Labs: Everything You Need to Know About Software Engineering Intelligence (SEI)

**Dan Lines** (0:00)
You're getting pressure to deliver projects on time. You're getting pressure to take the millions of dollars in workforce that you have, and ensure they're working as efficiently as possible, and then report that back to the board, right? You're getting pressure to make sure that the developer's experience is fantastic, because these folks can work anywhere they want, in high demand, right?
So if you're like the VP and the CTO, I think Software Engineering Intelligenc helps you to push one of your initiatives that would have been very difficult to do without data.

**Conor Bronsdon** (0:35)
Gartner just released their market guide, showing that Software Engineering Intelligenc platforms help engineering leaders significantly improve both team productivity and value delivery.
Through Gartner's in-depth analysis on the critical features of SEI platforms and how they can be used to drive engineering excellence, Linear B was named as a representative vendor, and therefore we're giving away a complimentary copy of Gartner's SEI Market Guide. Head to the link in the show notes to download your complimentary copy and learn how you can unlock the transformative potential of Software Engineering Intelligence for your team.

**Dan Lines** (1:09)
Hey, what's up, everyone? We're back with another Labs episode of Dev Interrupted. I'm your host and Linear B COO and co-founder Dan Lines.
Today, I'll be joined by my co-host Conor B, great to see you as always, Conor.

**Conor Bronsdon** (1:28)
Likewise, Dan, always great to be here and to hear about how badly your Fantasy Baseball team is doing.

**Dan Lines** (1:32)
I know, we were just talking before. We're at five and six.
We're battling for a playoff spot. You and I do Fantasy Football together. I think Fantasy Football is my number one. Fantasy Baseball is more like tie me over until football season starts, but definitely learning a lot about baseball.

**Conor Bronsdon** (1:51)
What I love is that you, not only in your career, but in your personal life, just you love data. You're like, hey, I really want to spend my free time figuring out data around a sport.
In my work life, let's do data around engineering teams. How can I optimize these teams? They perform better. It's almost like you have an obsession here or something.

**Dan Lines** (2:12)
That's actually funny. I think honestly, probably a lot of our audience has seen Moneyball. Moneyball is like what turned me on to fantasy baseball.
Checking the data, looking at the box score.

**Conor Bronsdon** (2:26)
Where's the optimization we can find? People aren't necessarily seeing so far.

**Dan Lines** (2:30)
Yeah, a little nerdy, but yeah, I like it.

**Conor Bronsdon** (2:33)
Well, and I think that drives into kind of the things that we talk about with developer productivity and developer experience are like, what are the workflow challenges that we can find or what are the things that people are under rating? You know, slugging percentage, maybe for an example, for baseball, that people aren't rating as highly as they need to. Our strikeout's a big issue or our long PR is a big issue.
These kind of optimizations.

**Dan Lines** (2:59)
Yeah, I think the cool thing is like, let's say that you're observing baseball or you're observing how your engineering team works and you might have an intuition or a theory. This happens all the time, both in business and sports. I'm watching a game.
And sometimes I'm proven right and sometimes I'm proven wrong. I think a player is getting better.
Actually, the underlying data says that's not the case or vice versa. Something in business is like, oh, I think we might have a problem in this area of the workflow. Actually, the data says we're okay, hit somewhere else. So I think it's also cool to see that, hey, this is what I think's happening. What's the data? Show me that type of thing. So yeah, maybe that's where the fantasy sports addiction comes from.

**Conor Bronsdon** (3:44)
Well, and I know that's the topic for what you want to chat about today, right? Which is, how are we understanding the problems and then solving them that are happening in Software Engineering?

**Dan Lines** (3:53)
Yeah, I think we have a cool topic today.
And I'll start out, I think, by just saying, earlier this year, Gartner released their debut SCI Platform Market Guide. I'm sure most people on the pod know what's up with Gartner.
And I think it's really important because finally kind of like all this stuff that we've been talking about with data, maybe since the beginning of the pod has come to fruition that it actually has like a formal name and is recognized. And it's this thing, it's called SCI, and there's a lot of love for it. Gartner estimates that adoption of SCI platforms, they're poised to increase about 50% of engineering teams will have one of these SCI solutions by 2027 compared to only a 5% increase this year. So it's like hot topic picking up. That's where we're going to dive in today. What is up with this SCI stuff?