With QlikView, power users can set up associative analysis sandboxes. (We wrote about analysis sandboxes in earlier posts here and here.) QlikView analysis sandboxes can incorporate trusted data sources like a data warehouse as well as external data sources like industry trend or stock market data. All this data is merged in one in-memory location, either on the power user's local machine or a QlikView Server. The power user can immediately begin interrogating the data for answers and insights.

QlikTech technical advisor Elif Tutuk has helped guide our customers in their use of QlikView for analysis sandboxes and recommends the following approach. Pull data from the source systems into a QlikView data file (QVD).* The QVD is a highly-compressed replica of source data. It includes just the data you would want to include in an application-typically not 100% of the data in the source system(s). Production applications as well as the sandbox environment can point to this shared storage area. Then you can go to town creating list boxes, charts, and tables. A chart could be very complex-it could consume a good portion of RAM to visually represent years of data by any combination of dimensions in hopes that the user may "catch something" (identify a trend or outlier in the data).

The QlikView approach to analysis sandboxes has several advantages over traditional approaches:

  • Power users can experiment and explore without harming the original data. They can use the same QlikView data file that's used by production QlikView applications, and also include additional data sources or calculation logic. They can massage the data when they create charts and graphs. Because power users are on a different server from other end users-even while all are accessing the same underlying data source-end user performance is unaffected by the power user's activities.
  • QlikView delivers a high-performance user experience. All users benefit from this approach: power users as well as more casual business users. With QlikView associative analysis sandboxes, there's no waiting for IT to stage the data. QlikView compresses the data at roughly 10:1 and delivers fast load performance. Right away, power users can start analyzing and visualizing the data and identifying patterns, trends, and outliers-and answering urgent, ad hoc business questions.
  • The associative experience facilitates the natural flow of insight discovery. Query-based analysis sandboxes make use of the intersections of data sets. In contrast, with QlikView every search term in the analysis is part of an entire network of data. Relationships among the data are clearly visible. QlikView shows the user not just which data is associated-but also which data is unrelated. QlikView looks through a network of associations for the data that's connected to the user's current selections. (See related blog posts here and here.)

The associative experience and QlikView's visualization capabilities facilitate the natural flow of insight discovery and support a "build to think" approach to BI. With QlikView, its does not take longer to build an analysis solution than it does to think one. (For more on the "build to think" approach to BI see related blog posts here and here.)

* A QVD is a file that contains a table of data exported from QlikView. It is a native QlikView format. The file format is highly compressed and is optimized for speed when reading data from a QlikView script. Reading data from a QVD file is typically 10-100X faster than reading from other data sources.