What could discourage organisations from adopting an analytics cloud platform?
An analytics cloud platform is a vital asset for any business. With the world forced indoors due to the COVID-19 pandemic, organisations need to find ways to meet their data analytics needs, and many analytics providers have stepped up to the plate. For example, SAS uses deployment patterns like containers and Kubernetes to orchestrate and streamline deployments across major cloud infrastructure providers like Microsoft and Google.
However, despite this immense progress, there are still several obstacles that prevent private and public organisations from adopting an analytics cloud platform. Anticipating these objections can help SAS analytics engineers proactively address them.
The challenges of implementing an analytics cloud platform
Data security
Security is one of the biggest concerns for organisations looking at an analytics cloud platform. Analytics engineers have to maintain a balance between tight security protocols and sufficient flexibility to make working with data easier.
After all, most companies don’t want their information stolen, but neither do they want a rigid system that makes standard operations difficult to carry out. To maintain balance, analytics platforms must have built-in flexibilities that allow them to integrate popular security tools.
Furthermore, since security threats are always evolving, platform developers need to keep an eye on the latest security trends and safeguards to assure potential customers that their information is safe from evolving dangers.
They work on any system
An analytics cloud platform should seamlessly integrate into an organisation’s processes. Organisations want to use their current technologies and processes, alongside the new cloud platform, instead of having to revamp their entire infrastructure to accommodate it.
The challenge for most analytics engineers is to ensure that their cloud platform is not confined to a specific operating system or hardware requirement because it reduces accessibility, locking out organisations who might have been interested in the platform.
However, creating a system like this is not an easy task because it requires the system to be self-aware and make decisions based on available resources.
For example, should the system use in-stream processing or a single-threaded processor? If an analytics cloud platform is going to work, the system needs to maintain processes, while scaling resource use up and down when needed, without intervention from programmers.
Operationalising data pipelines
One big concern for organisations is the work involved in getting data pipelines ready for use. As any cloud analytics engineer knows, operationalising a data pipeline complete with fault-tolerant recovery and full monitoring is not a task that can be completed overnight.
Establishing multi-cloud and hybrid workflows, setting parameters for dynamic control, debugging production runs, resource management and fault tolerance are just some of the operations that need to be completed before an analytics cloud platform is ready.
The effort needed to operationalise pipelines is an obstacle because most organisations are not clear about how all this technical work will affect their scheduling and production. They want to know how soon they can shift their data to the cloud, and use it to improve production processes.
As cloud analytics engineers, it’s your job to make the process as transparent as possible to build confidence with customers and encourage them to invest in the cloud platform.
Managing hybrid environments
A hybrid environment is often used as a solution for an organisation that needs 100% uptime performance. However, this arrangement comes with several challenges that hinder the appeal of the analytics cloud platform.
The main reason being that it takes a lot of work to operationalise the data pipeline across multiple cloud platforms and on-premise environments. Analytics engineers need to devise ways to optimise processes, to ensure they are as timely and efficient as possible.
Migration needs to be optimised
Migration from legacy systems, such as data warehouses can be time consuming and expensive. Data can be lost or corrupted if the migration process is not done properly. When shifting to the cloud, it is important to set up proper procedures, including backing up data.
Analytics programmers and engineers need to consider all aspects of platform migration and plan out the process in detail, which includes backing up data in a secure fashion.
Maintainability and governance
Who is responsible for monitoring cloud systems? How are servers going to be upgraded and maintained if applications need to be up 100% of the time? Establishing who is responsible for ownership can be challenging for many organisations, especially if they are not experienced in dealing with cloud platforms.
In my experience, the best way to resolve this uncertainty is to keep the terms as simple as possible and remain transparent. As analytics programmers and engineers, it can be very difficult not to discuss governance and maintainability without bringing in the technical terms.
But most clients are often put off by this method of communication, so it’s best to state the terms of maintenance and governance in a language that’s as simple as possible.
Addressing the challenges of cloud systems in analytics
An analytics cloud platform is an important asset, but it is filled with challenges that often obstruct companies from undertaking the platform. However, if we can anticipate these problems and address them, we can remove the obstructions that discourage companies from adopting cloud platforms.
The ideal analytics cloud platform allows businesses to leverage powerful analytics platforms without the need for a programming team and effortlessly integrates itself into business operations.
