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I've used Smartsheet for a project-based organization, however, it may or may not be useful for all organizations.
While you could use any knowledge management solution so long as it provides a good free text search engine, I've found that a push option (where folks subscribe to receive lesson updates based on specific key words or categories) works better and baking lessons into your standards & practices works best.
In my personal experience the only way to make lessons learned useful is considering them as a component of a knowledge management system (where system is not software system). Including it, to use a new buzzword, knowledge is a key component to gain into organizational agility. With that said, the tool could be any which support your knowledge management system. In my actual work place now we are using ms sharepoint in convination with forms and things like that but just because it was the best way to integrate it to ms azure and other items in the suite.
Reevaluate your process and what you hope to accomplish with lessons learned before getting a new tool. It seems like a lot of information captured is not actionable, or it only applies to a specific point in time on a specific project. As a result, the list of lessons learned ends up in a repository and is never seen again because most of the information is not applicable to future projects.
I try to run lessons learned multiple times throughout a project - especially on long projects. I publish my notes for all to review, but my main intent is capturing actionable items in the following categories:
1) immediate action is needed
2) action that needs taken in later phases/cycles of the current project
3) action that may need taken in other active projects
4) action that may be needed in a future project
Item 1 triggers varying responses - meetings, emails, phone calls, changes... depending on the action needed.
Item 2 results in changes to the project plan, with needed approvals and subsequent notifications.
Item 3 triggers notifications to the appropriate project managers/sponsors so that they can determine the appropriate course of action for their project(s).
Item 4 goes on a checklist, split into process groups or phases, that is actively monitored and updated.
Using Item 4 as an example, I'll review the checklist when I'm beginning to plan a project, and throughout the course of the project to check for changes that may affect my project. I have a curated list of items to consider, instead of hundreds of documents that never get looked at again (true story).
The checklist is reviewed, regularly, to determine if any items should be removed because they no longer apply. If it grows into a multi-page document with a lot of content that is no longer relevant, it becomes worthless. I've tried to keep mine down to under 1 page; it's never exceeded 1 1/2 pages. Since it's broken into sections (phases for traditional projects), you don't have to go through the whole thing all at once to make sure everything is checked off, but it is helpful when planning future phases.
In the organizations I work and worked with in the past, they all have their own Knowledge Management Systems that they have created.
Some were as simple as shared DropBox, others were more sophisticated like a website page for KM.
One thing many people find is that if the data is not structured in very specific ways to facilitate efficient searches, retrieval is difficult without sophisticated search approaches.
Amazon Web Services is an example of COTS computing capability more powerful than lists, spreadsheets, or databases for managing information. With less than ideally structured data such as free-form text, you can do things like easily find the most common keywords used in your lessons, or other data patterns that may not be obvious but prove very useful.
That type of system is most useful for large datasets, and requires someone who understands how to use the big data functions. AWS is scalable in capabilities and cost though, making it more practical for more uses that don't require massive computing capabilities.
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