Every IT leader knows the feeling: it’s barely Monday morning and things are already on fire. Payroll won’t run, the customer portal is timing out, dashboards that the exec team lives in are stuck on “loading,” and nobody can quite explain why. On the surface it looks like random bad luck or plain old “IT breaking,” the kind of pile-up you might have seen described in pieces like Glaad Voice’s own look at everything failing at once and the confusion that follows, which feels uncomfortably familiar if you’ve ever sat in an incident bridge at 8:05 a.m. with coffee still in your hand. IT breaking Very often, though, the real culprit isn’t a single heroic failure. It’s a hidden, badly understood web of dependencies behind the scenes, the sort of thing you only start to see clearly when you step back and really dive into depedency mapping instead of guessing at what talks to what in your environment.
Weak Spot #1: Invisible Chains Between “Independent” Systems
On an org chart, your systems probably look nicely separated: CRM here, billing over there, analytics in a different box, HR tools off to the side. In reality, most modern stacks are closer to a bowl of spaghetti than a neat diagram. Your “independent” apps share identity providers, message queues, databases, DNS, logging pipelines, and a pile of tiny services that nobody outside the team has heard of. So when one of those apparently low-risk components goes sideways, three or four business-critical services can fall over in sympathy, which is why you sometimes fix one thing and five different teams suddenly start sending you very panicked messages. The real problem is those invisible chains, the quiet glue that holds everything together until it doesn’t.
If you zoom in on the guts of these systems, you’re really just looking at layers of software dependencies that have piled up over time – libraries, services, APIs, cloud resources – each one necessary for something else to work, and each bringing its own risk when it’s ignored or left undocumented. That’s where dependency mapping becomes less of a buzzword and more of a survival skill: the job is to build a living picture of which services rely on which components, which data stores sit under which applications, and what an everyday user journey actually touches when they “just” log in and run a report. You don’t need a perfect Hollywood-style architecture map, but you do need something better than “ask Tom, he probably knows.” The quickest way to start is to take one or two key business journeys – say, “customer signs in and places an order” – and trace every system call and shared resource along the way; once you’ve done that a few times, you’ll usually discover more shared bottlenecks than you were comfortable imagining.
Weak Spot #2: Shadow IT and “Temporary” Tools That Never Went Away
The second weak spot is sneakier because on paper it often doesn’t exist at all. Almost every organization has a layer of “temporary” fixes that became permanent without anyone really deciding that they should. A developer spins up a small VM to host a script that reconciles invoices overnight, and it works so well nobody ever replaces it. A marketing team puts a SaaS analytics tool on a personal credit card “just to test it for a month,” and two years later the CEO’s favorite funnel report still depends on the data flowing through that account. A data analyst builds a Google Sheet that quietly powers a weekly operations meeting, with no logging, no backup, and no ownership outside their head. None of this feels dangerous until the day the person who set it up leaves, or the vendor changes pricing, or the one old VM finally dies and everyone slowly realizes that a crucial piece of business logic was running there all along, held together by duct tape and habit rather than any real governance.
Spotting this weak spot means deliberately looking beyond the neat, official inventory of “approved” systems and asking people what they actually use to get work done. Simple questions like “which tools, scripts, or spreadsheets would completely ruin your week if they disappeared tomorrow?” are often more revealing than any audit spreadsheet, because they surface the unofficial glue people genuinely rely on. Finance might admit there’s a weird script someone’s “been meaning to replace for ages,” support might reveal a small database that stores edge-case customer flags, operations might lean on a third-party scheduling tool that never got routed through IT. Once you know about these things, you can start pulling them into your dependency view, giving them owners, monitoring, and a plan B, but until then they sit in the dark, quietly increasing the odds that the next big outage starts in a place nobody thought to look.
Weak Spot #3: Change Without Understanding the Blast Radius
The third weak spot shows up every time teams make changes in isolation, assuming that a local tweak will have only local consequences. In reality, every change has a blast radius: change a firewall rule and you might be blocking some obscure but essential API call; upgrade a database and you may unintentionally break the one legacy app that still speaks an older protocol; decommission what looks like an unused server and you could be killing the monthly report that finance needs to close the books. You see this in the real world whenever there’s a wave of SSO outages and people discover just how many services were quietly depending on one login provider, even though it felt like “just another cloud tool” yesterday. The core issue isn’t change itself – you can’t freeze a modern stack in place – it’s changing things without a shared view of who else relies on the thing you’re touching, and how quickly they’ll feel the pain if you get it wrong.
Reducing this risk doesn’t require a 200-page change-management policy that nobody reads, but it does demand a few disciplined habits tied straight back to your dependency understanding. Before making a non-trivial change, someone should be able to answer basic questions like: “Which business services depend on this component?”, “If this is down for an hour, who calls first and what exactly breaks for them?”, and “What’s our rollback plan if this behaves differently in production than in staging?” It sounds almost too simple, yet a surprising number of post-incident reviews end with some variation of “we didn’t realize that service still used that,” which is basically an admission that the blast radius was a guess, not an informed estimate. Over time, if every significant change either confirms or corrects part of your mental map, you slowly shift from guessing at impact to making changes with your eyes open, and incidents stop feeling like pure bad luck and more like managed risk.
From Surprise Outages to Informed Risk
None of these weak spots are going away. As long as you’re adding tools, shipping code, plugging into new platforms, and trying to move faster than last quarter, you will keep accumulating dependencies, shortcuts, and odd edge-cases that defy whatever neat architecture diagram lives in the wiki. The point isn’t to chase some imaginary state where every system is perfectly documented and every connection labeled; that goal will drive you mad and probably slow the business down anyway. A more realistic aim is to shrink the number of nasty surprises – the mornings where three things fail at once and nobody can even agree where to start looking – by building a rough, honest map of how your stack actually hangs together and keeping it alive as you go. If you treat every major outage as an opportunity to add one or two missing links to that picture, and every meaningful change as a chance to check your assumptions, your IT stack doesn’t suddenly become simple, but it does become understandable, and that alone is often the difference between chaos and a messy system you can still manage.
