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Thanks for that detail, that's really helpful. I know it would be
lovely to give you a standard benchmark, but you really will do
better if we can find a metric that drives the kinds of behaviours
For any system or process, there are six basic things you can try
- fixed costs (ie baseline costs of staff, buildings, software
- incremental costs (ie cost per additional product)
- cycle time (ie how long one item takes to produce)
- throughput (ie how many products are being produced)
- quality (ie accuracy, integrity, value)
- excellence (ie delivering over and above expectations)
I'm guessing that cost isn't the primary driver, so let's focus
on the other aspects. Will your Knowledge Base be successful
merely if there are knowledge assets published?
What if those assets were really poor quality? Or they weren't of
interest to anyone?
Here are some ways to think about metrics within the constraints
of the available data. To support optimisation for:
- cycle time, you might seek a high number of downloads
on the presumption that users who download data are doing so in
order to support timely completion of their work
- throughput, you might seek a high number of uploads as
confirmation that the data scientists are productive in creating
assets for the organisation to reuse
- quality, you might examine the ratio of views to
downloads on the basic assumption that data which is perceived
as useful will get downloaded more often
- excellence, you might simply monitor view number trends
as a proxy for demonstrating that the KB is providing ongoing
value to its audience
I'll be honest though; none of these are great
metrics. They are pretty easily game-able so you'll need to trust
that your teams genuinely want to succeed on their merits rather
than by seeking to artificially inflate numbers. Of these, I'm
most inclined to:
- Monitor throughput trends (uploads) and aim for a
What's reasonable will be context specific; how big is the team
and do these knowledge assets take days or weeks to produce? Are
they responding to specific client requests or just producing
whatever research they feel is interesting?).
- Seek a stable or upwards trend in view traffic every
quarter as a proxy for excellence.
This is pretty straightforward - if the system isn't delivering
value to users overall, they'll stop using it.
You might also want to periodically examine quality of the
various uploads to determine if there are products being created
which aren't adding value and if so, engaging in a conversation
about if they should be stopped or adapted to be more relevant.
This would be more of a diagnostic than a metric though.
Executive, Information Management
M: 0401 829 096
On 28/04/2021 9:54 pm, Vandana Wadhawan
via groups.io wrote:
The aforementioned team is research heavy. They engage in data
modelling activities, and performs drug-specific researches and
are qualified data scientists. The KB is intended as a storefront
to store all their knowledge assets.
While I'm able to find out their monthly engagement (it's been
only a month), I'm also supposed to propose an engagement goal to
the team (a realistic percentage) that they could aspire to
achieve in 2-4-6 months.
Is there a standard benchmark that anyone has come across? i could
workaround that benchmark and come up with something.