The Lucrative Big Data Management Market

It’s estimated that 328.77 million terabytes of data are created every single day, with a forecast stating that 181 zettabytes of data will be generated by 2025. The amount of data we create each day is growing by leaps and bounds, with more and more people having access to technology and more innovations in tech encouraging use. Now more than ever, competent big data management tools and practices are of the utmost importance. 

The big data market is growing as fast as we generate data. It’s estimated that this market was valued at 349.4 billion USD in 2023 and will be worth 397.27 billion this year. With spending on big data to rise by 1.6x by 2025, the big data market is projected to grow by 14.8% CAGR from 2024 to 2032. More and more companies are adjusting their budgets accordingly to handle and make sense of the huge amounts of data coming in; in 2023, 87.9% of companies consider data analytics to be one of their top priorities, deserving huge amounts of investment. In addition, growing adoption of things like 5G infrastructure, artificial intelligence (AI)/machine learning (ML), cybersecurity, and Internet of Things (IoT) are only increasing the amount of data generated and driving the speed at which said data is generated.

New advancements in tech are setting the stage for the big data market to grow exponentially, but to understand why that’s so important, you have to understand what big data is in the first place.

What is Big Data?

If you’re interested in tech at all, you’ve probably heard the term “big data” float around here and there. While the name might mislead some people into thinking that it's complex, the big data moniker is taken quite literally. Big data refers to large amounts of data that is generated at ever increasing volumes and speeds, hence the “big” aspect of this data.

Big data is based upon 5 “V” characteristics:

• Variety: the different types of data that are available and generated
• Volume: the amount of data generated from so many different sources
• Velocity: the speed at which data is generated and received
• Value: the insights derived from the data processed, most important from a business aspect
• Veracity: the truth or accuracy of the data generated and received

On the topic of variety, there are 3 different types of big data:

• Structured data: data that is formatted best for analysis and is typically house within a database
• Semi-structured data: data not found within a database, but still contains elements that make it easier to analyze
• Unstructured data: data that is not organized whatsoever, making it the most difficult to fit within a database and analyze

Of the raw data generated, unstructured data is by far the most common data classification. In fact, Box estimates that 90% of the data generated daily is unstructured. Because of this, as well as the “big” aspect of the data generated today, traditional data tools and processing software have proven to be inefficient for data scientists to use. Many solutions catered towards big data like the data warehouse, data lake, and other various big data analytics applications have been developed to provide businesses and data scientists with the edge over data processing and storage. 

But where does this data come from? Raw data is generated from many different sources, from transactional data created when you order and pay, to social media data derived from your usage, to even data created when you use your GPS. Perhaps one of the biggest drivers of big data is the Internet of Things (IoT) and our use of connected devices. 

With the influx of data comes its increased usage; all of the departments within a business setting are able to use the insights and predictions derived from big data analysis. From customer service teams to marketing departments to DevOps teams, big data analytics is able to provide tons of insights into the users generating the data, as well as the problems they may have. Despite analytics capabilities being big data’s biggest draw, it also presents big data’s biggest challenge. From the different types of data such as structured and unstructured data, to the volume at which data is generated, big data is a beast in itself. Data science teams must innovate on data analytics tools in order to stay ahead of the curve and process and analyze data efficiently. 

Big Data Trends

IoT Adoption Growth

The International Data Corporation estimates that by 2025, there will be 41.6 billion IoT devices generating 79.4 zettabytes (ZB) of data. The adoption of IoT devices is growing for a variety of reasons: 5G being introduced, healthcare thriving, self-driving technology becoming more readily adopted in cars, and so on. Along with our already established smart devices, our usage and reliance on IoT shows no signs of slowing down. The same can be said for the data that our usage generates!

AI, ML, and Advanced Analytics Optimizing Big Data

As mentioned in a previous section, traditional data tools and processing software just aren’t cutting it anymore with regards to big data. To efficiently cut through the mountain of unfiltered data that is generated daily, data tools and platforms are implementing different innovations that allow data scientists to tackle data headfirst. Innovations including analytics automation, processing streamliners, and, of course, the use of AI and ML.

Artificial intelligence optimizes big data analytics and management through a variety of means. Trained on countless datasets, machine learning systems and large language models (LLMs) are able to identify patterns and anomalies within large datasets, supporting predictive capabilities that streamline many analytics processes. This also applies to data collection: AI can be implemented within things like chatbots and automated product recommendations to collect more personalized customer information, improving the customer experience in the process.

Through the use of generative AI and LLMs, companies are also able to revolutionize the way they interact with data. Generative AI offers an interesting approach to data visualization, automating tons of tedious graphic processes that data scientists would normally groan at, while adding its own interpretation of the data fed. In addition, through generative AI and LLMs, code can be generated for data observability and data monitoring in general, speeding up data processes overall. 

Despite the enthusiasm for artificial intelligence, data scientists must be wary of their usage and monitor the results of these automations closely- any wrong predictions or faulty automations conducted by AI may derail data operations. 

Gaining an Edge with Edge Computing

Many companies are finding that edge computing and big data are like peanut butter and jelly- they just work. 

Edge computing is a framework that closes the gap between applications and data sources by having networks, devices, and systems close to the user. In having networks and devices closer to data sources, data is collected and processed faster, bypassing the transfer to a processing location. Benefits of adding edge computing into your big data operations are bountiful, though some of the standout points include lower latency, increased security and reliability, and faster processing speed.

