If I was to give you a date; November 9th 1989, what would that mean to you? As a date on its own it means nothing. However, if I said Berlin November 9th 1989, would it become clearer? That was the date the Berlin Wall was torn down. I am sure your head is now full of images of people climbing the wall with pick axes as the wall is slowly taken away piece by piece. You may even remember where you were at the time you heard the news.
If I was to give you a date; November 9th 1989, what would that mean to you? As a date on its own it means nothing. However, if I said Berlin November 9th 1989, would it become clearer? That was the date the Berlin Wall was torn down. I am sure your head is now full of images of people climbing the wall with pick axes as the wall is slowly taken away piece by piece. You may even remember where you were at the time you heard the news. In this case just one simple addition of one item of context provides the association you need to reach understanding.
Information with context removed can also lead to incorrect interpretation. Take the act of contextomy (quoting out of context). By removing context, a whole sentence can change its meaning. A TV critic from Vanity Fair said of the TV program Lost “the most confusing, asinine, ridiculous —yet somehow addictively awesome — television show of all time.” When this sentence was eventually published by ABC it read “The most addictively awesome television show of all time” — Vanity Fair.
Further, data in isolation and without context can lead to bad business decisions. If you were to base your purchasing decisions just on sales from the previous month you would be in great danger of either being stuck with unsold stock or paying for warehouse space you don’t need. But if you were to wrap context around that data such as last year’s sales figures or sales of similar products you would be able to make better business decisions.
A lot of the time, this data is available but is scattered around the business in isolated silos and in this form can actually be dangerous because it lacks context. By joining this data together we can extract meaningful information that helps us to drive the business.
For example in the following data model; if you only have sales data, you would not see which products had been sold to this customer, or what sales territory had made the sales.
Historically to do this you may have had to build an expensive data warehouse or invest time and resources in creating a cube. Now though, QlikView joins together the isolated silos of data dispersed within your company simply. You have got the entire context you need in one app, and users just need to join the dots.