Realize the True Costs of Big Data Migration, CIO News, ET CIO
According to IDC, the world will have produced 175 zettabytes of data by 2025, much of it generated in edge environments. Currently, the fastest way to transfer large amounts of data is not through digital methods, but by copying the data to a disk drive. Drives often consist of full-size truck-mounted storage. The truck then transports the data to its destination, where it is uploaded to the data center. After that, the disk is cleaned and ready to be used again. Most data transport vehicles can carry a maximum of ten petabytes of data, so imagine the million trips it will take to move that amount of data in 2025!
Moving Big Data is expensive
Moving big data has direct and indirect financial costs. Direct costs are simple to determine because users can track them on a balance sheet. These costs include hardware expenses, overhead, software costs, moving and storage costs.
Indirect financial components are often less easy to identify and may be industry specific. However, costs such as hiring skilled personnel to convert, transport and reconfigure data can be astronomical. Other indirect costs of big data migration include lost productivity and revenue when on-premises servers experience downtime. When migration takes a long time, data is stale by the time it is accessible for processing, causing organizations to lose the benefit of insights that could increase profitability.
The cost in wasted time
Time is money, and it has never been more accurate than when applied to the business intelligence provided by Big Data. The longer it takes to access data and apply the insights generated, the higher the cost of 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 mobile edge of air transport can have significant implications for the safety of people on future flights, provided it is processed quickly enough.
The price of slow transfer speeds
Big data migration is usually a slow process, for several reasons. Migrating large amounts of data can take hours, days, or even weeks, depending on the technology used. Even the vaunted 5G can take up to two years to download a petabyte of data. Traditional data transfer methods are also subject to processing, queuing, transmission, packet switching, and propagation delays.
The cost of lost productivity
Data impacts productivity in several ways. From highlighting areas for expansion to helping employees be more aware of work activities, data continually fuels performance improvement. The more data an organization has, the more it encourages informed decision-making.
It is a fact that data analysis is an essential element in modern workplaces.
Low productivity is currently costing businesses around $1.8 billion a year, according to a recent HubSpot study. This situation occurs in part because reduced productivity leads to poor employee performance, which affects the quality and production of deliverables. When production costs are closely related to invoicing costs, profit margins are highly dependent on productivity.
Resources are rarely unlimited, and when it comes to human, financial and digital resources, tying them up for long periods of time in an effort to move big data has major implications. From higher fixed costs and cost of sales to reduced productivity and employee retention, companies depend on data to drive research and development, manage assets and gain business intelligence.
More human resources mean higher personnel costs, more infrastructure requirements and lower profit margins. The increased use of digital resources reduces the speed of communications and information transfer, while further increasing the financial cost.
The repercussions of such high resource use include increased pressure on the natural environment, low shareholder satisfaction and difficulties in obtaining financing. All of these can have far-reaching consequences, most of which can be avoided by solving the costs of big data migration.
Organizations need a smarter solution to help them transfer data at minimized costs in a secure manner. Deploying a quick and easy method of capturing data at the edge and transporting it quickly to a data center saves time and enables immediate processing. As data usage grows and its value escalates, businesses struggle to find new ways to transport data. Additionally, improved access to data and information that enables businesses to make better decisions faster, implement critical changes, improve employee performance, and improve quality control is the need of the industry. ‘time.
The author is co-founder and CEO, Tsecond