Posts Tagged visualisation

Building an analytics culture in your company

“Building an analytics culture in your company”. It sounds like a buzzword (ok, buzzphrase) and to be honest it probably is, but there is something of value in there.

In order to get real value out of web analytics, you and your staff and contractors need to understand exactly what they are looking at, but most of the responsibility of conveying web analytics information smoothly and directly belongs to us, and not to our clients. To this end we use visualisations and graphs, tables, and the written word in various forms. There is a world of difference between data and information. We believe that anyone looking at one of our reports should understand what’s being conveyed without difficulty.

The culture of analytics doesn’t mean your staff need to understand how to get at web analytics data or do a statistics course. They certainly don’t need to feel like an external analytics agency is judging their every move. All they really need is to understand how the information can help them. We like to work with a wide range of staff handling different aspects of a client’s business, and it usually doesn’t take long before different departments start to ask their own questions and engage with the analytics process.

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The difference between data and information

Data is valuable. Nobody holds data in higher esteem than data miners and web analysts, but there is a world of difference between data and information.

Take the famous (to devotees of data mining) and much admired (by devotees of data mining) Wallmart customer basket database. The implementation of barcode scanners meant that each collection of products bought by a Wallmart customers could be recorded, and they did just that. Back in the early years of the new century this database was probably the biggest in the world. It ran into TeraBytes back when that was an impressive achievement (even to devotees of data mining). Depending on how you reckon 1 TB, it’s either 1000000000000 or 1099511627776 bytes. The good folks at the University of California, Berkeley, reckon that amount of data at 50000 trees or a good sized forest worth of paper.   To put it another way, Dr Jess’ PhD thesis weighed in at 2464KB, or one 405844th of a TeraByte.

Cute arithmetic aside, a TB is a lot of data, and there are plenty of databases of that size around today. Many of the server logs we deal with are well into GB or larger. Wading through all those numbers is the job of data mining algorithms and not people, and that’s as it should be.

It’s the job of web analysts to sort through the data spat out by programs like Google Analytics and Webalizer, sometimes using algorithms and software tools and sometimes their own judgment.

The task isn’t just to build a giant database, it’s to convert that data into usable information. Wallmart doesn’t find out whether coffee and sugar are bought together by reading through their data, and nobody trying to make money from a website needs to wade through masses of facts and figures and sieve out what’s important. That’s our job.

We know what to look for and how to extract information from data, and we also know how to present that information in an easily accessible form, which is half the battle when dealing with large databases.

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