I tossed the clothing into the washer, grabbed the non-chlorine bleach, popped off the cap and poured some into the tub with the clothing. My wife, horrified, asked "What are you doing, didn't you measure it?" To which I say "Of course, it was three bloops."
"Three bloops," she recoils in further horror, "are you kidding me?"
"Well, no, look it requires exactly one cup of bleach for this size of load," I explained, then grabbed the measuring cup and turned the bleach bottle on top of it and let the contents "bloop" three times. It made exactly one cup of liquid. It would be one cup of liquid no matter how many times I repeated it. I simply took the shortcut and measured the bloops. An inexact measurement but just as effective. Of course, she wants me to use the measuring cup every time, and cannot imagine going through life with a bloop-here or a bloop-there. I mean, all those recipes in the kitchen with exact measurements. Do we reduce those to inexact quantities too?
A great mathematician once told me that he never figures the exact to-the-penny tip for a wait-staff of a restaurant. If he wants to give fifteen percent, he mentally calculates ten percent by chopping a zero off the whole-dollar amount, then divides this value approximately in half, adds it to the first then upwardly rounds to the nearest dollar. This method is horrifying to accountants, whom I have seen use calculators to figure exact tip amounts. He also told me that when figuring Celsius temperature, he simply subtracted 32 and divided by two. Is this the right "formula"? No, it's supposed to be 5/9ths right? But if all he wants to know is whether to grab a sweater, coat or neither, this is close enough.
I made some chili later that evening. This is a simple reciple. We brown and drain two pounds of ground meat. We then toss this into a two quart container and follow it with three large cans of diced tomatoes. Plus three packets of chili mix. Do I need to read the labels, really? Sure, it calls for a cup of this or several ounces of that. But lets face it, we're after a certain taste and I know that these ingredients in these proportions deliver it. This particular evening my daughter and I were making the chili together and she carefully followed the instructions, but we were left over with half a packet of chili mix plus half a can of diced tomatoes. We can see the pattern here, right? So to her horror I tossed the additional tomatoes into the container along with the mix and started stirring. I don't know if this is a "guy" thing or not. My youngest son was also horrified at my abject disregard for the instructions on the packet. I have tampered with the forces of the universe, you see. Do not deviate from the recipe, lest the earth open up and swallow us all. Or something like that.
Anyhow, when we served up the chili, they ate as voraciously as always and all was well. As a throwback from the days of yore, I add mustard to my helping of chili and use Frito's Scoops to spoon it out. Anyone familiar with Frito-Pie fully understands the connection.
Now one might well ask, what on earth has this to do with enterprise architecture or anything akin to it? In science, aren't we supposed to cross every T and make sure nothing is amiss? Yes and no. Implementations drill on detail. Architecture, not so much.
I listened to a crew of IT admins debate the required size of their new environment. One of them quipped that if we need more space or CPUs, we would need 6 weeks of lead time to order it. After sizing the environment, all agreed that we were at least within 10 percent of the necessary sizing, and this should be good enough. All except for one, who was concerned that if it was too small, we would have to order more. But you have six weeks to decide, right? Well, no, we need to order the hardware now so that it will be here in six weeks when the rollout needs it. Yeah, said another, but if we run into an issue between now and then we just order more. We don't need all of it right way. We're only using about twenty percent of the capacity to begin with. What's the big deal?
And yet, they continued in their paralysis, unwilling to make a committment until one of our rank simply forced them to. In the blink of an eye, all was clarified when everyone present was willing to admit that such decisions have inexact quantities all over them. It's an educated guess. Like a hypothesis. So we're still using science, but we have to make a call and get moving. People depend on it, and time has expired to delay any further.
We see a lot of this kind of in-exactness in data warehousing. Capacity planning especially has percentages and utilization wriitten all over it. A case in point is that we need to be seriously considering capacity upgrades when a system reaches 60 percent of its current capacity. Why is this? Because a red line exists at the 80 percent mark, and above that is reserved for system recovery and workspace. It is not okay to presume that the "last 20 percent" of capacity is available for regular use. It is the red zone. But if we go to the boss and say we are reviewing capacity when the current utilization is only 10 percent above the halfway mark (60 percent) - they may well ask - isn't this a bit premature?
Well, it honestly depends on how long it takes to get the upgrade/transition for capacity underway. If the assessment takes a few weeks, and the designation, procurement, delivery and installation take many more weeks, we have to consider how fast the data is growing. Will the data grow into another 10 percent of the machine by the time we're able to install the upgrade? Okay, then, we're still outside the red zone. But every percentage point above this is one tick closer to the red zone. And we don't want to cross it. Many times I have personally witnessed "perfectly operational" systems simply hang one day. Out of the blue. The processing capacity required for an intermittent spike did not have room to finish. Or an error recovery needed more spill space than it had left to give. Simple things often lead to catastrophic failure when chaos has no place to go. Or for that matter, when the machine cannot dispatch the chaos because it is already too overwhelmed.
