The process of moving big data is expensive, time-consuming, and requires a substantial investment in equipment and resources. Identifying the true cost of this process enables organizations to evaluate the benefit of alternative solutions.

Updated statistics show by the end of 2021, the world will have produced 79 zettabytes of data, much of it generated in edge environments. At present, the fastest way to transfer large quantities of data is not via digital methods, but by copying the data to a disk drive. The disks often consist of full-sized truck-mounted storage. The truck then transports the data to its destination, where it is uploaded to the data center. After this, the disk is wiped clean and ready for reuse. Most data transport vehicles can carry a maximum of around ten petabytes of data, so moving the data generated in 2021 will take more than 7 million trips.

Financial Cost

Moving big data incurs both direct and indirect financial costs. Direct costs are simple to determine because users can track them on a balance sheet. These costs include:

Hardware expenditure, such as physical servers, infrastructure maintenance contracts, and computing equipment

Overhead costs such as premises, staffing, and network connectivity

Software costs, such as program licensing, warranties, and support personnel

Moving and storage costs, such as bandwidth, data warehousing, and cloud migration programs. A study by Amazon shows a data warehouse containing 40TB of information required an annual budget of around $880,000 to maintain.

Indirect financial components are often less easy to identify and may be industry-specific. However, costs such as hiring qualified staff to convert, transport, and reconfigure data can be astronomical. Scrubbing information requires the involvement of a data scientist, for example, while maintaining security incurs labor and technology costs and the risk of ransoms and penalties associated with data breaches.

Other indirect costs of big data migration include losses in productivity and revenue when on-site servers experience downtime. When a migration takes a long time to complete, the data is obsolete by the time it’s accessible for processing, causing organizations to lose the benefit of insights that could boost profitability.

How to combat this:

BRYCK® reduces the financial costs of moving big data by minimizing the time it takes to capture, transport, and access information. The use of BRYCK®’s lightweight, handheld device and sophisticated software eliminates the need for high, long-distance WAN costs. Instead, the BRYCK® solution reduces ongoing financial costs.

The Cost in Wasted Time

Time is money, and that’s never been more accurate than when it applies to business intelligence delivered by big data. The longer it takes to access data and apply the insights generated, the higher the cost in lost productivity and revenue for organizations competing in a data-driven world.

It’s not just about the money, either. For example, flight data captured at the moving edge of air travel can have significant implications for the safety of people on future flights, provided it’s processed fast enough.

How to combat this:

By eliminating the need to use traditional data transfer methods, BRYCK® overcomes the issue and cost of time delays. Deploying this fast, simple method of capturing data at the edge and transporting it rapidly to a data center saves time and enables immediate processing.

Added benefits of moving big data in a shorter time include saving money on resources and getting urgent access to insights that affect productivity and profitability. Minimizing the data transit period also reduces the potential exposure to security risks.

The Price of Slow Transfer Speeds

Big data migration is typically a slow process, for several reasons. Migrating large amounts of data can take hours, days, and possibly weeks, depending on the technology in use. Even the much-touted 5G can take up to two years to upload a petabyte of data. Traditional data transfer methods are also subject to processing, queueing, transmission, packet-switching, and propagation delays.

How to combat this:

At 40 GB per second, BRYCK® offers faster throughput than most other options. The solution’s multi-threaded, parallel data transfer and advanced data placement algorithms enable it to utilize flash storage bandwidth effectively. The device serializes random writes into sequential writes while combining smaller I/Os into larger ones.

By moving big data faster, BRYCK® enables organizations to prepare it for analysis quicker, gather and apply findings sooner, and reduce the quantity and cost of resources used to do so.

The Cost in Lost Productivity

Data impacts productivity in many ways. From highlighting areas for expansion to helping employees be more aware of work activities, data continually powers performance improvements. The more data available to an organization, the more it encourages well-informed decision-making.

It’s a fact that data analysis is a critical element in modern workplaces. A Bain & Company survey of 400 corporations showed organizations with the most advanced analytics usage were twice as likely to be top performers in their industry. These companies were also five times more likely to make crucial decisions faster than their peers.

Low productivity currently costs organizations around $1.8 billion annually, according to a HubSpot study updated in June 2021. This situation occurs partly because reduced productivity results in poor employee performance, which affects the quality and output of deliverables. When production costs are closely related to billing costs, profit margins depend heavily on productivity.

How to combat this:

With the improved access to data and insights BRYCK® provides, companies can make better decisions faster, implement critical changes, boost employee performance, and enhance quality control.

These actions lead to increased productivity and the associated benefits of higher profitability, amplified staff morale, better talent retention, and competitive advantage.

Resource Repercussions

Resources are seldom unlimited, and when it comes to human, financial, and digital resources, tying them up for extensive periods for the purpose of moving big data has major implications. From higher fixed costs and cost of sales, to reduced productivity and employee retention, organizations depend on data to drive research and development, manage assets, and obtain business intelligence.

More human resources mean higher staffing costs, more infrastructure requirements, and lower profit margins. Increased use of digital resources lowers the speed of communications and information transfer, while pushing up the financial cost even further.

The repercussions of such high resource usage include increased pressure on the natural environment, low shareholder satisfaction, and challenges in obtaining funding. All of these can have far-reaching consequences, most of which can be avoided by resolving the costs of big data migration.

How to combat this:

By deploying BRYCK®’s data management solution, organizations can capture and transport data from any environment, including remote and moving edges. Companies can free up their resources for other uses, while experiencing the benefits of rapid access to data.

The costs associated with moving big data are more pervasive than they appear at first glance. It doesn’t have to be this way, however. As data usage grows and its value intensifies, companies are becoming hard-pressed to find new ways of transporting data.

The increasing prevalence of data captured at distant and moving edges, bandwidth, and other constraints, and the need to process it rapidly led to the creation of BRYCK®. This new solution is rugged, portable, blazing fast, and secure, and its capabilities promise to revolutionize big data migration in the near future.