting a Distributed File System for Containerized Environments. Appvia. Retrieved August 26, 2024, from .
- What is Cloud Storage? Google. Retrieved August 26, 2024, from .
- A Guide to Data Warehousing: From Strategy to Implementation. Analytics 8. Published July 29, 2024. Retrieved September 6, 2024, from .
- What is an In-Memory Database? Amazon Web Services. Retrieved September 6, 2024, from .
- What is Object Storage? IBM. Retrieved September 6, 2024, from .
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