pbis

Do you struggle with an overwhelming backlog?

Do you count the number of product backlog items in your backlog in tens? hundred? or thousands?

Does your backlog contain many stories which have been there for months, if not years, and yet never raise to the top of the backlog?

Is your success judged on your ability to do the backlog?

Backlogs were a good idea when they were introduced a bit over 20 years ago but today many teams slaves to the backlog – see my posts on the Tyranny of the backlog and Purpose over backlog. One of the benefits I’ve called out for OKRs is the ability to move away from backlog driven development (BLDD).

In Succeeding with OKRs in Agile I ask suggest you either need to prioritise your backlog over OKRs (in which case OKRs are derived from the backlog you intend to do) or OKRs over backlog (in which case OKRs are derived from strategy and the backlog plays a supporting role.) In my podcast with Jenny Herald earlier this year I even say “Let OKRs drive… nuke the backlog.”

Filipe Albero Pomar recently shared his backlog freshness blog which I think is great. Freshness is a great way to think about the state of the backlog that separates the size of the backlog from the relevance of the backlog.

Filipe’s idea is simply to talk about the backlog in terms of freshness – you have a fresh backlog if your backlog items are fresh: written recently, relate to current opportunities, problems and things people currently want.

And of course, the opposite of fresh: stale, stories that have been sitting in the backlog for months, even years, stories that relate to yesterday’s problems and project, stories which people wanted last year. The existence a big backlog of stale stories means the team is seen to be not delivering, the end-date is far off because people still expect all the work to be done.

Filipe suggests backlog freshness can be measured:

1. Set a cut-off date

2. Categorise stories as fresh or stale: fresh stories have been written since the cut-off date, those which are older are stale

3. Calculate freshness as a percentage of fresh from the total: if 25 stories out of 60 have been written in the last month then the backlog is 41% fresh, and 59% stale

Thats a useful metric, I think we can do better, look at the graphic above. I group backlog items into age groups and graphed them. For completeness I added a line to indicate average story age. Clearly this backlog is not fresh – nearly half the stories are over a year old.

I like the idea of graphing backlog freshness because it is easy to understand and makes an impact. In the graph above I’ve categorised backlog items into age groups and added an average line. Clearly this is not a fresh backlog. Whether this is the way to demonstrate backlog freshness I’m not sure – I’m playing with a histogram and quartile ranges.

With some clients I’ve thought of the backlog like a mortgage. There is the principle (the existing backlog), the interest rate (the growth rate of the backlog) and the monthly repayments (stories reaching done). Unfortunately when you do this you sometimes find the mortgage will not be paid for many years, and perhaps never. (Don’t worry about estimating the size of stories, for this sort of analysis the number of stories will get you started, and if your backlog is measured in hundreds of items the small will offset the large.)

I’d love to talk more about this and experiment with some ideas, I think it could be a very useful way of thinking about the backlog.

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