1 Reply Latest reply: Mar 8, 2010 2:03 PM by soxfanjim RSS

    Reduction of data but also group results

      Hi

      I have an application ready in which a user (when it is published) should not only be able to analyse his own data BUT compare his results to that of the group.

      For example (very simple):

      My Revenue this month: 10000 Groups revenue this month: 20 000

      The time periods etc are all flexible, so the user can compare any timeframe with his collegues/competitors.

      My next step is introducing section access and data reduction since the user should not be able to identify any individual collegue or competitor.

      Trying to reduce the data based on users login, you loose the group results (obviously.)

      Is there ANY way that I can use datareduction and/or section access in such a way that I do not have to calculate the group benchmarks for every possible combination?

      The latter is not an option since we are talking about quite an enormous database

      Does anybody have experience with this?

       

      Thanx

       

       

        • Reduction of data but also group results

          We have done this successfully. Our application allows the end user to see only the data associated with their organization, although the data for peer organizations is still present in the application for aggregate comparisons. The key is your ability to capture login information and tie it into the data model.

          We are using an AJAX application (no QV Plugin or Java client access) that we authenticate to via a web page that uses Basic Authentication. We've written a small application to get a ticket from the QlikView server, using DMS authentication, which is maintained so that it matches the Windows login from the Basic Authentication on the web site.

          Once we have the ticket, our app is aware of the user's identity via the QVUSER() system function. Our data model contains an Organization ID that also matches the login name. The table that stores this data is hooked into the rest of the data so that a user's ID is associated with their data.

          The last step is to apply logic to the expressions in the user objects. For example, in a chart we might use Set Analysis to match the Organization to the QVUSER() so that we do not allow the user to see any one else's data. At the same time, we can define columns that do not filter on QVUSER() when we want to present aggregate data for comparison.

          This approach took several weeks to build, but it is doable.