Posts Tagged ‘onboard’
Now, given what we do—or are at least perceived by the world at large to do—I should probably qualify that, huh? Honestly, though, I think the statement can stand on its own. While data seems like it’s useful, it’s trash, and this fact causes me no end of angst. We’re constantly referred to as “data providers”, even by members of our own team and marketing collateral, but in actuality we do not provide data.
We don’t provide data because it’s useless, meaningless, without value. Data is a collection of unrelated facts, the “product of observation”, with no meaning beyond its own existence. At their most basic levels, our products and services provide information, and go up from there. We’re information providers, and—much more importantly—knowledge providers. If you’re a data-geek, those distinctions and the DIKW “knowledge hierarchy” concepts probably aren’t new and the next bits are going to seem a bit “Applied Information Science 101″ to you—go on and bail, my feelings won’t be hurt. If you’re not a data-geek, but interested enough in information science for business or other reasons to be reading this blog, it’s probably a good distinction to start making. Note: if you are a data-geek and you haven’t read Ackoff’s “From Data to Wisdom”—go read that instead of this.
- I have three Things.
- One of the Things is reddish-brown, two Things are grey.
- One Thing weighs about two tons, one Thing about 10lbs., and one about 20g.
- One Thing has a trunk, one Thing has a tail, and one Thing has a publicist. Yep, a publicist.
No fair drawing correlations and/or conclusions yet! “Data” is exactly what you see there in that pile. We now have some facts (and even that’s an assumption at this point) about three “Things”. That’s it, no more, no less. That’s data. See what I mean? Useless. So if data is useless, what the hell are we doing? Well, if you take data and apply some processes to clean it, standardize it, and create some relationships between its constituent bits and pieces, you get information.
|1||reddish-brown||10lbs.||has a publicist|
|2||grey||20g||has a tail|
|3||grey||2 tons||has a trunk|
I’d argue that this stuff—information—is only mildly less useless than data, but it’s a start. It’s organized and has at least the potential(!) for allowing us to manufacture knowledge from it. It’s important only because if you get this part wrong, then any derivative knowledge is also suspect. Truthfully though, unless you know what you’re doing this stuff is almost more dangerous than raw data (more on that in a minute). The only reason we provide it in this form is because some of our customers have the desire and acumen to manufacture their own knowledge, and just want to make certain that they have the very best raw materials for doing so, and advice on the best way to go about the process.
But that begs the question, what is knowledge? Basically, you take your set of information and apply a cognitive process to it, one which actually draws correlations and conclusions, hypothesizes causal relationships, etc. This is done using a variety of mechanisms, which all boil down to human analysis. Algorithms, models, simulations—at the end of the day it’s just what some human being or a group thereof decided would be a useful way to process information into knowledge, signal from noise.
|1||reddish-brown||10lbs.||has a publicist||tabby cat|
|2||grey||20g||has a tail||mouse|
|3||grey||2 tons||has a trunk||elephant|
Well, that’s much more useful! It tells us what each of the entity-instances (records) is, and some of their attributes (fields). Feeling warm and fuzzy, now? Here’s the punchline: this last table, the one describing the knowledge we rendered from the information, which was in turn cobbled together from the data…as described herein, it has the potential to be both incorrect and incomplete. Remember the old adage about “it’s not what you don’t know that messes you up, it’s what you know that isn’t so?” I’m paraphrasing, of course.
So how could our example be wrong? In the knowledge set we drew the, not unnatural, conclusion that #3 was an elephant. What if it’s a Chrysler 300? That fits the available information (grey/2 tons/trunk). It could be something else altogether, though. How might our example be incomplete? In #1 we correctly assessed the “Thing” to be a tabby cat, but failed to differentiate it as Morris the Cat (ergo, the publicist)—a fairly important piece of knowledge, and a conclusion that might have realistically been drawn by a sophisticated enough model. Now take it up a step, to the information. What if the aggregation process failed and the #2 record has the trunk, #3 the tail? Well the probability that #3 is, in fact, an elephant just increased. But maybe #2 is actually Stuart Little, or Fievel. I mean, how many other mice do you know with trunks?
