Today’s Web interfaces are all about the Flash (literally). Smooth charting, cool effects, callouts to references — ways to try and simplify complex data collections.

Problem-solving and diagnosis requires a far deeper dive than the flashiest interface could ever provide, because it comes down to the numbers. The actual measurements that make up the flashy chart. If you look at the data used by a professional trader and a someone at home looking at stock charts, there is a substantial difference.

When you get down to that level of analysis, the interface becomes irrelevant. Any analyst worth her or his salary (or salt – same thing) can tell you more from a spreadsheet full of relevant numbers than they can from any pretty graphic. This is true in any field.

When do traders or Web performance analysts use pretty charts? When they have to explain complex issues to non-technical or non-specialist audiences. When these analysts work on solving the sticky problems faced in the everyday world, they always fall back on the numbers.

Web performance data consists of the same few components, regardless of which company is providing the data. In effect, beyond a few key pieces of information about how the measurement data is captured, all Web performance data is the same.

Just because the components that make up the data are the same does not guarantee that the data from two different providers is of the same quality. In an imaginary system, Web performance data from all the major providers could flow into a centralized repository and be transformed using an XSLT or some other mangler so that it would be indistinguishable in most cases to tell which firm was the source.

But a skilled analyst would quickly learn to recognize the data that can be trusted. That would be the data that quickly and accurately represented the issues he was trying to diagnose. The data that flowed with the known patterns of the Web site. The data that helped him do his job more effectively.

In the end, a pretty interface can go a long way to hide the quality of the data that is being represented. A shiny gloss on poor data does not make it better data. It is critical that the data that underlies that pretty chart is able to live up to the quality demands of the people who use it every day.

Selling the interface is selling the brand. Trust in the data builds the reputation.

Which one sold you when you chose your Web performance measurement provider?

There is clear dissatisfaction with the current state of marketing among the social media mavens.

• Fred Wilson and Union Square Ventures are looking for companies to invest in to take advantage of this.
• BuzzLogic releases their conversational ad service.
• The Inquisitr moves from AdSense to Technorati Media, indicating a potential shift at b5 Media.
• Lookery is providing demographic information that is relevant to social media providers and shaping ad presentation that way
• A completely off-the-radar person such as myself realizes that there is gold in what holds the attention, and that’s not always the latest and greatest

So what can be done? Jeff Jarvis points out that the problem lies with measurement. I agree, as there is only value in a system where all of the people involved agree on what the metric of record will be, and how it can be validly captured.

Currently CPM is the agreed upon metric. In a feed based online world, how does a CPM model work? And, most importantly, why would I continue to place your ads on my site if all your doing is advertising to people based on the words on the page, rather than who is looking at the page and how often that page is looked at.

In effect, advertisers should be the ones thrying to figure out how to get into the community, get into the conversation. As an advertiser, don’t you want to be where the action is? But how do you find an engaged audience in an online world that makes a sand castle on the beach in a hurricane look stable?

The challenge for advertisers is to be able to find the active communities and conversations effectively. The challenge for content creators and communities is to understand the value of their conversations, the interactions that people who visit the site have with the content.

In effect, a social media advertising model turns the current model on its head. Site owners and community creators gain the benefit of being attractive to advertisers because of the community, not because of the content. And site owners who understand who visits their site, what content most engages them, how they interact with the system will be able to reap the greatest rewards by selling their community as a marketable entity.

And Steven Hodson rounds out the week’s think on communities by throwing out the subversive idea that communities are not always free (as in ‘beer’, not as in ‘land of’). If a community has paid for the privilege of coming together to participate in communal events and discussions, then can’t that become an area for site owners to further control the cost of advertising on their site?

While the benefit of reduced or no marketing content is the benefit of many for-pay communities, this benefit can be used by site owners by saying that an advertiser can have access to the for-pay community at the cost of higher ad rates and smaller ads. The free community is a completely different set of rules, but there are also areas in the free community that are of higher value than others.

In summary, the current model is broken. But there is no way to measure the value of a Twitter stream, a FriendFeed conversation, a Disqus thread, or a Digg rampage. And until there is, we are stuck with an ad model that based on the words on the page, and not the community that created the words.

David Cancel and I have had sort of a passing vague, same space and thought process, living in the same Metropolitan area kind of distant acquaintance for about the same year.

About 2-3 months ago, he wrote a pair of articles discussing the efforts he has undertaken in order to try and offload some of the traffic to the servers for his new company, Lookery. While they are not current, in the sense that time moves in one direction for most technical people, and is compressed into the events of the past eight hours and the next 30 minutes, these articles provide an insight that should not be missed.

These two articles show how easily a growing company that is trying to improve performance and customer experience can achieve measureable results on a budget that consists of can recycling money and green stamps.

Measuring your CDN

A service that relies on the request and downloading of a single file from a single location very quickly realizes the limitations that this model imposes as traffic begins to broaden and increase. Geographically diverse users begin to notice performance delays as they attempt to reach a single, geographically-specific server. And the hosting location, even one as large as Amazon S3, can begin to serve as the bottleneck to success.

David’s first article examines the solution path that Lookery chose, which was moving the tag, which drives the entire opportunity for success in their business model, onto a CDN. With a somewhat enigmatic title (Using Amazon S3 as a CDN?), he describes how the Lookery team measured the distributed performance of their JS tag using a free measurement service (not GrabPERF) and compared various CDNs against the origin configuration that is based on the Amazon S3 environment.

This deceptively simple test, which is perfect for the type of system that Lookery uses, provided that team with the data they needed to realize that they had made a good choice in choosing a CDN and that their chosen CDN was able to deliver improved response times when compared to their origin servers.

