Why Big Companies Opted To Use Hyperscale Analysis?

Over the last few years, technology has changed the way we do business. These changes have allowed us to provide better products and services across an ever-changing digital landscape. As people utilize these business services, large amounts of data are being collected.

This data, called big data, is filled with hidden information. Successful businesses will tap into this data to extract information and intelligence. When we know what a customer purchases, it’s information. When we can understand the context of the purchase, it’s intelligence.

Big data can be leveraged to help drive innovation toward the development of new products and services. Innovation has become critical to staying ahead in a disruptive market. Big data analysis should be fundamental when building a strategy that leads to market advantage and success.

Hyperscale data centers are on the cutting edge when it comes to analyzing big data. The massive amounts of memory, storage, bandwidth, computational power, and speed required to capture and analyze big data are difficult to come by, for many organizations. To meet this ever-increasing demand in fast-growing markets, companies are turning to hyperscale analysis .

Hyperscale analysis

It would be easy to say that companies turn to hyperscale analysis because of the speed, flexibility, and reliability. But there are plenty of other reasons for considering hyperscale analysis. The most important reason is that data drives the world. Every digital interaction we have across mobile, tablets, desktops, IoT devices and more generates data. The sheer amount of data being produced is staggering. As more data is being collected, the need to analyze it quickly can be crucial to the success of a business.

Big Data

Data is what’s driving the changes in the current disruptive market environment. Companies realize the need to be more creative with services and product offerings. New technologies and data collection are leading the way for this new business model. Traditional databases struggle to keep up with the growing demand for capturing and utilizing massive amounts of data.

If data analysis is delayed, it becomes more difficult to make accurate business decisions in real time. For business areas such as finance, a delay could result in a significant financial loss. Another issue for companies using a traditional database solution is the variety of data. Traditional relational databases process data and provide analytics in multi-data stores. This system works within the range of terabytes and as long as the data growth is linear and structured. But once Big data increases in volume, velocity, and variety, (the 3Vs of data) traditional databases can’t keep up.

Big data presents a problem when growth is rapid and there’s no plan for managing it. These data types can vary from structured to unstructured, creating problems with real-time analysis. Hyperscale analysis solves these problems by identifying relationships within the data from different sources. And by identifying patterns and trends within the data. There are many ways to analyze big data with hyperscale analysis.

Security

The previous paradigm shift for companies was moving from a local server infrastructure to a cloud or hybrid-based solution. These solutions worked for a time, but companies started to realize the lack of real ownership and control over their data. If a cloud-based solution goes offline or if there’s a security breach, the damage can be considerable. Being cognizant of the need for growth, reliability, and security companies began looking for a better solution. A better solution is Hyperscale. Not only will hyperscale analysis be more secure; it will also adapt and scale as workloads increase. As the world becomes more connected, these workloads will continue to grow along with the need for better security.

Reliability

A cloud solution may be right for some organizations that don’t have a lot of growth. But for larger companies, growth is essential. Hyperscale analysis is not only reliable, but also automated. Automation means fewer people are needed to manage operations. With software automation, network management is far more reliable and cost-effective. When companies lose thousands of dollars every minute they are offline, it’s critical to have a reliable hyperscale analysis solution.

Scalability

Having the ability to scale largely depends on infrastructure. Hyperscale is designed around a distributed system that provisions on demand. There are several servers working together at high speed. This allows for horizontal and vertical scaling. Horizontal scaling is when on-demand provisioning adds more machines to the network as they are needed. Vertical scaling is adding more power to existing machines in order to increase computing capacity. Streaming services, video, mapping data , and IoT devices are creating so much data there’s an inherent need to be able to scale on-demand. Being able to scale on-demand allows for real-time hyperscale analysis of big data.

Sustainability

The right partner in hyperscale analysis puts environmental awareness at the center of everything they do. Reducing environmental impact while being sustainable is good stewardship. By choosing the right hyperscale solution, companies can work to meet their own targets for sustainability. When companies work together to focus on sustainability, we all benefit. Sustainability is an important reason companies choose to use hyperscale analysis.

Conclusion

There are many reasons why big companies choose to use hyperscale analysis. Having the ability to analyze big data quickly is one of the biggest reasons. Security is becoming more important as companies are managing growth. Having greater control and ownership of data ensures security and reliability. Reliability is key in fast-growing markets.Part of reliability comes from the ability to scale on-demand when needed. Horizontal and vertical scaling allows more machines and more power to increase computing capacity as workloads increase. Being able to scale on-demand allows for real-time hyperscale analysis of big data.

Data centers consume a vast amount of energy. Hyperscale analysis providers that also commit to pursuing eco-friendly facilities, such as LEED certification, are gaining popularity among large businesses that are cognizant of growth, security, scalability, and sustainability. To meet business objectives while also upholding a common commitment towards increased sustainable practice, businesses are choosing to use hyperscale analysis.