Enterprises are undoubtedly already running some apps in the cloud, but many have yet to make the jump to cloud-based analytics — at least entirely. We’re seeing an increase in the number of organizations moving toward analytics in the cloud, with some pursuing a hybrid strategy in which certain data assets remain on-premise while others move to the cloud.
It’s totally understandable that organizations need time to assess their priorities, go over their budgets, come up with an action plan and then make moves. But there are some compelling reasons why enterprises should at least think about moving analytics to the cloud — if not now, then in the next few years, here experts from JatApp explain why:
Potential Cost Savings
The first thing most organizations will want to examine is the Total Cost of Ownership (TCO) between on-premises data analytics vs. cloud analytics. Moving your data to the cloud can potentially reduce your TCO.
On-premises solutions can require larger investments outright for the hardware, software, and staffing costs. They can also take more time to get up and running — plus you have to factor in future upgrade cycles, which can cause disruption and require continued investments to bring your analytics tools up to speed.
Cloud technology tends to reduce the TCO because all you need is the software to get started and subsequent upgrades can proceed more smoothly, which means less downtime.
Of course, the final price tag depends on your analytics load and existing infrastructure — but there is the potential for cost savings or breaking even while improving functionality using cloud tech.
Speaking of downtime, cloud-based analytics tend to be more scalable than their on-premises counterparts, which is a key consideration for a growing business. One particular reason to consider moving analytics to the cloud is this approach makes it possible to pay only for the storage space you need until your data demands grow down the line.
Some organizations employ multi-cloud strategies, in which they use several vendors to mitigate risk and keep costs competitive.
Agility and Flexibility
Cloud analytics has the capability to integrate directly into business apps and workflow tools employees already in use. So, rather than having to access an external data analytics tool to access insights, people can now do so from within the apps they’re already using.
As TWDI writes, this contextualizing of data analytics within apps allows users to “operate a consistent workflow within a single application without multitasking and breaking their trains of thought.” The ideal result? Improved productivity, efficiency, and adoption of data analytics tools.
Cloud-based agility helps users get data into different applications faster and more seamlessly. Flexibility allows developers to spin up an app quickly and experiment around a new use case, for example.
Governance and Security
One major reason some organizations have been hesitant to proceed with cloud-based analytics is the perception that data governance and cybersecurity are riskier than with an on-premises solution. This used to be true. But advanced data analytics platforms today offer hands-on, enterprise-grade governance that allows administrators to set granular controls for data access and monitor usage across organizations.
As CIO writes, “Security is far less about technology than it is processed. That’s a fact no matter where the data resides.” Instead of thinking of on-premises analytics as safe because they’re physically within your company’s realm and cloud analytics as dangerous because they exist physically outside your company’s walls, consider your organization’s processes surrounding cybersecurity and data governance.
Of course, the tools you choose matter — the ability for administrators to control data usage is a must. But it’s no longer true in this day and age that on-premises solutions are inherently more airtight than cloud-based ones.
Every enterprise must critically evaluate the pros and cons of both cloud analytics and on-premises analytics to decide which combination works for them. But many organizations are finding moving at least some of their data analytics functionality to the cloud provides flexibility, scalability, and affordability.