Do Geologic Scientists Generate Too Much Data at the Edge?

Data has become massive — and it’s only growing bigger. 

• From 2010 – 2020, data interactions increased 5000%.
• Over that period, data usage increased from 1.2 million petabytes to almost 60 million petabytes.
• According to the Multicloud Maturity Report from Seagate, based on research by analysts at ESG, the median three-year compound annual growth rate (CAGR) for unstructured data under management is 39.4%. Seagate’s 2020 Rethink Data report noted that enterprise data, which grows faster than consumer data, was expected to have an average annual growth rate of 42%.

There are two simple reasons for this growth:

• More data can be collected than ever before at the micro, metro, and macro edges by devices such as cameras, drones, and autonomous vehicles and small, lightweight sensors that can now be attached to any device, located anywhere.
• Data is more valuable than ever before so individuals and organizations are collecting, transferring, storing, and analyzing every byte they can generate.

Unfortunately, while mass data has become available and valuable for everyone in every industry, many individuals and organizations are struggling to capture its value—and in the geosciences industry, the situation can be especially challenging. We wrote this article to help solve this problem. In it, we’ll explore:

• A real-world example of mass data uses and challenges in geosciences.
• How mass data challenges can be solved through better management.
• How to choose the right mass data transfer and storage tools.

The Challenges and Opportunities of Mass Data in Geosciences

Geosciences has become a data-first industry.

Before any decision can be made—from where to drill for gas, to where to erect an offshore windfarm—geoscience professionals must utilize a massive amount of data. And the more data they use, the better they can complete core tasks like exploring environments, maintaining assets, keeping workers safe, and improving efficiency.

However, geoscience professionals face many challenges collecting, transferring, storing, and analyzing the mass data they require. These include:

• Using a wide range of data, including seismic, oceanographic, meteorological, and structural data.
• Collecting most of this data from sensor-equipped field devices that are often located in harsh, remote conditions like deserts and mountains.
• Using field devices that continue to generate more and more data as they improve their sensors and create better, more detailed imaging.
• Pulling data sets that can be too large to move over satellite or 5G, and are coming from environments without consistent WiFi or wired networks.
• Leveraging data in a complex manner—some must be processed in the field, others transferred to offshore data centers, others shipped to end clients.

The result: Even though data generated at the edge is highly valuable for geosciences, the industry struggles to manage it properly, and lots of data from the field gets lost or left behind before it can be used to drive better decision making.

And yet, this is not a lost cause

Geosciences and other industries can learn to overcome these challenges, to take control of mass data, and to derive real value from every byte they can collect. Here’s how.

Mass Data Deluge: A New Perspective and Approach

On the surface, the underlying problem seems simple. Geosciences—and other industries that face similar challenges—just have too much data to manage.

There’s some truth to this. Every industry now works with more data than their existing tools and processes were designed to handle.

1. Yet ultimately this diagnosis isn’t useful for two primary reasons:
2. Mass data is only going to grow in volume and value. Professionals must adapt their management processes to handle it or get left behind. It’s already possible to manage mass data effectively.

We explored this second point in a piece of recent research that we published—The Multicloud Maturity Report. In it, we found that organizations that learned how to manage their cloud deployments and mass data:

• Beat revenue goals by nearly 2x more than less mature organizations.
• Are nearly 3x more likely to report they have a very strong business position.
• Are more than 3x more likely to expect their company’s value to increase 5x over the next 3 years.

These results make one point clear—mass data is not the problem. Professionals can evolve their approach to managing mass data and derive real value at any volume.

And at Seagate we’ve seen this firsthand. We’ve worked closely with many organizations as they evolved their approach to managing mass data.

From that real-world experience, we’ve come to see there are two practical areas to focus on to better manage data at any volume, speed, and complexity. They are:

1. How to Capture, Manage, and Transfer Mass Data.
2. How to Store and Control Mass Data Cost-Effectively.

We’ve also learned that while there are “soft” processes related to both of these areas, ultimately an organization’s ability to evolve their mass data management depends on the data transfer and storage tools they choose to deploy.

In the following sections, we’ll detail exactly what to look for in mass data tools that make it simple and easy to evolve these two areas.

Tools to Capture, Manage, and Transfer Data: 2 Criteria

Criteria 1. They Must Overcome the Key Challenges of Mass Data Transfer.

Mass data movement is complex and difficult to perform, primarily due to a handful of key challenges. Any tool must overcome these challenges, by:

• Supporting large volumes of data and coping as those data sets grow.
• Capturing, storing, and making usable every byte of data.
• Transferring data as frequently as needed (hourly, daily, weekly, or monthly).
• Transferring data fast enough to meet backup and deployment timelines
• Keeping data secure while it’s being captured and transferred.
• Interfacing seamlessly and compatibly with all cloud and edge storage destinations and architectures that geosciences companies may need to access.
• Offering a cost-effective solution easily scalable for varied data sets from small to large, with easy-to-understand and manage pricing.

Criteria 2. They Must Work in Different Data-Transfer Scenarios.

Data typically transfers across distributed infrastructures through one of three scenarios, and any viable tool must overcome the challenges inherent to each.

1. Endpoint-to-Edge: Moving data from the endpoints where they are generated (e.g. autonomous surface vessels for ocean monitoring, pipeline inspection gauges, or fiber optic distribute acoustic sensing in underground geologic structures) to edge infrastructure. Most challenges relate to the large size of the data sets being transferred.
2. Edge-to-Core: Moving large data sets from edge infrastructure to core data centers. Shares the same challenges as endpoint-to-edge transfers. In addition, edge-to-core often relies on unreliable, unavailable middle-mile infrastructure.
3. Edge and Core to Cloud: No matter where an organization generates data, they ultimately transfer it to their clouds. Traditional cloud providers provide reliable connections, but bandwidth can present problems for the regular influx of massive data sets and their costs are often high, unpredictable, and difficult to manage.

