It has been a while that the enterprise technology has been dependent on the workplace. The development side of technology is at an inflection point, with the emphasis being placed on finding a suitable way to manage the convergence of 4key megatrends, namely, cloud computing, data analytics, and AI abreast the internet of things (IoT) proliferation.

These emerging trends of technology disruption have elevated the fact that developments in enterprise technology grow dedicated to the digital workplace. This convergence will prove to be the game-changer for initiatives of enterprises, making work more intelligent to keep up with growing impediments in managing voluminous data.

Embracing a Combined Approach to Business Efficiency Enhancement

Although AI, data analytics and cloud computing are leveraged in separate ways, their work chains are interestingly linked. If the information generated is big data then the cloud is the media to extract that information. Along this chain, AI has the potential to add innovation while making the data received meaningful. With the use of such data, a knowledge repository can be developed with AI, which will enable accurate predictions & insights related to consumer activity.

Despite the inseparable nature of big data and AI, issues related to storage capacity prevail owing to the influx of voluminous data. This further entails the need for cloud services as a consequence. Several enterprises are using the cloud for effective storage & utilization of semi-structured or unstructured data of an organization. Also, a perfect blend of AI and cloud is touted to be revolutionary, wherein AI-as-a-service makes improvements in existing cloud solutions, carving newer paths toward development.

Most enterprises believe that the converged platform of cloud computing, data analytics, and AI will drive significant transformations in the technology industry landscape. Even though this convergence is still at its infant phase, inevitable and phenomenal advancements are imminent during the course of evolutions that cloud computing, data analytics, and AI together would bring.

Key Emerging Trends in this Phase of Convergence

As predictive decision-making continues to gain widespread acceptance as the standard, companies are implementing AI solutions which have caused further shifts in the industry.

  • Data Set Management: Building a robust predictive inferences system with AI needs voluminous data for training, testing, and validating the solutions. A reliable data infrastructure, which can be quickly engineered at scale has become a necessity.

  • Cloud Computing Infrastructure: For AI systems, which process huge amounts of data, a compute infrastructure is needed that can scale on the basis of computation requirements. This is where the cloud becomes an imperative asset for the purpose of horizontal and on-demand scaling. Key adoption enablers include the inherent redundancy and scalability of the cloud infrastructure along with containerization technologies.

  • IoT Data Management: IoT is another important emerging area that generated a much greater amount of data and requires faster decision making. Also, there is a need to store, process, transmit and make decisions on the maintenance requirements. This further needs all three technologies – cloud computing, big data, and AI – to work together.

The dynamic nature of the technology industry has made tech-savvy professionals push themselves ahead of the latest trends in the industry. They see upskilling of capabilities as the best possible way to move ahead professionally. In the current scenario, to upskill oneself in the convergence of cloud computing, data analytics, and AI is deemed to ensure a successful tomorrow.

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