Managing the demands and challenges associated with the ongoing rise in data will require data centers to undergo a digital transformation.

While assessing, maintaining, optimizing and integrating their assets, data center owners and operators must also consider the technologies they’ll need for ongoing efficiency, effectiveness and sustainability.

But, according to Gartner’s hype cycle, it’s possible to be both too early and too late in adopting these new technologies.

The question is, then, when should the decision be made?

Fundamentally, it depends on the nature of the decision. For example, growing environmental concerns mean that if a decision isn’t made around balancing performance against sustainability now, it’ll be too late.

There are other cases, however, where continuous testing and evaluation means the decision to accelerate can be made at the right time.

For instance, deciding on the level of autonomy you want to institute is a very complicated question. Take autonomous vehicles as an example. Full autonomy means a car no longer needs a driver. But most people aren’t ready to sit in the backseat while the car drives itself.

Despite this, it’s already happening in San Francisco, with the rollout of fully autonomous taxis. Time – and adoption of these taxis – will tell if the decision was made too soon. Your decisions about what level of automation you will need requires some thought and experimentation.

Feasibility, viability and desirability

Data centers are increasingly moving toward automation. Currently, though, some level of human monitoring of the operational environment will occur.

There are cases where an operator will need to perform manual tasks such as walking around with a clipboard to check data.

In other cases, the center’s control systems will provide single-device control, like remotely running a generator test.

Even when the control systems provide a single, automated system for multiple devices, an operator will still need to monitor every task and can take control at any time.

As in the autonomous vehicle example, there is perhaps a sense of unease about the idea of moving to automation that can monitor the entire operational environment.

While conditional or high-level automation means the system can perform most, if not all, operational tasks, certain conditions, including the option for operator override, must be met.

With full automation, on the other hand, the system will perform all operational tasks under all conditions with no requirement for operator attention or interaction.

So, we know that autonomy in the data center is both feasible and viable. But is it desirable? Progress in the field of automation requires us to separate the facts from the fears to determine what is possible, and then help people build trust in the data insights and decision-making this autonomy enables.

Trust the data

This last point is true for any accelerators a business is looking to drive. Whatever that accelerator is, it must be technically feasible, it must be viable from a business standpoint – driving a specific outcome, value and results – and it must be desirable.

Are people ready and willing to embrace it? If not, why not? Are they concerned that they’ll lose their jobs or worried that the machines will take over?

This is why it’s important for people to trust in the decisions being made and in the elements that lead to those decisions. It’s important to trust the data and the analytics, the people and their decisions, and the machines and their outputs.

Only by trusting in the data that’s being accessed and analyzed, along with the decisions that are being made and the actions that are being taken as a result, will it provide value – value that will offer credibility which will in turn build more trust over time.

Ultimately, if people don’t trust the data, it’s unlikely they’ll be willing to let that data help them in their decision-making.

The question is how valuable people perceive that data to be. If they trust it, then it’s hugely valuable, and that’s the point at which they will embrace and implement full automation without fear.

Running the right analysis

What, then, is needed for people to trust the value of the data within their organization?

From disaster avoidance to data center and distributed infrastructure management, smart data analysis for things like predictive health monitoring, alarm rationalization and power topology verification will help data center operators gain operational insights into their complex, mission-critical infrastructure, allowing them to make data-driven decisions.

By collecting the right pieces of data in a single platform, organizations can then run the right analysis on that data to help them make the right decisions while optimizing and accelerating their business.

Demands on data centers are becoming ever more complex. Decisions must be made around which technologies to adopt to meet these demands.

But, as Gartner suggests, the timing of that adoption must be right for the business and its workforce. Trust in data-enabled decision-making is paramount to knowing when to accelerate, and when not to. Get it right, and your business will be prepared for whatever the future holds. Get it wrong, and you could be left behind.

Find out how Eaton’s Brightlayer™ Data Centers suite software can enable data center operators and distributed IT teams to make data-driven decisions using deep operational insights.