The key observation was that individuals, groups and organizations are struggling to discover, assemble, organize, act on, and gather feedback from data. Data itself is growing and fragmenting at an exponential pace. We as individuals feel overwhelmed by the slew of data (messages, emails, news, posts) in the microcosm, and we as organizations feel overwhelmed in the macrocosm.

The very real danger is that an individual or organization’s feeling of being constantly overwhelmed could result in the reduction of their “Aha!” moments – our resources will be so focused on merely keeping pace with new information that we won’t have the time or energy to connect the dots.

The goal then is to find tools and best practices to enable the “Aha!” moments – to connect the dots even as information piles up on our fingertips.

My thought going into the conference was that we need to understand what causes these “Aha!” moments. If we understand the cause, we can accelerate the “Aha!” even at scale.

Earlier this year, Janet Rae-Dupree published an insightful piece in the International Herald Tribune on Reassessing the Aha! Moment. Her thesis is that creativity and innovation – “Aha! Moments” – do not come in flashes of pure brilliance. Rather, innovation is a slow process of accretion, building small insight upon interesting fact upon tried-and-true process.

Building on this thesis, I focused my talk on using frontline data warehousing as an infrastructure piece that allows organizations to collect, store, analyze and act on market events. The incremental fresh data loads in a frontline data warehouse add up over time to build a stable historical context. At the same time, applications can contrast fresh data with historical data to build the small contrasts gradually until the contrasts become meaningful to act upon.

I’d love to hear back from you on how massive data can accelerate, rather than impede, the “Aha!” moment.

Aggregate Knowledge powers a discovery network (The Pique Discoveryâ„¢ Network) that delivers recommendations of products and content based on what was previously purchased and viewed by an individual using the collective behavior of the crowds that had behaved similarly in the past. Again, each 1% increase of engagement is a significant revenue stream for them and their customers.

ShareThis provides a sharing network via a widget that makes it simple for people to share things they find online with their friends. In a short period of time since their launch, ShareThis has reached over 150M unique monthly users. The amazing insight is that ShareThis knows which content users actually engage with, and want to tell their friends about! And in its sheer genius, ShareThis gives away its service to publishers and consumers free; relying on delivering targeted advertising for its revenue: by delivering relevant ad messages while knowing the characteristics of that thing being shared. Again, the better their analytics, the better their revenue.

Which brings me to my point: data analytics is a direct contributor of revenue gains in these companies.Traditionally, we think of data warehousing as a back-office task. The data warehouse can be loaded in separate load windows; loads can run late (the net effect is that business users will get their reports late); loads, backups, and scale-up can take data warehouses offline –which is OK since these tasks can be done on non-business hours (nights/weekends).But these companies rely on data analytics for their revenue.· A separate exclusive load window implies that their service is not leveraging analytics during that window;· A late-running load implies that the service is getting stale data;· An offline warehouse implies that the service is missing fresh trendsAny such planned or unplanned outage results in lower revenues.On the flip side, a faster load/query provides the service a competitive edge – a chance to do more with their data than anyone else in the market. A nimbler data model, a faster scale-out, or a more agile ETL process helps them implement their “Aha!” insights faster and gain revenue from a reduced time-to-market advantage.These companies have moved data warehousing from the back-office to the frontlines of business: a competitive weapon to increase their revenues or to reduce their risks.In response, the requirements of a data warehouse that supports these frontline applications go up a few notches: the warehouse has to be available for querying and loading 365×24x7; the warehouse has to be fast and nimble; the warehouse has to allow “Aha!” queries to be phrased.We call these use cases “frontline data warehousing“. And today we released a new version of Aster nCluster that rises up those few notches to meet the demands of the frontline applications.

I want to build upon that observation here. If you haven’t read the posts, do read them first. It is well-worth the time!

I propose that there are 2 forces in action that help simple algorithms on big data beat complex algorithms on small data:

1. The freedom of big data allows us to bring in related datasets that provide contextual richness.
2. Simple algorithms allow us to identify small nuances by leveraging contextual richness in the data.

Let me expand my proposal using Internet Advertising Networks as an example.

Advertising networks essentially make a guess about a user’s intent and present an advertisement (creative) to the consumer. If the user is indeed interested, the user clicks through the creative to learn more.

Advertising networks are used today on a CPC model (Cost-Per-Click). There are stronger variants of CPL (Cost-Per-Lead) or CPA (Cost-Per-Acquisition) but these variants are as applicable to the discussion as the simpler CPC model. There is a simpler variant of CPM (Cost-Per-Impression) but an advertiser ends up effectively computing CPC by keeping track of click-through rates for money spent via the CPM model. The CPC model dictates that Advertising Networks do not make money unless the user clicks on a creative.

Today, the best advertising networks have a click through rate of less than 1%. In other words, advertising networks correctly interpret a user’s intentions 1% of the time, 99% of the time they are ineffective! I find this statistic immensely liberating. Here is a statistic that shows that even if we are correct 1% of the time, the rewards are significant.

Why is the click-through rate so low? I think it is because human behavior is difficult to predict. Even sophisticated algorithms (that are computationally practical only on small datasets) do a bad job of predicting human behavior. It is much more powerful to think of efficient algorithms that execute across larger, diverse datasets to exploit the richness inherent in the context to enable a higher click-through rate. I’ve observed people in the field sample behavioral data to reduce their operating dataset. I submit that a sample of 1% will lose the nuances and the context that can cause an uplift and growth in revenue.

For example, a Content Media site may have 2% of their users who come in to read Sports stay on to read Finance articles. A sampling of 1% is certain to reduce this 2% population trait to a statistically insignificant portion in the sample. Should we or should we not derive this insight to identify and engage the 2% by serving them better content? Similarly, an Internet Retailer may have 2% of their users who come in to buy flat-panel TV have also bought video games recently. Should we or should we not act on this insight to identify and engage the 2% by offering them better deals on games? Given that games are a high-margin product, the net effect on revenue via cross-sell could be higher than 2% in dollars.

We often want to develop an algorithm that is provably correct under all circumstances. In a bid to satisfy this urge, we restrict our datasets to find a statistically significant model that is a good predictor. I associate that with a purist way of algorithm development that was drilled into us at school.

Anand’s observation is a call for practitioners to think simple, use context and come up with rules that segment and win locally. It will be faster to develop, test and win on simple heuristics than waiting for a perfect “Aha!” that explains all things human.