The tl;dr summary:

“For Samsung’s 7500 and 8000 series TVs, all you have to do is say “Hi, TV,” when you walk into a room for the TV to turn on and know who’s there.”

“Think of it: The tech means an advertiser or TV programmer could, for the first time, know which members of a Nielsen household are watching a show or an ad. Cisco has even developed a system meant to read facial expressions and determine whether you’re entertained or bored.”

“Many people in the living room are multitasking with other devices. “We’re paying for that,” said Rex Harris, innovations supervisor at SMGX, a unit of ad agency holding company Publicis Groupe. “If you’re looking at other screens, then you’re not paying attention. We would like to know if we’re getting accurate impressions.”"

Commentary:

Alright – so – a simple innovation, the webcam, is jumping from the PC/DVR into a TV, and we get a few folks who come out and speculate what it could mean. It all ends up sounding like a 1984 telescreen idea, which, I’m 99% certain, is not what Samsung has/had in mind.

Broadcast isn’t digital.

Repeat: broadcast. isn’t. digital.

This has implications:

• There is enough inventory for targeted ads and offers in digital because the technology enables the creation of multiple ad treatments at scale. No such technology exists in the broadcast industry.

• People already effectively segment themselves by TV show preference.

• On Demand technologies like Netflix, and time shifting technologies like streaming and DVR’s, are already eroding the concentration of key market segments.

• Plot the S-curve adoption rate of the technologies driving market fragmentation against the adoption of new, Big-Brother enabled telescreens, and see which wins. (Hint: it’s time shifting and on-demand).

• You’re paying for junk impressions because we’re developing ad blindness, just like we’ve developed banner blindness.

No amount of surveillance is going to change that fact.

It’s also the first get together after eMetrics New York, which was a major, and had big time Canadian attendance. These tend to be among the more interesting evenings. It has also been some three months since the last WAWTO event, so there should be quite a few fresh stories.

It’ll be great to be back in New York.

There are a few people that I’m looking forward to seeing: John Lovett on social media, Melinda Driscoll on web analytics, Shari Cleary on media, Joseph Stanhope on mobile, Alex Langshur on government. And then there’s Michael Healy, Patrick Glinski, and me.

I’m presenting with Michael Healy on sentiment. Michael Healy is among the best thinkers in this space and is just great. There have been a few very recent breakthroughs in sentiment analysis over the summer (and as recently as last week), and I’m looking forward to explaining how to treat the measure. I understand a core problem with the application of the metric – the gap between what some want the metric to mean – and what the metric actually really measures.

I’m with Patrick Glinski of Idea Couture on Friday – presenting “Communicating Data to Designers”. It’s a really different topic, and something you won’t see anywhere else. It’s not on the radar yet as a differentiating competitive advantage. It’s new, it’s different, it’s fresh – and even a bit risky. So come on out. We won’t bite.

I’m looking forward to seeing you out.

It’s an excellent way to present data in a very accessible, very explorable way. I found it inspiring.

The call to action is “create your chart now”. A very good, honest, call to action.

The technology adoption S-curve can be a slow beast, and expectations of growth have persistently outstripped actual adoption, at least in North America, and especially in Canada. Adoption has a few drags on it in North America and Europe. No such drags exist in Asia.

The chart below compares all the countries smartphone penetration. (Click to embiggen)

That chart masks underlining maturity in each country. The chart below compares m-commerce ‘at least one time’ usage across Germany, China, and the United States. The big three economies. (Click to embiggen).

That’s a good snapshot. M-commerce is thought of as a pretty big risk in North America.

In many ways, the melding of couponing with check-ins was the right bridge into mobile for the times. As we all watch Groupon careen into the inevitable mess, we all ask ‘what’s next’. I ask, ‘where’s the utility’, ‘how can mobile be used to salvage previously wasted parts of my day?’

I look to m-commerce as being predictable. Certain firms, like Plastic Mobile, have extensive experience with eCommerce, and understand mobile. They’re not going to replicate the pain of the nineties. There’s an inevitability to it.

Though, painfully punctuated.

This would have happened sooner if it wasn’t for the double whammy of policy plus recession.

John Lovett predicts that Moneyball will put analytics on the map.

It’s likely. It’s just so damn relatable.

Ideas have a long journey from conception to popularization. Nash had been known to game theorists and a sub-set of political scientists who don’t understand people, since the beginning. Most didn’t learn of it until ‘A Beautiful Mind’ came out. Moneyball is that movie.