Check your Cacheability

Cacheability is a nasty word that my spell-checker hates. To define it simply, it refers to the ability of end-user browsers and network-level caching proxies to store and re-use downloaded content based on clear and explicit caching rules delivered in the server response header.

The second Article in David’s series describes how, using Mark Nottingham’s Cacheability Engine, the Lookery team was able to examine the way that the CDNs and the Origin site informed the visitor browser of the cacheability of the JS file that they were downloading.

Cacheability doesn’t seem that important until you remember that most small firms are very conscious of the Bandwidth outlay. These small startups arevery aware when their bandwidth usage reaches 250GB/month level (Lookery’s bandwidth usage at the time the posts were written). Any method that can improve end-user performance while stilll delivering the service they expect is a welcome addition, especially when it is low-cost to free.

In the post, David notes that there appears to be no way in their chosen CDN to modify the Cacheability settings, an issue which appears to have been remedied since the article went up [See current server response headers for the Lookery tag here].

Conclusion

Startups spend a lot of time imagining what success looks like. And when it comes, sometimes they aren’t ready for it, especially when it comes to the ability to handle increasing loads with their often centralized, single-location architectures.

David Cancel, in these two articles, shows how a little early planning, some clear goals, and targeted performance measurement can provide an organization with the information to get them through their initial growth spurt in style.

When I re-introduced my five articles on Web Performance Concepts last night, I had forgotten than I had already written two additional articles in the series.

1. Web Performance, Part VI: Benchmarking Your Site
2. Web Performance, Part VII: Reliability and Consistency

Look for Parts VII and IX in the next few days.

Two years ago I created a series of five blog articles, aimed at both business and technical readers, with the goal of explaining the basic statistical concepts and methods I use when analyzing Web performance data in my role as a Web performance consultant.

Most of these ideas were core to my thinking when I developed GrabPERF in 2005-2006, as I determined that it was vital that people not only receive Web performance measurement data for their site, but they receive it in a way that allows them to inform and shape the business and technical decisions they make on a daily basis.

While I come from a strong technical background, it is critical to be able to present the data that I work with in a manner that can be useful to all components of an organization, from the IT and technology leaders who shape the infrastructure and design of a site, to the marketing and business leaders who set out the goals for the organization and interact with customers, vendors and investors.

Providing data that helps negotiate the almost religious dichotomy that divides most organizations is crucial to providing a comprehensive Web performance solution to any organization.

These articles form the core of an ongoing series of discussion focused on the the pitfalls of Web performance analysis, and how to learn and avoid the errors others have already discovered.

The series went over like a lead balloon and this left me puzzled. While the basic information in the articles was technical and focused on the role that simple statistics play in affecting Web performance technology and business decisions inside an organization, they formed the core of what I saw as an ongoing discussion that organizations need to have to ensure that an organization moves in a single direction, with a single purpose.

I have decided reintroduce this series, dredging it from the forgotten archives of this blog, to remind business and IT teams of the importance of the Web performance data they use every day. It also serves as a guide to interpreting the numbers that arise from all the measurement methodologies that companies use, a map to extract the most critical information in the raging sea of data.

The five articles are:

1. Web Performance, Part I: Fundamentals
2. Web Performance, Part II: What are you calling ‘average’?
3. Web Performance, Part III: Moving Beyond Average
4. Web Performance, Part IV: Finding The Frequency
5. Web Performance, Part V: Baseline Your Data

I look forward to your comments and questions on these topics.

Today’s biggest victim of Black Friday appears to be Sears

Sears measurement data for the last 8 hours can be found here.

UPDATE: It gets worse for Sears.

Technorati Tags:
Grabperf, Sears, Black Friday, Web Performance

mon.itor.us, a service which also provides free Web performance measurement services, appears to be having a wee problem.

The most recent GrabPERF data on this site is available here. The issue may be corrected by the time you look at the data.

I don’t wish suffering like this on anyone. GrabPERF had it’s own 3-4 day outage a few months ago. It’s just sad to see when monitoring services go down.

Part of the problem with making a lot of East to West trips across the US is that the flights back are a nightmare for timing. You either lose most of a day or have to take a red-eye.

Well, taking the red-eye has only one advantage: you get to camp in the airline lounge and get some work done that you have been too tired/jetlagged to deal with.

I am in the United Red Carpet Lounge. It is 18:33 PDT, and my flight boards at 22:00 PDT. I have been here since 15:00 PDT. I have caught up with a client project, completed my (dreaded) weekly timesheet, booked accommodation for my Columbus, OH trip, and tidied up an analysis script that I use to process client measurement data.

Seems odd that this is the most productive time of the week.

This trip is a 48-hour turnaround from Boston to LA to finish up the project for a large client. The first half was handled by some colleagues, and was detailed here.

Then,when I get home, I have to go to NYC (Long Island City actually) for the day on Monday. And, as I mentioned above, Columbus which is set for the middle of the month.

Compared to some jet-setters out there, this is nothing. But I travelled so much between August 15 and November 15 that I went from 2,000 miles on United to well into Premier (24,000+ miles). This trip makes the fourth cross-country trip in 2.5 months. Not bad for a homebody.

I have three more hours. Think I will sit back and watch Trois Couleurs: Blanc.

GrabPERF returned to service at approximately 17:30 GMT (13:30 EDT — 10:30 PDT) September 20 2007. The database server was on all night, but an esoteric choice of primary interfaces (i.e. the least obvious one!) meant that it was taking to empty space.

Have fun and enjoy the data!

GrabPERF has been offline all day, and will likely be offline for the remainder of the day as Technorati relocates the servers to their new datacenter.

Sorry for the inconvenience.