How « Self-Service-like » are self-service BI applications really?: buffet or à la carte
.. about the cook, a buffet and the choice our guest have to consume analytics ..
In a recent thread on social media there was an interesting thread on “how self-service-like” today’s self-service analytics components really are. Some of the thread contributors doubted whether self-service BI was really something one could hand over to a business end-user. They are concerned whether self-service really can exist in day-to-day life of an end user; “isn’t there always some ICT intervention needed”, they said. It is an interesting discussion that hasn’t a black & white answer. Let’s deep dive in a few of the elements that drive the discussion:
Two Flavors in my restaurant: guests and cooks
So the whole idea of self-service analytics has always been to have business users work with analytics relatively autonomously themselves. Important elements for business users when applying for self-service analytics are:
Simplicity: users require simple to use tools
Low adoption curve: users look for tools that are very easy to understand and learn
Interactivity: users require to inter-act with the data; creating filters, drill-down and drill-through, click-select-filter capabilities
Focused: users look for tools allowing them to easily narrow down to the data-scope they are interested in or is of value to them
Autonomously: users don’t want to be “number 27 in lane” with the ICT or BICC competence center asking for a new report or dashboard. They want to with analytics on their own, though still having the ICT or BICC governance
Now back to the discussion. Why have some of the people in the above thread some doubts? Didn’t we conclude in this article that all of the above self-service requirements can easily be embedded in an enterprise analytics platform? Yes we did. But the “doubters” talk a different flavor of self-service. They talk about self-service for data analysts. There is a small, but strict, difference between self-service for end user or consumers, and self-service for data analysts. To explain this we need to use the analogy of a restaurant:
The guest
In a restaurant you have guest who come to enjoy a dinner. Our guests “equal” the business end users of analytics. A dinner can be seen as a collection of analytical insights. The insights are thoroughly selected by our guest picking either from a menu – and order à la carte –, or they go to the buffet and pick the things presented to them and ready for consumption. Ordering à la carte refers to end users opening specific dashboards, reports or storyboards from the business analytics portal.
Their workflow is like:
Screen the menu and roughly select the type and amount of food they want to eat, i.e. Fish or Meat, main course, and a dessert or not. Our analytics end user chooses whether he/she needs financial info or logistic info; what kind of detail-level is needed.
Next our guest chooses from the menu: will I take just a main course, or add a starter and/or dessert. "Do I maybe opt for two starters and skip the main course?". Our guest even might mention he/she wan't the meat saignant or well done. In analytics terms the user decides which reports, dashboard and/or storyboards he/she needs to get the insights required. Our user also decides on prompts or variables needed to get the specific scope of the insights.
When dinner is served our guest just digests what's being served; leaves leftovers if feeling so and enjoys what he/she asked for.
The above is similar when choosing from a buffet, with that difference that adding special requests (well done) is not possible with a buffet. On the contrary, the buffet allows to digest multiple small plates to our guest's needs; just like an analytical end user could consume reports and dashboards in random order.
Our guest will typically be a user of existing Design Studio applications or BusinessObjects Cloud storyboards. I have stipulated how they work in this article
The cook
Our next flavor of a self-service user, is the home cook that has to cook for him/herself. This user is more the like of a data analyst. Somebody who may not have a clear view on what kind of insight is needed, or requires insight on non corporate data that is not explored on a regularly basis. Here the workflow differs. Imagine yourself the workflow of the TV cooks we all see on tele very single day; it is the exact same workflow as our self-service end user:
Our home cook opens up the fridge and explores the ingredients needed; think of the data analysts that accesses the data sources he/she requires to start exploring data
Next our home cook starts cleaning, cutting, seasoning, mixing and combining his/her ingredients. Only those pieces of the ingredients that are needed for the meal, are used. It is where our data analyst starts filtering, enriching (hierarchies, formulas etc), blending (combining data sources) and cleaning his data
When this is all done, we typically see the home cook putting his selected ingredient-mix in the pot and put them on the stove. This is where the data analysts starts creating the visualizations, graphs and maps and combines them to a final storyboard which might be shared with others later on.
Our home cook makes quite an important decision in the last step; either he/she serves the plate à la carte to our guests (i.e. his colleagues or management) from above, or the final meal is just put on a buffet for users to consume.