A colleague relates that in his data processing shop, the disk space had long since breached capacity and regularly spilled over to tape drives during the evening's processing window. As he described it to me, their environment was using tape drives for runtime workspace! And the CIO could not stop complaining about how long the jobs were taking, but simply refused to buy any additional disk space for the environment. In his mind, the final storage needed exactly 80 percent of the capacity and they were not in the red zone. But weren't they?
In a data processing scenario, the "understood" quantity of disk space runs anywhere from 6x to 8x of the final product's size. So if we are targeting a 1 TB warehouse, we would need between 6 TB and 8 TB of workspace to support it. Whether this is actually hosted on the physical database machine is immaterial if the disk space is shared between database and the external, flat-file world, which is often the case. I recall one instance where I specified 300 gb for a 20 gb warehouse and the manager, himself a warehousing aficionado, raised a strong objection to such a need. When we did the math, I was actually being pretty conservative in the estimate, seeing that we needed to support Development, Testing and Production workspaces, you see. With 60 gb between them, and 6x needed to support each - voila! We have easily breached 300 gb. The punch-line of course, was that they needed to order an additional 150 gb to finalize the project. Oh well, it's just an educated guess.
But if he was willing to give me so much grief over 300gb, imagine what I would have heard for 450gb? The point being, it won't always be true that our bosses will give us, for all configuration lifecycle environment, upwards of 40x what we need in the end - but it certainly gives us insight as to why "all that disk space" seems to evaporate within a couple of months of the project's inception!
Set-based operations, big structured data handling, and now big-data on-the-grid, we will find even more "inexactness" to wade through. I had an interesting conversation just this week with someone who could-not-believe the data being returned by his big-data cluster. Something had to be amiss, he asserted, because "he just knew" that things had-to-be-different. Basically, he'd spent millions on marketing and brand recognition and had expected measureable lift for his product. When it did not arrive, it basically meant that all those millions were spent for nothing. Either that, or he was looking in the wrong place. I asked him if sales ever changed after one of these marketing pushes, and he said no, the marketing pushes were traditionally geared to keep product loyalists from defecting.
So I asked a very impertinent question - how do you know if the marketing pushes are doing anything at all? Wouldn't it be odd to just forego the next marketing push and see-what-happens? This was interesting to him, but simply out of his hands. The engine to create the marketing collateral and the waves of market "push" were ensconced as science in the highest echelons of the company. Asking them to forego even one cycle, and the risk involved in such a thing, could be suicide.
So let's measure it, I suggested. If the quantities are a science and we can know for certain where the lift is, or is not going, we can measure it as a trend. So he set up a number of market "trolls" as it were, to cast the net for information on their products and various trending for competitor products. He pulled these stats daily for the month prior to the marketing push, through the push and for one week thereafter. I warned him that if it measures "nothing" we have nothing to report on. We really need to report on "something" so we can show what directly affects loyalty to the product. He knew of several interesting 'anti'-quantities that could show us, almost in negative terms, whether the marketing push had any value. These are proprietary so I will not share them here. Nonetheless, by measuring these anti-quantities we could see a loyalty trend in a different way. Not when people re-aligned with their products, but when they dis-aligned with them.
This was an interesting graph. It showed that the loyalty to their brands had less to do with their marketing pushes and more to do with the sale-event discounts associated with their competitors. In a comedy of errors, their marketing pushes just-so-happened to be timed when their competitor sale-events were ebbing off, offering the illusion that loyalty was being restored when it fact it was simply re-aligning to its normal center. What if, he mused, they simply delayed the marketing push for some point after the customer loyalty naturally aligned-to-center? This could in fact pull in even more loyal customers, or let them know whether the loyalty push had any value at all.
Last year I ran into my colleague again, some two years after we had first characterized the situation. He told me that after showing all of the metrics, the marketing folks pooh-pooh'd his findings. All except for one, an ambitious soul who had recently been promoted to the second-in-command of the marketing department. According to legend, this person worked with my colleague to distil the right answers, inexact though they might be. Some 8 months later, they finally agreed to offset the marketing push by four weeks to see what the results would be. Three weeks into this cycle, with the upward trends behaving normally even though no marketing push was underway, gave them what they needed to know. The marketing pushes were at best, mis-timed and at worst, completely worthless. They decided to forego the marketing push entirely. Six months later, the trends remained in place without any effort on their part. With a primary benefit: They had saved many millions of dollars in marketing expenses. And by this time they had already begun the process of running an entirely different kind of marketing push, this time on the edge of the peak rather than in the trough of the lull.
So we can see that watching "trends" or "patterns" of gross movement gives us insight into how to attack (or retreat from) the marketplace in ways that make us more competitive. These gross movements are inexact. While we cannot conjure up successfull marketing potions with three-bloops of elixir, the approach to success is not so different. Patterns, swaths, wakes, edges, trends, peaks etc are all inexact measurements we derive from the existing information. But we need to do it on a scale that is impossible with commodity, general-purpose technologies. More importantly, while the detail data drives the final results, incrementally more information may not "move the needle" at all. In fact, just like the measurement of three-bloops - there's very little in how that measurement system will deviate from the center in any signficant manner. It is this "significance" we care about, and why the inexact results and processes to derive them may not be lockstep-perfect, but they tell us what we need to know.