However, data discovery is not a field without challenges. Raw data is purely raw in nature. IT teams have to set up big processes to mine the data and prepare the data. Also, every company’s requirements are different. Business initiatives are ever changing and keeping up with new directives can require agility and scalability. Additionally, successful big data analytics operations require high level of computing resources, technological infrastructure, and highly skilled personnel.
Most companies cannot keep up with these high qualifications and they fail eventually. Lack of computing power and automation was the primary reason which companies could never achieve a truly efficient production scale analytics operation. Big data was too expensive to operate upon without any clear ROI. The rise of cloud computing and newer technologies has made it possible for enterprises to afford new age big data platforms and tools.
What is different with Big Data today?
With an exponential growth in data, companies need to continuously scale their infrastructure to maximize the economic value of the data. Hadoop in its initial days of recognition was very expensive and required very highly skilled personnel to operate. The velocity of the data was high and software requirements were extreme. This caused many big data projects to fail as alignment of all parameters to operate in sync was a daunting task.
The accessibility to cloud infrastructure and advanced technologies has increased for many enterprises today. This has enabled the data administrator and devops teams to be the enabler of the entire platform operation, and no longer a bottle neck. Cloud provides a great infrastructure to enable companies to grow beyond their existing systems in all aspects of big data, i.e., all 4 V’s.