The Benefits and Future of Cloud Data Analytics

Virtually all CIOs recognize the critical value of data, and yet data analytics has been slower to move to the cloud than other enterprise workloads. With the rise of the Internet of Things (IoT) and new business imperatives magnified by the pandemic, that's finally changing. Business leaders should be thinking seriously about cloud analytics to gain the speed and agility they need to compete effectively.

A few factors have kept enterprise data on-premises historically, including concerns about security, data gravity — the notion that data exerts a gravitational pull in IT systems — and the challenge of transferring on-premises data quickly to the cloud. There's a reason Amazon still offers to ship your data to its cloud on the back of a truck.

But the imperatives of business today mean the cost of not moving to the cloud now often outweighs the cost of doing so. Most CIOs realize that a properly configured cloud is at least as secure as on-premises infrastructure, if not more so. Moreover, there are real business drivers that make analytics in the cloud attractive to CIOs — and more of a necessity than a nice to have. These include:

• Maintaining an on-premises infrastructure, along with the skilled personnel to run it, is costly. Constant hardware upgrades to keep pace with escalating data volumes and compute needs, combined with increasingly complex regulations around privacy and compliance, mean CIOs may find it easier to hand that work over to a cloud provider to focus instead on their core business differentiators.
• Businesses need to adapt faster than ever to the world around them, and the time it takes for IT to prepare data and generate new reports is often too great. The pandemic has shown the need to quickly combine data from internal and external sources to answer questions such as what demand will look like a week from now or how a specific situation — like Covid-19 — will impact suppliers. Answering these questions in the short timeframe needed is possible with a cloud-based platform. 
• AI workloads such as machine learning and predictive modeling are highly variable and compute-intensive, requiring an infrastructure that can scale up and down automatically to match needs. The cloud is far more dynamic and scalable than an on-premises infrastructure, making it better suited to these needs. 

So which analytics workloads will move to the cloud next? Clearly, some enterprise applications are already in the cloud, notably CRM and marketing. B2C companies in particular are using cloud analytics to identify upsell opportunities, guide product development and make personalized recommendations.

I believe the next wave of analytics for both B2B and B2C companies will be in core operational areas that were once viewed as cost centers but can now provide a real competitive advantage to those who innovate first. Here are three areas that are ripe for transformation when business leaders have access to fast, self-service analytics in the cloud:

The recent tariffs and Brexit showed that stability in global trade can no longer be taken for granted, and the pandemic has only amplified that reality. Enterprises need better visibility into supply networks and the ability to pivot quickly when disruption arises. The cloud can make it easier to incorporate third-party data — such as CDC infection rates and local news reports — to understand what's happening on the ground. Armed with this information, businesses can identify changing demand patterns and supply chain disruption sooner, and make smarter decisions about sourcing and supply.

Seeing the impact of transactions in near real time, without needing to run lengthy batch processes, is a holy grail for CFOs seeking to cut costs and manage finances more profitably. We've seen great uses of machine learning and predictive models for estimating late payments in finance. Being able to predict payables and receivables, and more closely manage assets and asset mixes to maximize profit, all benefit from a more agile, elastic infrastructure.

The war for talent and a younger generation of workers that demands more satisfaction at work make human resources an area ripe for innovation through analytics. Applying statistical models to employee-related data can help reduce attrition, improve retention and lead to better business outcomes. HR has remained relatively unchanged for decades, but modernizing operations in the cloud can improve business outcomes by making it easier and faster to slice and dice information in new ways. 

Clearly, not all enterprise analytics will move to the cloud overnight. Businesses continue to have significant on-premises investments, and I expect they will operate in a hybrid model for many years to come. But businesses need the freedom to explore and experiment with data without the burden of lengthy transformations and ETL processes that can add weeks or months for any change to be made. This type of speed and agility is available in the cloud, and the role for CIOs is to identify which workloads in their organizations can yield competitive advantage by taking advantage of a cloud model.

This article previously appeared on Forbes.com.