Fourteenth Blog Birthday

Every year, on the anniversary of the first-ever post on this blog, I write a post summarising my thoughts on what’s happening in the world of Microsoft BI and what I’m up to professionally.

This year, rather than go on about how Power BI is taking over the world (which we all know already), I thought it might be interesting to consider how the focus of this blog – and by implication the focus of my work – has changed over the last few years by looking at the viewing statistics of some of my most popular posts.

As you probably know, for a long time the only product I cared about was Analysis Services Multidimensional and MDX: it was pretty much all I blogged about and the only thing I did consultancy and training on. The following graph shows how the number of hits on four of the most-viewed posts on this subject from 2014 to 2018: Aggregating the result of an MDX calculation using scoped assignments; Joining the results of two MDX queries together; Last Ever Non Empty – a new, fast MDX approach; and One Cube vs Multiple Cubes.

None of these posts are, in technical terms, out of date but the downward trend is the same for all of them. The decline in traffic is matched by the decline in demand for consultancy and training on SSAS MD and MDX. While I still spend around 20% of my time doing SSAS MD and MDX consultancy, I do very little training on them at all these days – I guess because no-one is building new solutions using SSAS MD, although there are a still a large number of SSAS MD solutions in production that need maintenance and improvement. I expect the launch SSAS MD in the cloud as part of Power BI Premium will lead to a spike in the amount of work I do on it as I help my customers migrate but that will only be short-lived.

In contrast, look at the trend for four of my most-popular Power Query/M related posts: Referencing individual cell values from tables in Power Query;  Working with web services in Power Query; Creating tables in Power BI/Power Query M code using #table(); and Web.Contents(), M functions and dataset refresh errors in Power BI. These are not necessarily new posts (the earliest dates from 2014) but again they are all still technically relevant and the steep increase in the amount of hits over the last few years that they receive is clear:

Power Query and M is a bit of a niche topic, though; right now my most popular posts are on general Power BI data modelling and DAX – a topic I don’t actually blog about all that often, but which I nevertheless spend a lot of consultancy and training time on. The following graph shows the trends for the posts Comments and descriptions in DAX; Creating current day, week, month and year reports in Power BI using bi-directional cross-filtering and M; Dynamic chart titles in Power BI; and (although I’ve never really understood the popularity of this one) Using DateDiff() to calculate time intervals in DAX.

Perhaps I should blog about this more? The reason I don’t is twofold: first, there are a lot of people out there such as Marco and Alberto who specialise in DAX, have covered all the obvious angles and do a much better job than I ever could; second, my philosophy has always to blog about what I’m interested in and excited about, and frankly I have always enjoyed Power Query and M more than DAX.

One last graph is needed for context, showing the most popular posts from the three graphs above next to each other. The following graph shows how Aggregating the result of an MDX calculation using scoped assignments, Working with web services in Power Query and Dynamic chart titles in Power BI compare against each other:

It goes to show how the “Dynamic chart titles” post is now much more popular that the “Aggregating the result of an MDX calculation” post was, even at the peak of its popularity. I guess Power BI is a safe bet for my future.