IT automation is paradoxically old and new.
It’s “old” in the sense that various forms of scripting, business process automation, and other technologies have been around for decades. It’s “new” – or newer, at least – in terms of containerization, Kubernetes, infrastructure as code, security automation, robotic process automation (RPA), and the broad landscape cloud-native tools.
Either way, it’s hard to find an IT store taking a “just say no” approach to automation in 2022. On the contrary, there are a multitude of numbers that all seem to say: IT automation is practically everywhere. It’s evident in software pipelines, hybrid cloud infrastructure, security operations centers, and more.
IT automation is no longer just for IT either, as departments within an organization seek to take advantage of tools like RPA – often IT-enabled, but not entirely dependent on it. – as part of their own wider transformations.
While automation has grown tremendously in recent times, it is far from growing. This is an area defined by continuous growth and optimization. Moreover, even the “automate everything” teams continue to optimize and improve.
[ Related read: Automation: 5 issues for IT teams to watch in 2022. ]
Nearly three-quarters of respondents (74%) in In Red Hat’s 2021 State of Kubernetes Security Report said they have adopted DevSecOps. In the same survey, 25% of organizations said their DevSecOps implementation is at an advanced stage that integrates and automates security across their entire software pipeline, indicating plenty of exciting progress and plenty of room. for continued growth.
5 automation tips for IT managers
Whether your team is in the early stages of an automation initiative or an “automate everything” organization or somewhere in between, we’ve selected five expert tips to help you.
1. Start incremental, get strategic
A key part of the appeal of automation is that it can be done incrementally: a Bash script here, an automated web application vulnerability scan there — at whatever pace your capabilities allow.
“It’s automation through the lens of traditional system administrators and even site reliability engineers in many cases,” Red Hat technology evangelist Gordon Haff told us recently. “Do something manually more than once and automate it so you never have to do it again.”
You don’t need a big bang launch; Automation done right will always be a work in progress anyway, so why treat it any other way?
But Haff and other experts note that incremental approaches to automation shouldn’t preclude planning. Incremental makes it manageable; strategy will make automation a success.
Leon Godwin, Principal Cloud Evangelist at Sungard AS, advises creating and using a digital strategy document that outlines the key aspects of your automation approaches, especially in any scenario where automation is a primary levers of an organization-wide digital transformation.
Automation approaches should not preclude planning. Incremental makes it manageable; strategy will make automation a success.
This document should define “the experience they want for their internal and external stakeholders and how that differs from the experience they have today,” says Godwin. “They also need to identify the obstacles that prevent them from achieving this result and what technologies could help overcome these obstacles.”
Your strategy should also identify the expertise, technologies and partners that will be needed to achieve your automation and digital transformation goals, according to Godwin.
Your strategy needs to be nimble and flexible enough to pivot when things don’t go well, says Godwin, which also makes the incremental approach a good choice.
2. Treat ROI as Cumulative and Compound
Successful organizations are obsessed with ROI for a reason: if you’re spending a lot of money on hiring, technology, training, or any other aspect of your business, you want to be able to see (and hopefully , celebrate) the results.
But much of the ROI measurement and discussion takes place within narrow, even artificial parameters, such as on a quarterly or annual basis, or on a per-project (or similar) basis.
Take a long-term view with automation ROI, especially if your implementation is incremental.
Take a long-term view with automation ROI, especially if your implementation is incremental.
“Worry less about the automation ROI you’re trying to tackle,” says Ton Roelandse, managing director of Trexin Consulting.
Roelandse adds that when you break automation down to the task/process level – as you would in many RPA workflows, for example – many might miss a simple ROI test of “time to build automation”. relative to “time currently spent per week per resource.”
But when you group all your manual tasks into different processes, that’s a different story – that’s when you’ll see, as Roelandse puts it, “the light of automation.”
The same basic principle applies broadly to various forms of IT automation, whether in a CI/CD pipeline, cybersecurity strategy, hybrid cloud management, and elsewhere. “Ultimately, it’s the cumulative value of automation that will determine your value,” says Roelandse.
3. Build automation and data skills everywhere
As noted above, automation is no longer just about IT. It is everywhere (or soon will be) in the modern organization.
As the amount of work in an organization done by machines and code grows, there will be a corresponding need for people who can act appropriately on the outcomes of automation, machine learning, and technology. AI, according to Amaresh Tripathy, global head of analytics at Genpact. Most companies are not there yet.
“For most organizations, small teams with specialized training are responsible for making these decisions today,” says Tripathy. “In the digital age, however, this model is not sustainable – all employees must have access to the tools and skills they need to unleash the power of technology.”
[ Learn how leaders are embracing enterprise-wide IT automation: Taking the lead on IT Automation ]
The advent of tools such as low-code or no-code tools that make it easier to spread automation and even start it outside of IT does not diminish this need – it accelerates it, because the success of long-term automation will depend on a wide variety of people who understand the inputs and outputs of increasingly automated workflows.
Genpact itself had work to do to practice what it preaches. “For example, we recently launched an initiative to increase data literacy across our organization and shift employees away from transactional projects toward insight-generating roles,” Tripathy says. “Essentially, our goal is to equip approximately 100,000 employees with the data and analytics tools, techniques, and skills they need to create value for our customers.”
4. Treat data management as more than a technical matter
In highly automated environments, good data management and governance is much more than a technical issue – it can rise to the level of a moral obligation, not to mention regulatory compliance, brand reputation, etc.
“An often overlooked challenge in automation is efficient, secure and ethical data management, especially for companies in highly regulated industries like financial services,” says Tripathy. “It is crucial to prioritize security, compliance and removing potential data biases.”
This is another area that can no longer be the exclusive purview of a relatively small team. The broader and deeper your automation strategy, the more important it becomes to have people in a wide range of roles who understand the importance and implications of the data they are working with.
“This effort requires concerted enterprise-wide alignment as part of an overall data-driven transformation strategy,” says Tripathy. “That means creating clear guidelines for managing data and developing employees for better data literacy with ethical use and regulatory compliance in mind.”
5. Understand how data flows through your organization
You can distill many automation goals – and automation challenges – into two categories: process and data.
Along with the process (which some people may break down into a smaller unit of “tasks” or group together into a larger workflow made up of multiple processes), one of the common requirements is to make sure you have an understanding complete and visibility on the said process. process, both now and later. Otherwise, you run the risk of helping a bad process run faster and more frequently – or not run at all.
With data, one of the common requirements is – wait for it – to ensure that you have full understanding and visibility of that data, now and later.
“Get into any digital transformation exercise by asking the question of how this data will flow through the enterprise,” said Craig Stewart, chief data officer of SnapLogic. “Each vendor is normally only concerned with the direct integrations of their application with others, not the larger picture of all dependent interdependent applications.”
Just as data literacy will be crucial to the long-term ROI of automation, so too will data visibility. In fact, the latter is a prerequisite for the former.
“Somebody needs to have that higher-level view and understand where the potential bottlenecks are in that automated system,” Stewart says. “Make sure there’s a place where it can be seen, and a place where the organization can go to solve any new challenges.”
[Where is your team’s digital transformation work stalling? Get the eBook: What’s slowing down your Digital Transformation? 8 questions to ask.]