Which brings us to wisdom. Wisdom is basically a local phenomenon—strangely topical given that the focus of recent conversations in the RE.net seems to be revolving heavily around localism as the most significant agent/broker value proposition. I’ve heard it phrased as “local knowledge”. Not to belabor the semantics, but I feel the phrase “local wisdom” is more applicable.
I mean, we have knowledge. From evaluating the information Onboard organizes from the data that we aggregate, I “know” that the schools are “great” in an area, and maybe I can therefore help home-buying parents find a starter home. The local agent, though, can tell them that the HOA for the home they’re looking at just voted in a real PITA who hates kids and doesn’t let them ride their bikes without sign off in triplicate, and that speeding seems to be a problem. You probably won’t find that in our databases. Yet .
For the purists, I know I skipped Ackoff’s “Understanding” layer—formally defined as the “appreciation of why”, as opposed to “who, what, where, when, and how”, and nested between knowledge and wisdom. This is by design. First, the common wisdom (loaded word in this context) in information science circles seems to be to steer away from some of the more…metaphysical aspects connoted by his treatment of the subject. Second, if you look at my treatment of the “Knowledge” layer you’ll see that I tend to combine in the one layer both the deterministic processes defined by Ackoff’s version and the probabilistic/interpolative processes he espouses for his “Understanding” layer. I don’t really see the benefit in a separation, and actually feel that the cognitive processes involved are complimentary enough to warrant combination as a matter of course. And if he doesn’t like it, he can just come find me, huh? Battle Royale!
What’s the point of all this? The point is that data is useless, information is only as good as the systems and assumptions used to process it, and the quality of a knowledge set is a factor of both its constituent information and the cognitive processes used to manufacture that knowledge. Ultimately, the way you determine whether a “data provider” is worth a damn is by looking at the people who make up the team which aggregates and organizes that data into information, and whose grey matter and diligence is responsible for transforming that refined information into useful knowledge.
Final note: this shouldn’t be construed as the only legitimate treatment of knowledge management, or as a comprehensive description of our thought processes at Onboard. In many ways this methodology is limited, and doesn’t—at least not intuitively—take into account the dynamism inherent in knowledge of any significantly useful complexity. My intention was to use this as an introduction into the amount and nature of thought that goes into creating knowledge, and identify the sharp difference between a product and “data”. Data may be fungible, but knowledge…is…not. And, knowledge-wise? I’ll put our team up against anyone else’s.
As for wisdom? It’s probably overrated, and almost certainly to remain a uniquely ineffable human endeavor. We’re working on it though. This’ll have to do for now:
Information is not knowledge
Knowledge is not wisdom
Wisdom is not truth
Truth is not beauty
Beauty is not love
Love is not music
Music is THE BEST…
Wisdom is the domain of the Wis
- lyrics from Frank Zappa’s rock opera, “Joe’s Garage”, Act III, Scene XVI
Falling within the demographic group of real estate’s red-headed stepchild, ages 18 to 34, I felt it necessary to defend my brethren from a realtor that clearly doesn’t understand our relevance. Now, I’m no pied-piper, I’m not a leader of men, but I do know bs when I see it. I think that over the next 7 years, 18 to 34 year olds are going to be buying a significant amount of houses; and, subsequently, realtors should pay us more attention than we’re currently getting-which is where Mr. Brady and I differ. . .
You’re just back from Inman Connect? Forget everything you heard there. Chasing the hip, young 18 to 34 market is gret if you’re selling sneakers but could be detrimental to the health of YOUR business for the next 7 years. here’s why:
[Me] I disagree with my grammatically ignorant friend (“ain’t,” really?), to quote Jay-Z, “I’ve got ninety-nine problems but my bank account isn’t one” . . . or something like that. The monetary situation of 18-34 year olds is what it has been for the last few years:
· “According to the report, members of Generation Y command more spending power than preceding generations at the same stage of life “because they are well-educated and have higher starting salaries out of college.””
· In 2006: “first-time buyers, the median age was 32.”
· The long-term demand for second homes looks favorable because there are large numbers of people buying second homes. “Currently . . . 40.9 million are between ages of 30 to 39. These younger segments will drive the second-home market over the next decade.”
Don’t get me wrong, I’m not saying that my age demographic is the only action in town; but to say we “ain’t got no money” is not only offensive it’s just plain wrong. Maybe we don’t have as much money as some other age groups; maybe we don’t have as much money as Mr. Brady would like, but how much money do we need in order to be perceived as “worthy of a realtor’s time?”
