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.

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.

I started tweeting Evan out of the blue, in part because of a podcast he was in,

I initiated:

CB: @evanlapointe You make good points. I’m skeptical that the person holding the ruler should also be responsible for generating the strategy

EP: @cjpberry I wouldn’t say generating the strategy, necessarily, but conducting the orchestra once the music is written

CB: @evanlapointe Yes. I agree. I make the distinction between measurement, convenient reasonsing, strategic analytics and marketing science.

EP: @cjpberry we’re lucky if it’s only 4!

CB: @EvanLapointe Measurement is straight observation and reporting. Convenient reasoning is explicit sandbagging.

CB: @EvanLapointe Strategic analytics is the application of the scientific method to data for the purpose of deriving competitive advantage.

CB: @EvanLapointe Marketing Science is the search for truth and enduring sustainable competitive advantage. So there are 4 divisions.

EP: @cjpberry yeah, I think our greatest value is in asset allocation. we know the value of portfolio mixes via our analysis.

EP: @cjpberry I agree with you. But simply providing a measure service horribly muffles value of analytics

EP: @cjpberry generally, analysts don’t understand business. essentially all about efficiency and congruency once a business is up & running

CB: @EvanLapointe Some analysts understand the business better than the people who believe they run the business. Some. Not all.

EP: @cjpberry I guess my core point is we don’t have to be the specialists. our value is in arranging the specialists

EP: @cjpberry I can’t say that’s an untrue statement, but with such a web focus, I’d say the ratios weigh 95% to not at all

EP: @cjpberry I haven’t spoken with a single web analyst who has noted that their web issues are a consequence of comp structure

EP: @cjpberry our sites are a result of our operations. not the other way around

EP: @cjpberry nearly every IA, UX, or marketing issue can be directly tied to a conflict of interest of some sort. not an accident.

EP: @cjpberry do you think my #s are off? do you think more analysts really get how to run a business?

CB: @evanlapointe Hmmm. I believe the best analysts are the ones who try running an eCommerce site. Less than 5% try. Your figures are right.

EP: @cjpberry always a pleasure!
I left it there, in part because not everybody was following both of us. And Twitter really isn’t supposed to be a conversation medium (apparently).

To add to the discussion:

The unique, zesty zing that a web analyst can bring to the table is rooted in the analytical mindset. I agree with Evan that we ought to be better. I want to enumerate just how much better, and where.

Strategic analytics mandates that the ruler is reliable and unbiased. The ruler is essential.

It also mandates an understanding of the business, strategy, and analytics.

The definition of strategy is deliberate, reinforcing choice. The definition of analytics is the application of the scientific method to data to derive sustainable competitive advantage, typically through insight generation. An insight is a novel finding that wasn’t known before, that causes a decision that would not have been taken, that results in profit or a sustainable competitive advantage.

Great strategists are incredibly rare. I don’t quite understand this grand desire among so many to become ‘strategists’. There are loads of imitators. Choice is difficult, because of a strange culture where we’re taught not to really make a choice and stick with it. Systems thinking is hardly ubiquitous. You could spend your

I know many analytics analysts and marketing scientists. They too are very rare in general.

The overlap of the two sets is golden. How many strategic analysts are there on Earth? Now really?

Now add business acumen – the ability to even understand how to organize capital – and you have some sort of ultra cyborg.

I’m not saying don’t try.

I’m saying realize that it is far more probable that a really great analytical mind, with knowledge of strategy and some business acumen is the most probable outcome. We have to start from the base of analytical thinking because that’s what we’re strongest at.

So, what’s the strategy for breaking up?

The goal of target setting is to make a statement about a desired future state – with or without a forecast.

Targets are political artifacts. You can read all about such dynamics in public policy here.

Forecasts, ideally, are scientific artifacts.

The interplay between forecasts and targets is particularly interesting. Those who produce sophisticated forecasts should understand that the motivation of those probing models is to assess whether or not a future state is possible, or, in certain situations, just how probable a given scenario could be.

Don’t become trapped into the mindset that a trend is locked in permanently. Actively explore what needs to be true, in which circumstances, to produce a better outcome. Most importantly, and I’m learning this the very hard way, make every attempt to make affirmative recommendations.

• New information
• Executable
  
• Causes action
• Profitable

Or, more detailed, an insight is:

• A piece of information that you didn’t know before, which -
• Can feasibly executed, culturally acceptable and of a scale relevant to the firm, and -
• Causes a decision to be made that wouldn’t have been made otherwise, and -
• Results in profit or a sustainable competitive advantage

I’m finally happy with this definition. It aligns with the best innovation rhetoric very nicely and is generalizable to both design thinking and analytics communities.

Steve Miller and I have worked fairly closely over the years – he as an Information Architect and myself as a Marketing Scientist. We were both part of the landmark NASA.gov redesign. Since then, we did major sites in the healthcare and retail sectors. He welcomed me into Syncapse on my first day, and since then, we’ve iterated on social media measurement experience design.

His contributions in terms of usability in data driven insight generation are well reflected both in the interfaces and in roadmaps. He’s a champion for user centric design. And, I believe that we have learned more in a 1 year period here than we ever could anywhere else.

As IA continues its inevitable fracturing into UX, their importance as a glue between Tech, Creative, Science and Strategy will intensify. With so much risk comes great opportunity.

Steve will return to a market that, confusingly, will still emphasize the role of waterfall IA. The market for that, at least in Toronto, is huge. However, Steve will be one of the absolute few that will be capable of taking on Lean-IA or Lean-UX. He’ll be one of the few to identify early feature creep. He’ll be one of the few to understand the challenges of MVP and speed-to-market.

I hope, for Toronto’s sake, that he’ll come back in such a role. We need more Lean-UXers.

The invite:

“Developers make it possible to measure anything, statisticians and dataminers work models, IAs finesse interfaces, analysts mash and managers action. Effective Analytics takes an orchestra. Lets talk to each other and see whats possible.”

Historically, WAW’s attract a strong contingent of web analysts, social analysts (many from Syncapse), IA’s, a few dev’s, recruiters, vendors, and yes, two dataminers. And it’s a great mix. Let’s keep that mix and expand it. Additional invites to business strategists, eScientists, Marketing Scientists, and specialized developers.