In my life as a consultant, I often discuss What Web performance data means and how to interpret it to solve problems. Solving the problems is, however, inherently based on whether the data that is collected is meaningful. In trying to find data that is meaningful, we have found that there are four categories that Web performance measurements fall into: Customer Generation, Customer Retention, Business Operations, and Technical Operations.

Customer Generation

How can you use Web performance measurement data to outperform your competition and impress your prospects. Read it here!

Customer Retention

Impress your customers with your skill and responsiveness, and keep the competition from sneaking in the back door. Read it here!

Business Operations

Know how you are doing against your competition and prioritize what you need to do to stay ahead. Read it here!

Technical Operations

Know what to measure and how often to keep a detailed eye on your internal systems and external performance. Read it here!

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?

In 2004-2005, as a lark, I created my own Web performance measurement system, using PERL, PHP and MySQL. In August 2005, I managed to figure out how to include remote agents.

I dubbed it…GrabPERF. An odd name, but an amalgamation of “Grab” and “Performance” that made sense to my mind at the time. I also never though that it would go beyond my house, a couple of basement servers, and a cable modem.

In the intervening three years, I have managed to:

• scale the system to handle over 250 individual measurements
• involve nine remote measurement locations
• move the system to the Technorati datacenter
• provide key operational measurement data to system visitors

Although the system lives in the Technorati datacenter and is owned by them, I provide the majority of the day-to-day maintenance on a volunteer basis, if only to try and keep my limited coding skills up.

But this post is not about me. It’s about GrabPERF.

Thanks to the help of a number of volunteers, I have measurement locations in the San Francisco Bay Area, Washington DC, Boston, Portugal, Germany and Argentina.

While this is a good spread, I am still looking to gather volunteers who can host a GrabPERF measurement location. The areas where GrabPERF has the most need are:

• Asia-Pacific
• South Asia (India, Pakistan, Bangladesh)
• UK and Continental Europe
• Central Europe, including the ancestral homeland of Polska

It would also be great to get a funky logo for the system, so if you are a graphic designer and want to create a cool GrabPERF logo, let me know.

The current measurement system requires Linux, cURL and a few add-on Perl modules. I am sure that I could work on other operating systems, I just haven’t had the opportunity to experiment.

If you or your organization can help, please contact me using the GrabPERF contact form.

While this post is aimed at Web performance, the curse of the single metric affects our everyday lives in ways that we have become oblivious to.

When you listen to a business report, the stock market indices are an aggregated metric used to represent the performance of a set group of stocks.

When you read about economic indicators, these values are the aggregated representations of complex populations of data, collected from around the country, or the world.

Sport scores are the final tally of an event, but they may not always represent how well each team performed during the match.

The problem with single metrics lies in their simplicity. When a single metric is created, it usually attempts to factor in all of the possible and relevant data to produce an aggregated value that can represent a whole population of results.

These single metrics are then portrayed as a complete representation of this complex calculation. The presentation of this single metric is usually done in such a way that their compelling simplicity is accepted as the truth, rather than as a representation of a truth.

In the area of Web performance, organizations have fallen prey to this need for the compelling single metric. The need to represent a very complex process in terms that can be quickly absorbed and understand by as large a group of people as possible.

The single metrics most commonly found in the Web performance management field are performance (end-to-end response time of the tested business process) and availability (success rate of the tested business process). These numbers are then merged and transformed by data from a number of sources (external measurements, hit counts, conversions, internal server metrics, packet loss), and this information is bubbled up in an organization. By the time senior management and decision-makers receive the Web performance results, that are likely several steps removed from the raw measurement data.

An executive will tell you that information is a blessing, but only when it speeds, rather than hinders, the decision-making process. A Web performance consultant (such as myself) will tell that basing your decisions on a single metric that has been created out of a complex population of data is madness.

So, where does the middle-ground lie between the data wonks and the senior leaders? The rest of this post is dedicated to introducing a few of the metrics that will, in a small subset of metrics, give a senior leaders better information to work from when deciding what to do next.

A great place to start this process is to examine the percentile distribution of measurement results. Percentiles are known to anyone who has children. After a visit to the pediatrician, someone will likely state that “My son/daughter is in the XXth percentile of his/her age group for height/weight/tantrums/etc”. This means that XX% of the population of children that age, as recorded by pediatricians, report values at or below the same value for this same metric.

Percentiles are great for a population of results like Web performance measurement data. Using only a small set of values, anyone can quickly see how many visitors to a site could be experiencing poor performance.

If at the median (50th percentile), the measured business process is 3.0 seconds, this means that 50% of all of the measurements looked at are being completed in 3.0 seconds or less.

If the executive then looks up to the 90th percentile and sees that it’s at 16.0 seconds, it can be quickly determined that something very bad has happened to affect the response times collected for the 40% of the population between these two points. Immediately, everyone knows that for some reason, an unacceptable number of visitors are likely experiencing degraded and unpredictable performance when they visit the site.

A suggestion for enhancing averages with percentiles is to use the 90th percentile value as a trim ceiling for the average. Then side-by-side comparisons of the untrimmed and trimmed averages can be compared. For sites with a larger number of response time outliers, the average will decrease dramatically when it is trimmed, while sites with more consistent measurement results will find their average response time is similar with and without the trimmed data.

It is also critical to examine the application’s response times and success rates throughout defined business cycles. A single response time or success rate value eliminates

• variations by time of day
• variations by day of week
• variations by month
• variations caused by advertising and marketing

An average is just an average. If at peak buiness hours, response times are 5.0 seconds slower than the average, then the average is meaningless, as business is being lost to poor performance which has been lost in the focus on the single metric.

All of these items have also fallen prey to their own curse of the single metric. All of the items discussed above aggregate the response time of the business process into a single metric. The process of purchasing items online is broken down into discrete steps, and different parts of this process likely take longer than others. And one step beyond the discrete steps are the objects and data that appear to the customer during these steps.

It is critical to isolate the performance for each step of the process to find the bottlenecks to performance. Then the components in those steps that cause the greatest response time or success rate degradations must be identified and targeted for performance improvement initiatives. If there are one or two poorly performing steps in a business process, focusing performance improvement efforts on these is critical, otherwise precious resources are being wasted in trying to fix parts of the application that are working well.

In summary, a single metric provides a sense of false confidence, the sense that the application can be counted on to deliver response times and success rates that are nearly the same as those simple, single metrics.

The average provides a middle ground, a line that says that is the approximate mid-point of the measurement population. There are measurements above and below this average, and you have to plan around the peaks and valleys, not the open plains. It is critical never to fall victim to the attractive charms that come with the curse of the single metric.

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.