Who gave the business buzzword a seat at the table?

The term itself arose in the 1940s to describe phrases that had unique, often shorthand value among people across different disciplines, backgrounds, and generations. Buzzwords do have limited use, but as a group of DLC Advisory Board members agreed during a recent call, business and data buzzwords are often more perishable than clear, direct language and can be used to feign deep knowledge on a topic then inadvertently backfire. 

“It looks like we have a quorum so we can double-click on this topic,” said Zak Geis, Executive Director, Software Engineering – Global BI Tech Lead, JP Morgan Chase & Co. “Does that resonate with everyone?”

“I’d say we go after the low-hanging fruit,” said Aisha Quaintance, Vice President Strategic Development at RelationalAI. “Otherwise we might risk boiling the ocean.”

Quaintance admitted that her first few years in consulting before moving back into the AI startup world were a rude buzzword awakening. “We used to play buzzword bingo to get through meetings,” she said. “I remember every time people would present and say they weren’t going to drain the slide – which means to read it start to finish – it got under my skin so bad. Don’t tell us you’re not going to do it: just don’t do it!”

Motives behind the madness

“With my least favorite buzzwords, I find that people are just trying to use other words rather than plain English,” said Caroline Carruthers, Chief Executive at Carruthers and Jackson. “What are we parking when we say, ‘Let’s park that?’ Why do we need to say, ‘let’s circle back’ or ‘touch base’ when we could just say, ‘Let’s have a conversation?’ The amount of people that are focused on making themselves sound smart rather than be understood is shocking.” 

“It’s kind of amazing how ubiquitous these terms are,” said Colm O’Grada, Director of Data at Tines. “They’re well known and well understood and poorly tolerated. I would agree with Caroline, that a lot of this just comes down to just using simple language effectively and not having to overcomplicate things.” O’Grada suggested that the group focus on the big rocks and therefore avoid sitting in the storm of perspective, even though no one was completely sure what this meant.

“One of the things that really upsets me is when in the middle of the discussion people start saying, ‘Oh, let’s take this offline,’” noted Omar Khawaja, Global Head Data & Analytics / CDAO at Givaudan. “Well, why did we have this meeting if you don’t want to discuss this?” He noted that an agile mindset was clearly lacking in getting everyone on the same page in these cases, at which point the group decided to pivot to more specific AI and data buzzwords.

AI: the buzziest current trend

“I often hear people dismissing something in AI because it’s only a hype cycle,” Quaintance continued. “And then people aren’t sure whether something is real or an AI hallucination. Right now I’m stuck on whether something is compound AI or composite AI. I might be too close to it. We’re all trying to stitch the vernacular together here.”

“One I hear a lot these days from consultants is that we just need to ‘put all our enterprise structured data into this GenAI and it’ll give us magical answers,’” Omar Khawaja said. “Another one that makes me laugh is that we need to apply data governance on our unstructured data. Yes, excellent. Great. I would think that first we should build success stories on applying data governance to structured data, but this seems to be something everyone must mention in an AI meeting.”

All participants agreed that responsible AI and ethical AI had sunk into corporate buzzword status and that pulling actionable insights from them would be a heavy lift. 

Data do’s and don’ts 

DLC Advisory Board members acknowledged that data is the new oil, that using “big data” may indicate you’re not quite up to speed, and that many companies’ data architectures were less federated than stitched together. “Getting clients to admit that sort of wraps up the conversation and puts a bow on it,” Quaintance said.   

One buzzword north star the participants aligned around was data fabric or data mesh. “To me these are just new terms for things we’ve been doing for ages,” said Caroline Carruthers. “I feel like we’re just going to confuse everybody by giving it another name.”

“There are actual theories to your original point here,” Aisha Quaintance added.  “These terms had meaning originally, but then they got overused in ways that people who didn’t fully know the original theory just started using them. There’s also a little bit of politics at play since one company or analyst might be trying to get us to buy into their political vision about their data architecture.”

“So much of data is so broad,” said Solomon Kahn, Founder & CEO of Delivery Layer. “What does data governance mean, right? It means something different to everybody.” On the other hand, Kahn noted, a term like “data exhaust” is specific and accurate enough that it isn’t a buzzword. “For those that aren’t in a lot of data monetization space, data exhaust is data that is created as a byproduct from your company that might be valuable for something that’s not your core business.” 

Data wrangling, on the other hand, “has the potential to become meaningless,” Kahn said. “This seems to be true of a lot of these terms. They did have meaning at one point and they’ve lost it as they’ve become overused.”

Does anyone like the optics of business buzzwords?

Colm O’Grada moved to close out the conversation. “I see at least three categories here,” he said:
• There are buzzwords that are overused phrases where you should just use simple language instead.
• There are buzzwords that are new and unnecessary marketing terms for something that we’ve known for a long time.
• There are buzzwords that are just ambiguous terms used in an attempt to make the speaker sound smart. You’re not really sure of what they’re talking about – and the speaker probably isn’t either.

“Anything when you first hear it or use it brings some level of usefulness,” Caroline Carruthers agreed, “but when you are flooded with it, it’s like eating donuts. The first two might be fine, but by the time you hit your tenth donut, you never want to see a donut again.” 

There was much more to be said on this topic, but several data leaders had to bounce due to a hard stop.