Opportunities and innovations have set the stage for edge computing to show its competence in the big data space. The introduction of 5G has placed a huge emphasis on IoT devices and associated networks, with edge computing providing strong connectivity with low latency. Industry specific business applications and services operated near or on-site, such as with regards to energy and utilities, have been highly sought after- edge computing provides the power for these applications. And lastly, the amount of data being generated is growing exponentially every single day from a variety of sources, edge computing allows companies to collect and process this data locally- proving to be a competent alternative to large data centers.

Big Data Challenges

Making Sense of Unstructured Data

The IDC reported that over 73,000 exabytes of unstructured data was generated in 2023 alone. While that amount of data is a dream for some data science and analytics teams, many groan at the sheer volume of unstructured data created daily. Because unstructured data doesn’t have a predefined structure, it’s much harder to organize than structured and semi-structured data. In addition, because of the variety of formats that unstructured data can present itself in, it’s leaps and bounds harder to search and analyze as well. 

“Too much of a good thing is a bad thing” and if you don’t have the right tools to tackle unstructured data, let alone structured and semi-structured data, this mantra rings true.

One of the biggest challenges of big data is tapping into the potential of unstructured data. Structured and semi-structured data have a relative structure to work with, streamlining many data processes while providing guidelines for organization and analysis. Unstructured data often requires a separate tool set to tackle because of its complexity, which companies are often unprepared to face.

Box estimated that 40% of tech spend is dedicated to unstructured data, despite unstructured data making up a majority of the data generated. Many companies are finding themselves unprepared when facing the beast that is unstructured data, often losing the potential insights that the data holds in lieu for easier insights from structured data. 

With the right investment towards AI solutions, untangling the unstructured data mess can become a thing of the past.

Upholding Data Security and Privacy

IBM reports that in 2023, the global average cost of a data breach was $4.45 million, with that number being a 15% increase over the 3 years prior. Although data breaches aren’t necessarily a groundbreaking phenomenon, they certainly have increased in recent years, with the Harvard Business Review estimating that from 2022 to 2023, data breaches have increased by 20%.

But why?

More methods to leverage weaknesses within your data security are readily available. Anything from ransomware to even employee tampering are on the table, just be cognizant of who has access to your data.

A majority of the data we generate is stored on the cloud, Statista even estimates that in 2022, 60%+ of the global corporate data generated was stored within the cloud, too. Unfortunately, in 2023, more than 80% of data breaches involved cloud-stored data. Vulnerabilities within cloud storage are created once companies working with cloud providers misconfigure their clouds- whether granting access to an unwanted guest or even backing up their data sloppily. 

To combat vulnerabilities in data security and privacy, many cloud providers offer assistance when configuring organizational clouds, even going as far as to aid in maintenance. Companies looking to increase their security can implement a role-based access control (RBAC) system that allocates data access based on an individual’s role. And of course, companies should be looking into encryption, creating firewalls, conducting daily backups, and implementing data recovery systems.

Big Data and Lyrid

Although the big data and the insights gathered from it have limitless potential, the challenges surrounding the tech cannot be overlooked. From managing big data to boosting the security of your valuable datasets, Lyrid provides a unique approach to data storage and management, aiding in fields like:

Data Storage and Management

A huge concern when working with big data is where to store it. Databases housing that much information must be flexible, scalable, and accessible - essentially ready for anything that is thrown at it. Data scientists working on these databases must be ready for the strenuous setup, configuration, and maintenance processes.

Lyrid Managed Databases provides fully-managed MySQL and PostgreSQL databases for any solution- with all of the setup, backing up, and updates and maintenance handled. Our databases host several features to make sure that your data is being handled properly:

• RapidDeploy: Using Lyrid Cloud Manager, API, or CLI, quickly create and deploy production-ready databases
• BackupHero: Automated database backups every 24 hours
• SecureGuard: Secure systems with trusted access and IP address access to protect your data to the max
• FlexiScale: Flexible database instance plans that fit best with your business needs

Another take on data storage is using Lyrid Object Storage. Our object storage solution uses storage buckets for files and content delivery networks (CDNs) to access them. This allows for stronger and faster storage capabilities and simplified management of unstructured data, content assets, and memory-intensive workloads.

Lyrid Object Storage is serverless, storing data through APIs and web interfaces thus removing the need for servers and virtual machines, driving down your storage costs in the process. Global availability of our object storage solution is also guaranteed because of its S3 compatibility, expanding on performance as your data grows. All of your backup files, databases, logs, and data sets are also kept behind lock and key with our Secure Data Hub, making sure that only the people you grant access to can access.

Security Concerns

Perhaps the biggest challenge when it comes to dealing with big data, security and privacy have to be bolstered from a variety of different aspects. Not only do your datasets need to be protected from malware and corruption, but also from restricted access, accidental deletion, and so much more. 

Our managed database and object storage solutions host a variety of ways for you to protect your data. From RBAC access to daily backups to the security built on the Lyrid platform itself, you can rest assured that your data is safe with us.

Maximizing Edge Computing with Kubernetes

IoT, one of the biggest generators of data, is only made better when coupled with edge computing. Edge computing provides IoT networks with:

• Reduced communication latency
• Increased data security and privacy
• Real time analytics

And so much more! 

The problem with edge computing and IoT deployments is that they rely on lightweight hardware that is located on-site. With Lyrid Managed Kubernetes, you’re able to deploy containerized apps- the perfect lightweight solution for an edge computing approach to IoT. 

Providing all the capabilities and benefits of Kubernetes, without the headache, our managed Kubernetes solutions provides a plethora of automations to streamline your approach to edge computing. Automated cluster scaling, resource usage, and health monitoring are just some of the ways your clusters can become self-sufficient. And when it comes to the data generated by IoT networks, automated solutions and self-sufficiency are key.

If you’re interested in learning more about what Lyrid can do for your business, book a call with one of our product specialists!