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Andrew Edwards is a managing partner at Technology Leaders, a web analytics consulting and technology firm based in New York.

He is a co-founder and former board member of the Web Analytics Association.

A pioneer in the interactive space, Andrew formed one of the first web development companies in New York in 1993. Andrew won numerous industry awards during the 1990s and helped found Technology Leaders in 2002. At Technology Leaders, Andrew has spearheaded the development of a consulting practice focused exclusively on web analytics.

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The Curse of Too Much Data

Written on
May 18, 2011 
Author
Andrew Edwards  |
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The Curse of Too Much Data

ADOTAS – Few marketers know of the mountainous treasure trove of data available to them via the use of web analytics. Many are surprised when, having deployed an analytics tool, data comes at them in a gusher. It often seems too much, too fast, and is more often than not, little understood. Web analytics can easily become too much of a good thing. It can also be inaccurate, misleading, overloading, confusing, even paralyzing.

In these situations, marketers typically let the analytics tools run but they reduce their interaction with the data–often because there is too much of it and it is poorly targeted to their needs. After a while, web analytics “adoption” rates sink, and the organization suffers from a lack of insight into user behavior despite a robust analytics data collection schema. But it is still paying in some way shape or form to have that data, even if they are using a so-called “free” solution.

Does this sound familiar? If so, you may be in the majority. An informal study has shown that even in some large organizations, web analytics is only moderately-well understood, and often that understanding is siloed inside of a web analyst role or a tech role; but not often enough broadly distributed among the key decision-makers in marketing. And the main culprit is “too much data with too little information.”

Every tool vendor markets its solution as a package full of goodies. A long list of features is made available–and many of them may even be useful to the organization if properly deployed. But listening to vendor-speak, one might get the impression that their tool performs a kind of insight-infusing magic that, of course, it cannot (without human intervention).

Where the vendor says: “Buy our tool and you’ll have insight!”, the marketer should really be thinking: “Buy that tool and I will have a number of ways to study important data but not without careful paring and targeting of the data streams, directed at the right people at the right time.”

Of course you can leave your tool in default mode and it will dutifully record literally millions of hits, clicks, page-views, downloads and performance indicators; and you will one day go hunting for a nugget of information only to find it’s not really available in the way you need it; while at the same time you are confronted with what seem like a thousand unnecessary and distracting “reports” that mean little to you even if you were to take the time to understand them.

These classic web analytics problems are often overcome by smart organizations. Here are some of the ways we have seen web analytics deployed in a targeted, meaningful manner:

1. Simplify

Have your analytics experts get rid of all the reports, profiles, plug-ins, spreadsheet exports and integrations that you are not using regularly. This will reduce the load on everyone involved and may even create a lower price point on your licensing depending on what type of tool you have.

2. Authenticate

Get your web analytics infrastructure audited by a neutral third party. You will probably find much that needs adjusting for accuracy; as well as a great deal of extraneous tagging and configuring that slows down both page load and report processing. And of course, you will see enhanced accuracy and timeliness of data.

3. Target

Rather than have everyone log in and look for reports, find out what they are looking for and create custom dashboards for them, clearly labeled and footnoted. Limit usership only to those who have a real interest in using the data.

4. Look for Trends, Not Individuals

Some organizations shy from good analytics because they believe it should do what it cannot. For instance, a number of marketers claim they want to see “individual visits” as if this will somehow inform them on a macro level about what to do with a web site. Individual visits are excellent when integrating with CRM or in a warehouse environment, but they are neither very possible nor very helpful in a pure analytics environment. Analytics is designed to deliver trending data or “aggregate” data and this trending ability is the real strength of any good analytics tool.

Over the years I have found that “too much data I don’t understand” may be the most common barrier to adoption of analytics within an organization. The tools in the market today are designed to fulfill a wide range of insight requirements. Therefore they enable many reports meant to cover “all the bases”. But you don’t need all of them. You may only need five or six out of hundreds of possible reports.

Keeping the focus on simplification, accuracy and targeting, with a keen awareness of what analytics tools are built for, will make for a much more effective impact for any web analytics deployment.





Reader Comments.

I would be amazed to find many companies where analytics data is “moderately well understood”. The only company benefiting from most installs of Google Analytics is Google (because it allows them to track Internet usage and build huge profiles on individuals)

Most installs are not properly optimized so they don’t provide actionable data and most businesses would have no idea what to do with it even if it did.

Avinash Kaushik was correct years ago when he said businesses should spend less on collecting data and hire someone who could tell them how to apply it.

Posted by Gail Gardner | 6:23 pm on May 19, 2011.

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