Unravel Data: The AI foundation for a cloud future
For years, world-wide organisations have become increasingly excited about the prospect of a cloud-based future. As the dream becomes an ever closer reality for many kinds of business and institution - with organisations of all sizes on their cloud journeys, at the beginning of 2019, Forrester predicted that enterprise spending on cloud services was set to surge. IDC also predicted that global spending on public cloud services and infrastructure would reach $210bn in 2019, an increase of 24% from 2018. But one obstacle stands create friction and introduce risk: the process of migration.
As all indications point to a massive shift in data deployments to the cloud, it is more important than ever that the transition from on-premises to Cloud is as risk free as possible.. In today's climate any loss or disruption to data can have a huge business impact. It’s a complex process, is frequently underestimated and many organisations have found there’s lots that can go wrong that can impact the business.
The answer to this is automation fueled by robust Machine learning training models and artificial intelligence. These concepts and the tools that enable them can determine the prerequisites of cloud infrastructure, application dependencies,the appropriate target cloud instance profiles, and provide troubleshooting in real-time.
With this in mind, let’s dive into the ways in which AI will build the future of business in the cloud.
We can’t build from the top down; we need a strong base
Organisations across the globe have found the cloud to be an ideal place to run modern data applications due to big data’s elastic resource requirements. Furthermore, with the lack of data talent an ever-looming issue for most companies today they have been driven to adopt a cloud-first strategy to ensure business operations are accessible for a range of employees.
The cloud offers great promise for developers especially, as it can increase the speed at which they develop software features and increase the resilience of applications once they are deployed - along with enhanced security through the use of multiple server locations. With all this considered, it is no surprise that 42% of UK businesses leverage some kind of cloud service, according to Eurostat.
However, all the perceived benefits of leveraging the cloud are redundant if organisations come up against barriers to accessing cloud services. Cloud-based data pipelines still suffer from complexity challenges at the moment, along with the lack of visibility into cost and resource usage at the application and user level. Organisations often don’t have the appropriate guidance on how to properly configure apps and resources once they’re in the cloud, as they behave very differently once being moved off-premises.
To summarise, the promise of the cloud has created a sense of excitement amongst enterprises, however, they have proceeded to go full steam ahead into adopting a cloud service, without sufficient data to ensure performance service level agreements (SLAs).
The Magic Triangle of Data: Machine Learning, Automation and the Human Force Driving Data Science
Why AI forms the strongest base
The frustration around enterprise cloud services does not have to be an ever-present problem for businesses. There is a solution, and it lies within the use of Artificial Intelligence (AI).
By using AI, machine learning and advanced analytics together, enterprises can expect unprecedented visibility, insights, recommendations and automation for optimising data workloads in the cloud.
How does AI overcome the barriers to create harmony with cloud services that so far have not been tackled? It comes under two simple categories; visibility and prediction.
Prediction: the predictive power of AI helps businesses overcome the common challenges when trying to make the most of their cloud infrastructure as it provides automated troubleshooting and autotuning of complex and real-time data pipelines running on cloud platforms. Furthermore, automation allows for recommendations to be made on the best apps to migrate to the cloud based on the performance of the existing big data stack, ensuring that organisations only migrate the apps to the cloud that will thrive in the cloud.
Visibility: enterprises struggle with ensuring visibility across their entire data stack, however with the use of AI and automation, the technology is able to provide detailed reports and metrics on app usage, performance, cost and chargebacks in the cloud in real-time.
We are all aware that the future is one built within the cloud, however, our cloud-based future will crumble beneath us if we don’t ensure that it has a solid foundation, built on AI capabilities.
Kunal Agarwal, CEO at Unravel Data
Kunal Agarwal co-founded Unravel Data in 2013 and serves as CEO. Kunal has led sales and implementation of Oracle products at several Fortune 100 companies. He co-founded Yuuze.com, a pioneer in personalised shopping and what-to-wear recommendations. Before Yuuze.com, he helped Sun Microsystems run Big Data infrastructure such as Sun's Grid Computing Engine. Kunal holds a bachelors in Computer Engineering from Valparaiso University and an M.B.A from The Fuqua School of Business, Duke University.