A decomposition tree visual or a widget is very helpful in understanding cause and effect of data. It gives an easy-to-view insight to the underlying data.
Suppose we have a fact table of raw data of products sold, revenue, place and time of sale, etc. etc. etc. Every month, year, product, country repeat as they occurred. There is no dimension table. In addition to power query and pivots, we can also generate some powerful visuals with not much extra work or querying. The above image is an example of a decomposition tree.
We can analyze revenue for example, as explained by time (year, then month) and country. Another thing we can do it find the key influencers, or the primary movers.
For example, we can answer question like what’s the primary factor to profit increase as explained by products. IOW, which product is most profitable?
We can also use a waterfall visualization to understand the cumulative effect of product, time on revenue increase, revenue decrease for example as shown in the example below.
Here’s a short clip of all of them put together in a Power BI report including a quick view of the underlying dataset of 700 fictitious records. Hope you like it.
Interested in creating programmable, cool electronic gadgets? Give my newest book on Arduino a try: Hello Arduino!