Are you looking for real-world examples of enterprise content operations in action? Join Sarah O’Keefe and special guest Adam Newton, Senior Director of Globalization, Product Documentation, & Business Process Automation at NetApp for episode 175 of The Content Strategy Experts podcast. Hear insights from NetApp’s journey to enterprise-level publishing, lessons learned from leading-edge GenAI tool development, and more.

We have writers in our authoring environment who are not writers by nature or bias. They’re subject matter experts. And they’re in our system and generating content. That was about joining us in our environment, reap the benefits of multi-language output, reap the benefits of fast updates, reap the benefits of being able to deliver a web-like experience as opposed to a PDF. But what I think we’ve found now is that this is a data project. This generative AI assistant has changed my thinking about what my team does. Yes, on one level, we have a team of writers devoted to producing the docs. But in another way, you can look at it and say, well, we’re a data engine.

— Adam Newton

Related links:

NetApp Product Documentation featuring Doc, NetApp’s GenAI-powered assistant
AI in the content lifecycle
Technical debt in content operations
The business case for content operations

LinkedIn:

Transcript:

Disclaimer: This is a machine-generated transcript with edits.

Sarah O’Keefe: Welcome to the content strategy experts podcast brought to you by Scriptorium. Since 1997, Scriptorium has helped companies manage structure, organize and distribute content in an efficient way. In this episode, we talk about content operations with Adam Newton. Adam is the senior director of global content experience services at NetApp. Hi everyone, I’m Sarah O ‘Keefe. Adam, welcome.

Adam Newton: Hey there, how are you doing, Sarah?

SO: It’s good to see and/or hear you.

AN: Good to hear your voice.

SO: Yeah, Adam and I go way back, which you may discover as we go through this podcast. And as those of you that listen to the podcast know, we talk a lot about content ops. So what I wanted to do was bring somebody in that is doing content ops in the real world, as opposed to as a consultant.and ask you, Adam, about your perspective as the director of a pretty good-sized group that’s doing content and content operations and content strategy and all the rest of us. So tell us a little bit about NetApp and your role there.

AN: Sure. So NetApp is a Fortune 500 company. We have probably close to 11,000 or more global employees. Our business is primarily data infrastructure, storage management, both on-prem. We sell storage operating system called ONTAP. We sell hardware storage devices, and we are most importantly, think, at this day and age, integrating with Azure, Google Cloud Platform, and AWS on first -party hyperscaler partnerships. My team at DENAP is… I actually have three teams under me. The largest of those three teams is the technical publications team. The other two teams globalization responsible for localization translation of both collateral and product. And then finally, and most new to my team is our digital content science team, which is our data science wing. Have about 50 to 53, think, employees at this point in my organization and all told probably about a hundred with our vendor partners.

SO: And so I think we all have a decent idea of what the technical publications team and the globalization teams do. Can you talk a little bit about the data science side? What does that team up to?

AN: Yeah, that’s a thank you for asking that question. So about two years ago, I was faced with an opportunity to hire. And maybe some of your listeners who are managers are familiar with that situation, right? I hope they are, rather than not being able to hire. I took a moment and thought a little bit more about what I needed in the future. And I thought a little bit differently about roles and responsibilities, opportunities inside NetApp and the broader content world and decided to bring in a data scientist. And then I thought a little bit more about, well, there are other data scientists at NetApp. Why would I need one? And I thought a little bit about the typical profile of the data scientists at that time at NetApp, mostly in IT and other product teams. Those data scientists were primarily quantitative data scientists coming from computer science backgrounds. And I thought, well, you know, we’re in the content business. I want to find a data scientist who is a content specialist and who has a background in the humanities and who also has skills in core data science skills, emphasizing, for example, NLP. And so that was my quest. And I was very, very fortunate to find a PhD candidate in English who wanted to get out of the academy and who had these skills. And it’s been an incredible boon to our organization. We’ve even hired a second PhD in English recently. And Sarah, since you and I are friends, I’ll say one was from UNC and one was from Duke. Okay. So we don’t have to have that discussion here. I’m an equal opportunity person. Although I did hire the UNC one first, Sarah.

SO: I see, I see. So for those of you that don’t live in North Carolina, this is… I’m not sure there is a comparison, but it is important to have both on your team. And I appreciate your inclusion of everybody. It is kind of like… I’ve got nothing.

AN: Yes.

SO: Okay, so you hired some data scientists from a couple of good universities. Or do they get along? Do they talk to each other?

AN: Fabulously, yes. No petty grievances.

SO: Okay, just checking. All right. So how do you, in this context then, what does your environment look like? What kinds of things are you doing with the docs team? And what’s the news from NetApp docs?

AN: So maybe a little bit of background actually, and you and I have talked about this previously, but we used to be a data shop. And then as things sped up inside our business with the adoption and development of cloud services at NetApp, we found that some of the apparatus of our data infrastructure, our past practices weren’t able to keep up to speed of the cloud services that were being developed. I think this is actually, I’ve talked to other people in our business, this is a very common situation. We handled it in one way. There are many ways to handle it, but the way we chose to handle it was to exit data and to move in our source format anyway to a format called ASCII doc, which I always frequently describe as a dialect of markdown. And we went from being a closed system of technical writers working inside a closed CMS to adopting open source. We now work in GitHub. Our pipeline is all open source and we have now contributors to our content that are not technical writers. In some cases, they’re technical marketing engineers, solution architects, and so forth as well as a pipeline of docs that we build through automations where we, for example, transform API specifications or reference docs that are maintained by developers and output those into our own website docs.netapp.com. In addition to just the docs part, my globalization team has been using for many years, machine translation. So speaking to one particular opportunity of being in one organization, when we output our docs and whenever we update our docs in English, they’re automagically updated in eight other languages and published to docs.netapp.com. So we roughly maintain 150,000 English files and you can times those by eight. Is that right? Did I do the math right? Yeah.

SO: Or nine, depending.

AN: Nine. Yeah. Is English the language? Yeah, sure. Let’s count it.

SO: Depends on how we use it. Okay, so you have an ASCII doc, you know, Markdown-ish. Is it fair to call it Docs as Code environment?

AN: So we often describe it as a content ops, environment. I’m not sure if that is, different from Docs as Code, but I think maybe I will accept that as a reasonable description in the sense that, we have aske...