Transformation is the word in IT right now, but not only for the reasons you might assume.

Yes, companies are accelerating their digital transformation efforts due to the pandemic with automation, machine learning, APIs, and modern data analytics. But there’s another kind of transformation underway that’s just as significant, but not as discussed: role transformation in the IT organization.

How Are IT Roles Transforming?

To sum it up, there’s a growing need for multi-role, IT generalists in businesses. There’s still room for specialists, but even they should be prepared to act as a “Swiss Army knife” when needed. That is, they must be able to quickly apply (or at least identify) whatever tool or knowledge is required to problem-solve and bring new ideas to life quickly.

I’ve been observing the role transformation trend for a while with database teams, in particular. I thought it would be an ideal topic for deeper exploration at our recent Pure//Accelerate® Digital conference. I asked my colleagues Nathan Hall, VP of Worldwide Systems Systems Engineering; Marsha Pierce, Director Field Solutions Architecture; and Jon Owings, Director, Cloud Architecture, to share their perspectives on the factors driving IT role transformation in a session titled, “DBAs and IT Admins of the Future: The Swiss Army Knives of the Data Center.”

Following are the key takeaways from the session, along with a few recommendations for how IT practitioners, like database administrators (DBAs), system administrators (sysadmins), and IT administrators can ensure they stay relevant in a rapidly changing environment.

Complexity Is the Enemy of Specialization

The cloud has created an intolerance for IT infrastructure complexity. And the truth is, businesses today just don’t have time to wait for an IT specialist to throw down a slew of esoteric command-line calls to provision storage or keep things up and running. Instead, they need their entire IT staff to move as quickly as possible to help the organization implement new technologies and keep transformation progressing.

Also, automation of manual processes is decreasing operational complexity. This is further reducing the need for specialization within the IT organization. Consider DBAs whose workdays are no longer consumed by repetitive, specialized tasks like testing backups and restores or managing tables and indexes. With automation, DBAs have more time to engage in value-adding work for the business, like architecting, problem-solving, and strategy.

Silos Are Quickly Falling Away

Cloud and automation, and even analytics, are also helping to break down traditional silos in IT and increase consolidation and collaboration across teams. For example, in many organizations, storage teams and virtualization teams are now one in the same. DBAs and IT admins are also getting together more often now to discuss data management strategy.

And to defend against and recover quickly from cyber threats like ransomware, pretty much everyone in the IT organization needs to work together closely and often to help identify potential security gaps and reduce risks, from misconfigurations in cloud deployment to vulnerabilities in application development. (Hello, DevSecOps.)

App Development Cycles Keep Accelerating

Agile development practices led to DevOps, which eliminates the barriers between development and operations teams. And DevOps is definitely an area where we started to see the shift away from IT specialization early. The DevOps practice demands that IT pros create a “Swiss Army knife” skill set that allows them to handle various functions within development and operations.

Now, IT teams, from DevOps to SecOps, find themselves intersecting and needing to broaden their skill sets even more as the business expands its use of containers and Kubernetes to reinvent how it builds and runs applications, and accelerate development cycles even more.

That’s why the specialists on the database team, like SQL or Oracle DBAs, are starting to feel more pressure to evolve their roles. And, rightly so. If they don’t understand how to run a database in a container, integrate databases with microservices, and troubleshoot common issues related to containers and Kubernetes, they aren’t likely to remain useful to their organization for much longer.

Here’s some more pressure: While more than 90% of applications today don’t use containers, 95% of new applications do.¹ And Gartner predicts that by 2022, more than 75% of global organizations will be running containerized applications in production, up from less than 30% today.² Also, as traditional enterprise apps reach the end of their life span, you can be confident that most, if not all, will be replaced by microservices architecture.

Future-Proofing Your IT Career: 5 Recommendations

The above trends are just a few reasons we’re seeing role transformations throughout the IT organization. What can IT pros, like DBAs, sysadmins, or IT administrators, do to ensure they can ride this wave of change, and not get washed away by it? Here are five recommendations that my colleagues offered the audience during the Pure//Accelerate® Digital session:

  • Really understand what’s in the cloud computing stack. Businesses are relying more on cloud services like SaaS, PaaS, IaaS, and even STaaS to simplify their infrastructure, build resilience, support remote teams and so much more. IT teams need to know what these services do and how to work with and get the most from them to help the business meet its goals— it’s as simple as that.
  • Learn NoSQL. Unstructured data, from text messages to spreadsheets to images, makes up more than 80% of enterprise data today, and it’s growing at the rate of 55% and 65% per year.³ Most businesses really want to harness insights from this data to understand their customers better and make strategic decisions. And to work with this data, they need NoSQL databases.
  • Become a data scientist. OK, so you don’t have to go back to university and earn a degree in data science (unless you want to). But IT pros should understand how large data sets, unstructured and structured, work. These data sets are only going to keep growing as the Internet of Things (IoT) continues to expand. Data science knowledge also makes you better at working with data, generally—and if data analysis isn’t part of your job now, it likely will be soon. So, look for online courses that can help you gain baseline knowledge in data science, and from there, consider earning in-demand certifications that can set you apart in your field.
  • Build container technology knowledge. As noted earlier, a SQL DBA who doesn’t know how to work with containers is on the road to trouble. It will be well worth the investment in time to learn how to run a database in a container, step by step. Really dig in to understand how the data works in containers, too. And as you increase your knowledge, keep growing it. This is still a new area, which means there’s a lot of opportunity to become an expert. There are also plenty of resources available online to learn about containerization. (And while you’re at it, take time to learn about cloud-native Kubernetes, too, which is becoming only more important for agile development and application and microservices delivery).

One final recommendation for future-proofing your IT career and meeting the challenge, and opportunity, of role transformation is to consider up-leveling your skill set to align more with that of a data architect. Data architects can translate business goals and requirements into specific database solutions. They can visualize and design the data management framework, standards, and principles for an enterprise. But their knowledge extends beyond data modeling and design to many other areas of IT, including network management, security, application architecture, and IT performance management.

In other words, they’re a Swiss Army knife.

    1. https://go.451research.com/2019-mi-kubernetes-cloud-native-mainstream.html
    2. https://www.gartner.com/en/newsroom/press-releases/2020-06-25-gartner-forecasts-strong-revenue-growth-for-global-co
    3. https://www.datamation.com/big-data/structured-vs-unstructured-data/