How useful is Landau notation?
In the context of a new answer to an old question on Computer Science SE and a subsequent chat, I started thinking about how useful Landau notation (also “BigOh”) really is. Since I am quite opinionated about the whole thing I thought a blog post would be a better place than Stack Exchange for collecting these thoughts.
To be clear, I have Landau notation as commonly used in algorithm analysis in mind, i. e. something along the lines of
O(f) = \{ g : \mathbb{N} \to \mathbb{N}_0 \mid \exists c > 0, n_0 \in \mathbb{N}.\ \forall n > n_0.\ g(n) \leq c \cdot f(n) \}.
The usual theme you see is that some cost measure (usually running time) is said to be “in O(n^2)” (or some other function). When I write “Landaubounds” in the sequel I mean the ensemble of O, \Omega and \Theta in the above flavor. You may add o and \omega; they do not change the picture significantly.
Advantages

Landaubounds are easier to prove than more precise results, in particular because we can restrict ourselves to counting a dominant operation. That makes their use attractive for the training grounds, i. e. undergrad education.

Sometimes it is hard to get more.
Consider, for example, divideandconquer recurrences; do you know how to solve them more precisely than with the commonly taught Master theorem? I saw a more precise analysis of Mergesort in class once. It was quite involved, and the technique did not stay with me. I for one do not.
As it turns out, there is the continuous Master theorem by Roura [1] – it is not that old a result. It is certainly not something that is usually taught in computer science education, as far as I am aware.

Landau bounds are easy and shorter to say and write down.
Alleged Advantages

Focus on what is essential.
This one you hear often. It is true – assuming one buys into the “constant factors and small input sizes don’t matter” narrative. I argue below why I do not.
It is definitely true if you only talk about algorithmic ideas; without a specific implementation in mind, precise analysis does not make a lot of sense.
Problems
Technical

It is unclear how the commonly taught definition extends to multiple variables – despite everybody and their mother using it that way!

Constant factors matter in practice.
Without analyzing constant factors, you can not even start analyzing the speedup of parallel algorithms (on constantly many processing units). You also can not compare different flavors or implementations of the same algorithm; for instance, what is the best Quicksort?

Small (read: finite) input sizes matter in practice.
Formally, O(\_) does not say you anything for any finite input size, but even really small sizes matter; for instance, how do you choose when to stop recursing to Quicksort and to which algorithm to switch?

Lowerorder terms matter in practice.
Even though their contribution vanishes relatively in the limit, it can be very significant in absolute terms for the input sizes you actually encounter.

Landaubounds alone do not enable you to predict running times (or any measure).
How to do science (according to the scientific method) in such a situation?
In summary, Landau notation alone is neither useful for predicting the behavior of algorithms in practice with any amount of precision, nor is it useful for comparing algorithms of similar performance.
Cultural

Landaubounds have become the end when they should be a means.

Analysis (too) often focuses on the worst case.

The literature is rife with abuse of notation.
It starts with (ab)using the equality sign, doing arithmetics with Oterms, and other things that can be rigorously defined, but continues to things which everybody disagrees on the meaning of.

Laypeople use it without knowing what it means.
No. Nonono. No! The amount of programmertypes you encounter on the internet who think O means worstcase, \Theta averagecase and \Omega best case is … appalling. But then, apparently even published textbooks get things (horribly) wrong.

Focus on Landaubounds alone encourages coarse, abstract formulations of algorithms that leave gaps in important places.

Focus on Landaubounds alone encourages sloppy, handwavy analysis of algorithms and use of mathematics. You should never have to say “this usually works” when doing proofs.
Alternatives

Derive exact formulae.
Well, Knuth can do it, but I agree that this is not a workable solution for most people in most situations.

Derive more precise asymptotics.
This is actually something you can often do with reasonable effort. Note how a statement like
T(n) = 2n\log n  n \pm O(\log n) We still have to be careful to agree on what “=” and “\pm” mean here!
is way more informative (and useful) than a bland T(n) \in \Theta(n \log n)! In fact, this use of Landaubounds as “error terms” is pretty much the only one I can condone without hesitation. For many purposes, we still need an absolute bound on the hidden terms, though.
Sedgewick’s Coursera course is pretty well done as well. You are not alone, by the way: Knuth [2,3] as well as Sedgewick and Flajolet [4] can teach you how to do this.

Alternative definitions may circumvent some of the technical problems (but break the literature); see e. g. recent work by Rutanen et al. [5].

Teach the limitations and more responsible use of Landaubounds.
Verdict
Arguably, notation has one purpose and one purpose only: enable effective and efficient communication. Do Landaubounds really serve this purpose in the context of rigorous algorithm analysis?
I say no. We should use them as little as possible and strive to produce better, more precise results.
Dear readers, did I miss something? Do you agree on the lists but not with my conclusion? Please leave your comments below!
References
[1] Salvador Roura: An Improved Master Theorem for DivideandConquer Recurrences, in Automata, Languages and Programming, 1256, eds Pierpaolo Degano, Roberto Gorrieri, and Alberto MarchettiSpaccamela (Springer Berlin Heidelberg, 1997), 449–459, DOI: 10.1007/3540631658_201.
[2] Ronald L. Graham, Donald E. Knuth, and Oren Patashnik: Concrete Mathematics: a Foundation for Computer Science, 2nd ed. (AddisonWesley, 1994). ISBN: 0201558025.
[3] Donald E. Knuth: The Art of Computer Programming (1968–20xx).
[4] Robert Sedgewick and Philippe Flajolet: An Introduction to the Analysis of Algorithms, 2nd ed. (AddisonWesley Professional, 2013). ISBN: 032190575X, aofa.cs.princeton.edu.
[5] Kalle Rutanen et al.: A General Definition of the ONotation for Algorithm Analysis. 2015. arxiv.org.