As enterprises accelerate their digital transformation, they collect large volumes of data about their business processes. These data, when properly analysed, enable unprecedented improvements in business workflows, along with opportunities for educated and more efficient decisions. Therefore, a great deal of the added value of enterprises’ digital transformation stems from data analytics. As business datasets grow in volume and diversification, enterprises consider cloud-based solutions for executing their data analytics functions. Cloud analytics come with several compelling value propositions, which is the reason why many enterprises choose to ride the wave of cloud computing for their analytics. Nevertheless, the deployment of cloud analytics solutions is associated with various technical and organizational challenges that enterprises must consider prior to investing in data analytics workflows in the cloud.
The main benefits of cloud analytics stem from the nature of cloud computing. Specifically, they include:
In addition to these benefits, modern cloud infrastructures come with a host of tools that facilitate application development. These tools offer high-level and easy-to-use functionalities, which decouple IT programmers and business users from the low-level deployment details of the analytical tools. This is nowadays possible because cloud providers offer services at higher levels of abstraction.
Once upon a time, cloud computing providers supported analytics applications based on elastic access to computing and storage resources based on the popular Infrastructure-as-a-Service (IaaS) paradigm. With IaaS, companies gained access to computing cycles and other hardware resources needed for implementing analytics functions. Furthermore, since the early days of the cloud, cloud providers offer access to Virtual Machines (VMs) in the cloud, which provides abstraction of the operating system. Using VMs, companies can deploy their cloud analytics over the virtualized infrastructure of the providers.
Over the years, the above-listed models evolved in ways that facilitate the tasks of analytics developers and data scientists. Specifically, it is currently possible to run entire application containers in the cloud, which combine entire operating systems, analytics tools and analytics applications in a single package. Such packages decouple developers and deployers from the need to deal with the low-level operations of the platforms (e.g., operating systems and tools installations) that are integrated in the package. Likewise, they facilitate the distribution of the cloud analytics applications using container images. Recently, cloud providers are also offering integrated analytics environments over their infrastructures. For instance, it is possible to use complete machine learning and data analytics environments in the cloud, to develop and deploy analytics applications. This model is characterized as Machine Learning as a Service (MLaaS) since it enables the creation and deployment of entire machine learning pipelines over cloud resources. Along with MLaaS, there are also other Platform as a Service (PaaS) paradigms for cloud analytics, which enable developers to implement end-to-end cloud analytics workflows that orchestrate multiple cloud analytics functions.
During the last couple of years, higher level abstractions have also emerged: Companies are able to invoke pre-trained and deployed cloud analytics functions as serverless cloud programs such as Cloud functions. This paradigm is conveniently called Function-as-a-Service (FaaS). FaaS enables the execution of cloud analytics as serverless functions. These high-level cloud analytics abstractions enable enterprises to save efforts and costs when opting for cloud analytics applications instead of developing their own ones on-premise.
Despite the benefits of cloud computing, several companies have second thoughts about migrating to the cloud for their analytics. The main reason for this is that most companies face challenges when realizing the migration from on-premise analytics to cloud analytics. Some of the most prominent challenges are:
Overall, Chief Information Officers (CIOs) and Senior IT Managers cannot afford to ignore the power and the benefits of the cloud when planning their business analytics functions. However, they must also make provisions for addressing the technical, technological, and organizational challenges presented above. Migrating to cloud analytics requires effective technology management to deliver the promise of increased efficiency at a lower cost.
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