advantage means something to businessThe first reason is that it actually means something to the business, unlike the widely misunderstood technical term “real-time”. One company that is currently boasting “real-time” performance is SAP, which claims that its in-memory databases will result in analytics that are “really in real-time”. Larry Dignam quotes Hasso Plattner as follows. “In analytics, there’s theoretically no limitation on what you can analyze and at what level of detail. (In-memory databases) mean reports on a daily basis, hourly basis.” [ZDnet] @davidsprott calls this In-memory madness. Even if an hourly or daily report counted as “real-time” (which it doesn't), this kind of technical wizardry doesn't make any sense to the business.
On a Linked-In discussion thread recently, I've seen vendors excusing their misuse of the term "real-time" (to describe software that isn't strictly, or sometimes even remotely, real-time) by claiming that the meaning of technical terms evolve over time. Oh yeah, very convenient. But we shouldn't allow vendors to fudge perfectly good technical terms for their own marketing purposes, any more than we should tolerate car manufacturers self-interestedly redefining the word "friction".
(In The 2 second advantage...the 2 culture disadvantage? Vinnie Mirchandani praises TIBCO executives for being able to talk business, and contrasts them with the IT analysts in the expo hall, with a special dig at Gartner.)
Unfortunately, TIBCO is not content with the "two second advantage" slogan, and we find TIBCO CEO Vivek Ranadivé over-egging the pudding by introducing some additional notions including Enterprise 3.0. Transcript of HCL keynote by TIBCO CEO Vivek Ranadive (April 2010). For @neilwd "Enterprise 3.0 ... is the sound of a company trying too hard" (TIBCO, Enterprise 3.0 and the two-second advantage, May 2010. See also Tibco’s Hits and Misses (Ovum, May 2010).
advantage is relativeThe second reason I like the phrase "Two Second Advantage" is that it focuses our attention on the business advantage - not of raw speed but of getting there first. If you are a speculator who judges that some asset is overvalued or undervalued, the way to make money from this judgement is to buy or sell and then wait for other speculators to arrive at the same judgement. Being just ahead of the pack is actually more profitable than being a long way ahead, because you don't have to wait so long.
And although simple decisions can be taken quickly, complex decisions need time for understanding. (See my presentation on Mastering Time.)With complex decision-making, it's about spending just enough time to process just enough information to make a good enough judgement.
Ranadivé also talks about two trends - the increasing volume of data and the diminishing half-life. (In physics, the concept of "half-life" suggests a long tail of residual value - just as a radioactive sample will always remain somewhat radioactive, so the value of data never reaches zero.)
But as @neilwd points out, these trends don't necessarily refer to the same kinds of data, especially if we measure data volumes in terms of physical storage, since these volumes are dominated by email attachments and rich media. Maybe we need to find a way of measuring data volumes in terms of information content ("a difference that makes a difference"): as the cost of data transmission and storage continues to get smaller, it is not the number of megabytes but the number of separate items (giving managers the experience of being overloaded with information) that really matters.
Even if we limit ourselves to traditional data, the relationship between data volumes and response speed is not as simple as all that. Let's look at a specific example.
If a retail store gives a hand-held scanning device to the customer and/or places electronic tags on all the goods, it can collect a much higher volume of data about the customer's behaviour - not merely the items that the customer takes to the check-out but also the items that the customer returns to the shelf. As technology becomes cheaper, this enables a huge increase in the volume and granularity of the available data, collected while the customer is still shopping, and therefore the retailer actually has more time to use the data before the customer leaves the store.
For example, you might infer from the customer's browsing behaviour that she is looking for her favourite brand of pasta sauce. The shelf is empty, but there's a new box just being unloaded from a truck at the back of the store. Find a way of getting a jar to the customer before she reaches the checkout, and there's your two second advantage.