2- They don’t trust real estate as an investment. This demographic believes that real estate is either perpetually overpriced or that it is dangerous. Some eschewed the asset class, some leveraged it irresponsibly and lost. It’s not that they don’t trust you because you’re a shady REALTOR, they don’t trust your product.
3- They view you as a functionary. Your value hasn’t been established to them because they haven’t had good experiences with real estate. They see you as an over-priced clerk because they watched you make “easy money’ while they chased the overpriced asset.
[Me] I’m addressing both #2 and #3 together because I think they both reek of bitterness. In #2, I think things are a bit backwards. We trust real estate as an investment; we just don’t trust shady realtors. And I think that the mistrust derives from the difficulties, the dragging of feet, that has occurred within the RE community in regards to accessible community data and housing listings on-line. If it’s not public: it’s a secret, it’s private; it’s trespassing; it’s not cool; it’s irritating to a generation of people that (for the most part) don’t know what the world would be like without the internet. And despite all the red tape:
In #3, I think someone sounds like their in desperate need of a hug. If it makes you feel any better, I value you Mr. Brady. I wouldn’t buy a house without you and I don’t think I’m alone:
[Me] Do we really need an education? If old is now new in the world of lending (with FHA mortgages—a.k.a the first time buyer mortgage—making a comeback) I have to wonder what age group would be fueling such a trend. Because I would assume, if Baby-Boomers wanted to purchase more real estate, they would walk across their summer home’s beautifully landscaped lawn and shake their money tree . . . No lending necessary!!
5- They really don’t have any “pain”. They’ll be focusing on mitigating losses rather than maximizing wealth. Their “pain” is best served by loss mitigation specialists and not wealth maximizers.
[Me] I can’t believe that such a broad generalization could actually be conceived as sound reasoning.
So…if that’s true, why the hell are you screwing around on Facebook and Twitter? Because the fastest growing user groups on those two social networks are the cheese, baby…the 45-65 age group.
[Me] Erroneous!! One particular month is not indicative of Facebook’s overall growth last year. In fact, if you read the article hyperlinked in Mr. Brady’s post, and check the original press release it references, you’ll see that “The most dramatic growth occurred among 25-34 year olds (up 181 percent), while 12-17 year olds grew 149 percent and those age 35 and older grew 98 percent.” Baby-Boomers were the 2nd slowest growing user group. And Twitter never even came up.
PS: I’m generalizing when I categorize the demographic groups. There are a lot of successful and responsible 18-34 year-olds but your odds are better with their parents until 2015. The cool part is that 80% of your competition will buy into the Youth Myth while you clean up on the Boomers.
[Me] I tip my cap for recognizing the generalizations, but I’m still not sold on the Baby-Boomers being in a situation to buy more homes in the next 7 years. I mean, unless the Boomers have severed all ties with their children, I think their situation isn’t exactly “pain free.” If I’m doing the math right, a Boomer’s kid (depending on their age) is in need of College tuition, help with paying for their wedding, purchasing a home, clothes for grandkids . . . good parents do these sort of things so that their kids don’t have to worry about starting their adult lives in debt and they can (for argument’s sake) buy a house. I’d say that Boomers aren’t going to be looking for a retirement home, investment property, or vacation home for . . . about 7 years from now.
The Case-Shiller method for home determining housing prices is another glorious product of the 1980s; while it may lack the flair of Madonna and the early years of MTV (back when it played music), its consistent benchmarking and dependence on the “repeat sales method” can only be dwarfed by the Rubik’s cube.
Now that the quota for 80s references has been hit, let’s get into the “nitty-gritty” of the Case-Shiller index…
The Case-Shiller index has been seen as a trusted tool for housing prices in the United States since the 1980’s when it was introduced by Case, Shiller, and Weiss’s research principals. The technique is built around the “repeat sales method” – which uses data on properties that have sold at least twice, in order to capture the true appreciated value of each specific sales unit. Sales pairs are designed to yield the price change for the same house, while holding the quality and size of each house constant. This methodology is considered by many as the most reliable means to measure housing price movements and is used by other home price index publishers, including the Office of Federal Housing Enterprise Oversight (OFHEO).