To extend the lesson from Moneyball -

1. Everybody has the same mental model of the way the world works with respect to some phenomenon. (Experience E used to perform Task T with performance P as the outcome).

2. A dominant model dictates a dominant set of Key Performance Indicators.

3. Somebody discovers a better model and validates it through analytics.

4. Somebody effectively competes better as a result of the new model.

5. Others resist the new model (lock-in), they persist in the market until they die or are defeated.

6. New model becomes the new dominant one.

7. Return to (1) and repeat.

***

I’m Christopher Berry.

I bridge the gap between marketing science and data science.

I welcome connections on Twitter and LinkedIn.

If you define analytics as being ‘the scientific method applied to data to generate sustainable advantage’, then there are three major concentrations of practitioners: finance, marketing and operations.

The financial sector breaks out into the risk management and the speculation fields. There’s a higher self-referential graph amongst the risk management people in insurance than there are on the banking side. The speculation analytics folks are at severe disadvantage against their New York counterparts. If there’s a thriving hedge fund section in our community, I don’t know about it.

Marketing is divided between startups, CRM vendors, agencies, and client side (which includes data mining and web analytics). There are very few deep pockets of specialization – but they do exist in social analytics, mobile analytics, and web analytics. Extreme subject matter experts in Omniture, Coremetrics, and Webtrends number fewer than 10. Subject matter experts in Google Analytics exceed 30, however, that’s not to be confused with ‘people who have google analytics on their blog’.

There’s a thriving operations section of analytics – supply chain/logistics, transportation logistics, and internal operations. I also lump the impressively large gaming analytics (online gaming and gambling), and the minuscule dating optimization folk into this space.

There’s variety and specialization in analytics just within Toronto. And it’s pretty awesome.

The best way to break into analytics is to practice analytics. People who practice analytics in a context specific to something they enjoy are more likely to enjoy success and attract attention.

The best way to find somebody who knows analytics is to attend an industry event. They are frequent and welcoming.

We need both physical technology and social technology to practice analytics at scale.

There are three grouping of physical technologies:

• First, there’s instrumentation technology that we use to measure  and record the world around us.

• Second, there’s analysis technology that we use to understand the data that’s coming.

• Third, there’s presentation technology that we use to communicate a world view, and what to do next.

On the instrumentation technology side, we’ve all had a few challenges with instrumentation as of late. Specifically, the understanding of definitions, their impacts, and the unexpected impact of bugs. Empathy from one technologist to another on this front. Instrumentation is not easy.

On the analysis side, SPSS, R, SAS, Datameer, Python. Amazing technologies, some of which may be used as controllers, some of which are used by analysts to peer into the deepest, most chaotic systems.

On the presentation technology side, we have excel, powerpoint, keynote, and certain dashboarding technologies. They have pros and cons. XML or JSON API’s ought to be the future, or some version of it, here. It doesn’t seem like a big problem. But it a fairly wicked one, because credibility and authority are bound up in aesthetic.

Getting these three physical technologies right, linked up together, is very important to practice analytics at scale.

Social technology at scale

People are incredibly important because they’re the ones who generate insight, and ultimately cause beneficial change that results in sustainable competitive advantage. It’s not the physical technology of software and hardware. The institutions that cause them to behave in very specific ways is a social technology. And it must be in place to scale.

There are a number of problems that an organization creates for analytics professionals.

For one, most organizations don’t know what they don’t know about analytics. They don’t understand that instrumentation is still young and buggy. That truth isn’t absolute. That sleuthing is part of the role. That it’s not just “pizza and spreadsheets”. That it takes time to put together a series of recommendations that make sense in a given a system or context. That not every convenient reasoning business case can be generated, or generated quickly. That not everything is recorded by the instrumentation. The first three months setting up any new analytical institution is entirely about resetting expectations.

There are a number of problems that analytical leadership causes for their organizations.

For one, most organizations don’t know what they don’t know about analytics. Bad behaviors result. They hive off the data. They clam up indiscriminately. They refuse to engage with regions of the company for extended periods of time without a strategy in place for such cut-offs. They hire the wrong people. They don’t secure headcount for enough people to be successful. They’re unable to demonstrate their own ROI. They don’t say no often enough to be able to cause their own ROI. They don’t champion their own work. They acquire a siege mentality. They don’t publish and they don’t share their successes with industry. They churn rapidly.

