7 min read
Do you remember the first time you encountered a self-service checkout terminal in the supermarket? I do. My first reaction was to ask what incentives the store was going to give me for being my own cashier? Discounts? Coupons? Surely I wasn’t expected to do someone else’s job for free.
Today, I rarely even bother with manned cashier lines. Self-service is quicker and I don’t have to deal with chatty employees or comments on what I’m buying. I’m a self-service convert.
Chances are you are, too. In fact, research has shown that more than half of US retail shoppers prefer self-service checkout to cashiers in most situations. A recent study in the UK found that only 10 percent of shoppers say they’ve never used a self-checkout terminal, and 71 percent said self-service machines are more convenient when buying just a few items.
_Source: Software Advice_
And the trend is clear. A study of users of self-service point-of-sale systems in restaurants by Software Advice found that more than 70% percent of diners between the ages of 18 and 34 preferred them, compared to less than 35 percent of those over the age of 55. We’re raising a self-service generation.
So why shouldn’t the same dynamics apply to business intelligence (BI)? The time is right. From the early days of BI in the late 1980s until recently, the process of supporting analytical queries changed little. Business users specified exactly what data they needed, IT created a custom schema and then extracted, transformed and loaded (ETL) the necessary production data into the BI database. Reports were limited to what could be specified in an SQL query and new data had to go through the same arduous ETL process each time it was updated. The whole process was so painful and time-consuming that few users even bothered.
How times have changed.
Hadoop created the concept of a “data lake,” or a repository of structured and unstructured data that users could mine without supervision for BI-style modeling. Analytics languages like SAS, SPSS and R provide powerful programming capabilities to data scientists, but tools like Tableau, Qlik and AtScale give much of the same functionality to business users without all the complexity. A visit to the Tableau Gallery shows you the wonderful things people come up with when not hidebound by schemas and rules.
Building a self-service BI playground on top of a data lake doesn’t have to be difficult or expensive. Here are a few principles to keep in mind if you go that way:
The beautiful thing about self-service is that it’s a win-win. The organization providing the service saves on labor and can allocate its resources to more productive and interesting tasks. Users get the information they need quickly without a lot of overhead. With cost barriers tumbling and tools proliferating, now might be a good time to give self-service BI a try.
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