Thanks for the summary Pranay. Please update your BEP with how exactly this has influenced your approach. What part are you working on at the moment?
On 1 July 2013 16:12, Pranay B. Sodre <[email protected]> wrote: > Hello Everyone- Here is what was discussed at my meeting with the end users > for the Apache bloodhound reporting functionalities. > > Currently: > > Scripts are run at the end of the day outputted to a CSV file (usually by > last 24 hours, and by priority). Priority is set as priority level 4, by > default and then it is set into the appropriate priority when processed. It > would be useful to automatically compare against internal SLAs. > > > > It is useful to have, in reporting: > > 1. How many tickets closed this week vs opened this week? > > 2. What priority were they raised at or closed at? > > 3. Snapshot view day to day (like a dashboard.) > > 4. Compare percentage amount of tickets closed in 15 minutes or other > time frames. > > 5. Compare this time to last year. > > 6. Applying logic to reports based on time stamps. > > > > Ideal world: > > 1. Full scale dashboard. > > 2. SLA perspective- these are response time for P1 (priority 1) type > scenarios. Of 50 tickets, 10 critical P1’s, etc. Cause and effect type of > reporting functionalities. > > 3. Drill down to next level, based on criteria. > > 4. Cataloguing problems within the 'components' field. > > 5. Notifying people based on changes to tickets. > > 6. What would the report look like at a particular time is another > important aspect to help with usefulness of the report. > > > > In essence, a generic query to time slice will be helpful to move into > other aspects of the reporting. > > 1. A query, that uses time, as a way to slice data, and provide reports on > a specific time period. > > 2. ‘Component’ field data would be useful as a basis for further reporting > functionalities. > > > > Sincerely, > > Pranay B. > > > > "Everything has beauty, but not everyone sees it." > > -Confucius > -- Joachim Dreimann | *User Experience Manager* WANdisco // *Non-Stop Data* e. [email protected] twitter @jdreimann <https://twitter.com/jdreimann>