Not all leadership is bad and not all organizations are poisonous to analytics.

If we accept the premise that organizations want sustainable competitive advantage from analytics, and that analytics leadership wants that same outcome, we can construct a physical technology stack and a social technology stack that achieves that end.

Current thinking on that end:

1. Mediums and Medias are fragmenting. The most progressive thinking on the subject is towards medium planning (Syncapse, Teehan+Lax), and as a result, analytics leadership that resists new, novel, mediums are likely to be viewed as obstructionist. Instrumentation will fragment as a result. This is okay. Derive a medium measurement strategy. It’s what our collective leadership must become good at.

2. Analytics means having an analytical tool to use. SPSS is preferable because of usability. R is preferable in certain environments because it’s free. Firms that actively compete on analytics may require a big data stack to data mine very large sets.

3. Recommendations are communicated in powerpoint. The business schools have decreed this. Collaborate on problems without a powerpoint presentation. New thinking from the business schools have decreed this.

4. Every single organization has a C-level that always asks for go-pher analyses. The rest of the organization typically pays no cost to support an analytical seat. These ad-hoc seats are an excellent way to hire new talent out of the universities and are good training opportunities, under supervision. Allow your experienced guns to find the insights, and let your inters-juniors do the gophering. Record the output of the gophering.

5. Nobody in the organization has incentive to acknowledge the analytics departments for insight discovery. The leadership of that department must make sure that there’s a solid internal culture that rewards insight.

Most organizations desire analytics at scale – which means handling both the intelligence and ad hoc sectors of the business – simultaneously. The way to get there is by combining social and physical technologies that enable that scale. It will be an ongoing losing war, but every battle should bring victories.

All the best to them.

Then I had empathy.

There was an election in Canada. The federal statute is an Elections Canada law that prohibits anybody from transmitting results to regions of the country where the polls are still open. That includes all broadcasters and applies to online. For 30 minutes, between 9:30pm EST, when the polls closed in Ontario, and 10:00pm EST, when the polls finally closed in BC, I relied solely on twitter and a dashboard on CBC TV. It was horrible.

That screencap shows a dashboard, populated with data from later on in the evening. You could see the seat totals for parliament, and they’d cycle through a bunch of ridings at the bottom of the screen. Even more maddening was that I knew of 4 independents who had a shot at winning and Elizabeth May. The ‘Other’ column was of reduced utility.

For thirty minutes, I knew the NDP was surging. I couldn’t tell where, though, the color commentary indicated that it was happening in Quebec. But how was Montreal doing? And by how much? The commentary was of reduced utility to me, but of course, I’m not their audience.

I had a very specific set of questions, and no means to access the data.

That’s the first time I’ve probably ever written those words. I’m accustomed to near-instant access to information. How’s the site doing? Log in. How’s this new navigation working out? Log in and watch a few people use it in real time (yes, that happens). Who are my best customers? I have a very rapid method for that and a syntax written out. Data may fool me with randomness, but I have methods for estimating when I’m getting tricked. And if I can’t find out online, I can pick up a phone and use the network.

I’ve become used to finding out anything I want to know. My behavior is a direct product of technology and education. And it causes a pause.

After 10pm, the CBC did have one of the best interactive mapping applications among the broadcasters, and I used it to tweet real time analysis from my laptop throughout the evening.

It provided a very good amount contextual data, and reloaded automatically in 10 second increments. It was a great experience, of course I’m biased though.  I prefer to consume data through a map and a table. That requires a high degree of geographic knowledge which I just expect everybody to have, and a moderate degree of numeracy.

In a large way, I’m motivated to create products that enable people to see the world the way I do. Empower people to find novel findings and they’ll find it.

Information Deprivation is horrible and it should be cured.

The price of that brevity is abstraction.

Jargon unites people in as much as it alienates them from each other.

I’ve experienced this first hand – visiting data miners, market researchers, marketing scientists, entrepreneurial developers, and brand managers. It becomes very easy for people to dismiss entire modes of thought purely because the jargon doesn’t resonate.

Deep within abstraction are generally understood understandings. For instance, the term ‘qualified traffic’ means something very fundamental to a search marketer. The same term, ‘traffic’, is perceived a fair bit differently among web analysts. And in terms of CRM people – well – they don’t view ‘response’ as a form of traffic.