Data Transfer Options: Strengths and Weaknesses.

Finally, any viable data transfer tool must work seamlessly with all cloud and edge storage destinations and architectures that geosciences companies may need to access. These include:

• Wireless: A widely available and relatively low-cost option that can suffer performance issues and bottlenecks during mass data transfer scenarios.
• Last-Mile Wired: A very reliable, mature, always-on option with open standards, but with variable performance depending on location.
• Middle-Mile Wired: An option that delivers massive performance and reliability, but that’s very expensive and not widely available.
• Cloud Transfer: A high-performance option that’s easily provisioned, but creates vendor lock-ins and features a complex, unpredictable cost model.
• External Drives: An open, easily available, and low-cost option, but offers minimal security, and required on-premise deployment and administration.
• Cloud Vendor-Specific Devices: A rugged, mature option built for enterprise use, that’s expensive, creates vendor lock in, and requires on-premise deployment.
• Enterprise Data Shuttles: A flexible, rugged, open, fast option that’s limited by physical shipping and requires on-premise deployment and administration. 

Learn in more detail how to determine the most effective data transfer options available for your situation by reading our Enterprise Data Transfer Playbook.

Tools to Store and Control Data Cost-Effectively: 3 Criteria.

Criteria 1. They are Cost-Effective.

Cost is a big challenge in the world of mass data. Any viable tool must offer a cost-effective solution to the field’s biggest pricing challenges, by including:

• Predictable cloud costs that allow accurate budget forecasting and a clear picture of how costs scale with data storage and usage.
• No add-on ingress/egress fees or unexpected API fees that create unpredictable costs after a migration or from calls made by applications.
• No data lock-in that makes it expensive to move data to new providers and environments where it’s needed to render insights.

Criteria 2. They Make Data Accessible 24/7/365.

Finally, any viable tool must be able to maintain large quantities of data and offer full control over that data. To do so, the tool must offer:

• Always-on storage that is accessible any time, from anywhere, for any usage.
• Data sovereignty to place, move, and share data as desired without fees.
• Freedom of movement of large and small data sets in a frictionless manner.
• Bring your own anything interoperability that works with any solution.
• Orchestration that easily unites stored data with resources and applications.

Criteria 3. They Offer Modern Technical Features and Capabilities.

Any viable tool must be built according to modern technical standards, and operate seamlessly within today’s data infrastructure environments. To do so, they must offer:

• Analytics support by consolidating mass data within single repositories.
• Scalability to automatically adapt to growing data sets and sources.
• Flexibility to manage retention time for data sets.
• Integrations to seamlessly connect with a range of devices and applications.
• On-Demand Access to retrieve live data from anywhere, at any time.
• Critical Data Protection with secure, end-to-end user access control and object immutability that meets regulatory and data security requirements.
• Capacity-Based Pricing that moves large volumes without added costs or fees.

Mass Data Made Easy with Seagate’s Lyve Mobile and Lyve Cloud.

Finding viable tools for mass data transfer and storage in the geosciences industry can be challenging. Many data tools were designed for environments where data was smaller, slower, and leveraged for far fewer uses—and these tools typically fail to meet the criteria outlined above.

Thankfully, Seagate has developed two tools that are tailor-made to meet these criteria, and to help individuals and organizations evolve their mass data management as quickly, easily, and cost effectively as possible. Let’s look at both.

How Lyve Mobile Captures, Manages, and Transfers Mass Data.

Lyve Mobile is an edge storage tool that transfers mass data sets quickly, securely, and efficiently through an on-demand consumption model that keeps costs simple.

Lyve Mobile offers a range of storage devices that are scalable, modular, and vendor-agnostic, and designed to capture, manage, and transfer modern data sets in modern environments. Every Lyve Mobile storage device:

• Solves the key challenges of mass data transfer by capturing high-volume data from the field—even at the extreme edge where geosciences often play—and rapidly and securely transferring it to workspaces.
• Drives all three data-transfer scenarios and integrates seamless with every data transfer technology—from wireless to wired.
• Provides cost-effective data transfers as a service that scale up and down as needed through predictable pricing models and subscriptions.

Learn more about Lyve Mobile.

How Lyve Cloud Enables Simple, Scalable Storage and Management of Mass

Lyve Cloud is an object storage tool designed to handle mass data sets and to reduce total cost of ownership (TOC) through stable, simple-to-understand pricing models.

Lyve Cloud makes it easier to maintain a single flexible data repository and to combine it with services from multiple cloud providers, and allows data to move seamlessly between them through a single intuitive dashboard.

• Lowers TCO by up to 70% through predictable, cost-effective, capacity-based pricing and no unpredictable hidden fees for egress or API calls or lock-ins.
• Offers always-on 99.99% availability that provides access to data at any time without sacrificing durability, security, or regulatory compliance.
• Dissolves siloes across multi-cloud environments by making it easy to transfer data seamlessly across public and private cloud deployments.

Learn more about Lyve Cloud.

These two tools give individuals and organizations everything they need to evolve their ability to collect, store, and use data from any source, at any scale.

Take the Next Step: Gain Control Over Mass Data with Seagate.

It’s time to take control of your mass data.

You can now collect, store, and pull value from far more data than ever before, and drive your work to new heights — if you have the right tools, and the right partner.

• Learn More: Read our Data Transfer Playbook and Multicloud Maturity Report.
• Go Deeper: Dig into our Lyve Mobile and Lyve Cloud solutions.
• Get 1-on-1 Advice: Schedule a free consultation with one of our experts.