In today’s economic scenario, businesses of all shapes and sizes are witnessing explosive data growth, which is widely impacting how IT industry manages the surpassing data limits. The influx of information generated from multitude of sources present new challenges for companies that include backup requirements, storage capacity for exponentially growing data, and capability to meet application availability. These challenges have originated owing to technology advancements, increased collaboration, and hunger for information analytics. Going forward, data and information is stored on physical servers, virtual or cloud-based servers. To tap into the full potential of big data, it is imperative to protect both structured and unstructured data against all vulnerabilities. 

Now Let's Dig Deeper to Understand What Challenges Enterprises are Facing due to Big Data Growth:

Data Protection: The increasing data that includes applications, files, databases, and social media demands stringent security for its protection and large capacity to execute backup. The companies need to pay focus on data capturing, backup scheduling, and API integration for virtual servers.

SLA Compliance: The exponential data growth makes it difficult for enterprises to limit themselves in a specified backup window boundary and meet data shielding agreements. In addition, voluminous data makes disaster recovery process more intricate.

Data Restoration: It is important to back-up all the critical datain order to take the business to the new level. Selecting right set of severs and high-tech restoration capabilities are complex.

There is no denying the fact that Big Data opens new vistas of opportunity for leveraging data and information as an important asset. At the same time, it is mounting pressure on IT organizations to translate the way they manage and maintain data. How efficiently these challenges are addressed, it has direct impact on the bottom line of a business. 
In this light, businesses should look for data backup and recovery services that can promptly address the complexity, volume and other big data challenges. 

Moreover, they need to better optimization technology and evolve from conventional data-centric approach to ageographically dispersed, scalable deployment approach to ensure enhanced data protection coverage.