This is a rant. Fair warning.
I guess the new ‘Millenial’ colloquialism for “grumpy” or “sarcastic” is “salty“. So I’m feeling extra salty this week. For several reasons. One, it’s audit season. Two, I had to churn out about a dozen new reports in the span of 4 days because the manager who was supposed to be tracking that project dropped the ball and forgot they were due by the end of this month until… yeah, last Friday. Wheeeeee!
Thus, I decided, my current ‘velocity’ (a SCRUM/DevOps term for “how much work are you getting done”) shall be measured in FPH – Facepalms Per Hour. Currently I’m at 3. Earlier this week I was approaching the double-digits, when the lovely report consumers kept thinking of “just one more little thing” they forgot about until after I’d delivered the ‘final’ product.
‘Final’ actually being a meaningful adjective in this context approximately NEVER.
How best to describe this scenario while still maintaining separation of “real job” from “blog land”… Hrm. So let’s say we have a CRM, like most companies. This stores customers, among other things, in a database. And since it also stores sales transactions and financials, it’s heavily audited — it has a lot of change-tracking mechanisms.
Now, auditors come along and want a report of some specific type of change over time. I happily oblige. Then… PANIC! And not at the disco. “What are all these changes to these customers by these users who don’t have permission to make said changes?!?”
K, calm down sparky. Try not to sound the alarm; auditors are a sensitive bunch.
Turns out, those changes are, in a word, “fake”. You see, there’s this background “customer sync” process that keeps them up to date with another part of the CRM where the actual changes were made. But, because it’s written poorly, it thinks that ANY field change, even just the Name or Address (which a lot more CSR’s, customer service reps, have the permission to change, because, you know, that’s their job), constitutes a change to the ENTIRE customer record on the other end. So the change tracker logs a change to every single field on the receiving end of that sync process, even though nothing really changed on the source side except maybe one or two fields.
With me so far? Great. So now the question is, “Well, can we get a report that doesn’t show those ‘fake’ changes?” But wait, it has to be “system generated” and you’re not allowed to “filter” or “add special exceptions” to it, because it still needs to be audit-able.
So what you’re saying is, give me a report that shows me what I care about, but you’re not allowed to change the logic behind said report.
So I give them a new report. I don’t explain how the sausage is made, I just make it and serve it up. “But why is this different from the original report?”
Well, do you want the audit-able answer, or the real answer? The audit-able answer is, “We made a system change that allowed us to prevent the ‘fake’ changes from being logged incorrectly.”
The real answer is, “B*tch, I AM the system!” — meaning yes, I excluded those with some hacky logic, and you need to stop asking questions about it.
Anyway. Change Logs are super fun.
Speaking of reporting. I could really go on for pages about how terrible and broken this whole system of “request-based report development” is. But it’s frankly all we have right now. Until there’s sufficient business buy-in to the concept of agile data warehousing and collaborative cross-functional data modeling, shit just comes in one funnel and goes out another with a little sparkle spackled to it. And we call it a report.
Example, you say? Sure! Let’s say we run a special sale on certain types of widgets every quarter. We want to track how these ‘specials’ perform — do they increase our sales of those widgets? By what factor, compared to the other not-on-sale widgets? Can we trend this over several quarters?
Oh but wait. The data structures that govern widget pricing and time-span-based sale pricing, and the logic that relates customer orders to what pricing structure they used at the time of ordering, is awful, terrible, and changes every time there’s a new quarterly promotional sale.
So you’re saying you want a report that trends sales of widgets based on arbitrarily changing promotional pricing as compared to other widgets that may or may not be subject to ‘normal’ pricing during that same time period, all without a simple definitive data-point that says “This is a Quarterly Promo sale, and That is Not.”
Let’s try to get at the root of the problem, shall we? The business doesn’t seem to understand that the way they implement promo-sales is detrimental to long-term/comparative reporting. The data model makes this harder, not easier. Can we perhaps put some heads together and come up with a compromise that both A) makes more business sense, and B) improves the data model to be a bit more intuitive?
What’s your FPH? What causes you to facepalm on a regular basis? Let me know in the comments! :o)