Credited for pioneering the data discovery space, Qlik is well positioned to continue as a leader in this new market. With the release of Qlik Sense, Qlik is resetting the bar in offering user-friendly data discovery tools, while providing a well governed and scalable platform.
QlikView 11 Overview
For readers unfamiliar with QlikView or Qlik Sense, this section describes the core elements of the platform, and explains how it differs from traditional BI.
In-Memory Storage Means No Need for Pre-Calculated Cubes
Unlike OLAP systems, QlikView uses RAM as the physical storage medium for data. Since computers can access memory hundreds of times faster than disk, calculations and aggregations can be performed on the fly, with astounding speed. Thus, the limitations of building pre-aggregated cubes are gone! Figure 2-2 shows a data ecosystem with the addition of QlikView. Notice that QlikView can extract from multiple sources and does not require any pre-aggregated cubes.
Figure 2-2: QlikView in the data ecosystem
Using QlikView, transactional data can be loaded into RAM and then summarized at runtime, at the user’s request. If you’re used to the terminology of traditional BI, you can think of QlikView as creating “cubes on-demand,” from RAM. Transparent to the user, the aggregations occur seamlessly in the front-end, with each chart essentially creating its own cube. The benefit of loading the granular detail is two-fold: the data can be aggregated up to any level, and the user can drill down to view the details.
For the small or medium-sized organization, QlikView may replace the need for OLAP or other reporting tools. For the enterprise, QlikView is often added as a data discovery/analytics platform that works alongside OLAP systems – particularly if the organization still requires paper-based reporting.
An Interactive User Experience
A user accustomed to the traditional BI report interface knows that you need a game plan going in, before actually seeing any data. Typically, the user must select a specific report and provide the required parameters or filters before the report is run. QlikView completely rejects this approach, and instead presents the user with all of the available data, immediately accessible in the interface.
When a user opens a QlikView application, data is visible right away, without specifying any parameters. The user interacts with the interface to step through the data in an exploratory way, to zero in on specific results. Figure 2-3 shows a basic example of QlikView application containing sales data for an apparel company. You’ll use this data set, which is available from the book’s download site, throughout the book.
Downloading the Electronic Materials for This Book
If you haven’t done so yet, please download the electronic materials provided for this book. You can find the detailed instructions at the end of this book’s Introduction.
This is a screenshot of a typical QlikView application. Using a tabbed sheet layout, developers place objects on the sheet to allow for searching and selecting data and visualizing measures.
Figure 2-3: Example QlikView 11 application
In this app, a few filter objects called list boxes are shown across the top (Year, Quarter, Month) and down the left pane (Channel, Product, Season). Three visualization charts are shown: a pie chart, bar chart, and straight (non-pivot) table. The data in the charts reflect the entire data set, with no filters applied. In QlikView parlance, filters are called selections. The current state of the selections can be tracked in the Current Selections box, shown in the upper left.
Using a familiar tabbed sheet layout, this simple QlikView application invites the user to make selections to explore the data and click on the tabs to explore the layout. By default, selections made on one tab are persistent throughout the entire application (this behavior can be changed by the developer, depending on requirements). In Figure 2-4, the app is shown with selections applied for Channel and Season.
Figure 2-4: Filters applied in a QlikView 11 application
As soon as the user makes selections, the data in the charts dynamically update. No need to press Go, Generate, or Apply – results are rendered immediately. All visual objects in a QlikView app can be interactive in some way. Most obviously, the list boxes allow clicking or searching for attribute values. In addition, the user can click or lasso the slices of a pie chart, or the bars in a bar chart, to make selections within the visualization itself. The table in the bottom-right of Figure 2-4 is also selectable and sortable.
With an attractive and interactive interface, QlikView apps encourage users to ask questions of the data, which may encourage asking questions of each other, which may result in collaboration, which may then lead to true business insight.
Associative Logic Powers Data Discovery
Perhaps the most effective driver of data discovery in QlikView is its patented associative query logic. Without going into the details of how it works, let’s look at what it delivers.
Selected, Associated, and Non-Associated Values
Perhaps the most obvious feature of QlikView’s associative logic is the ability to visually see how other pieces of data are associated with your selections. The feature that truly differentiates QlikView is the ability to see the data that is not associated.
Figure 2-5 shows list boxes with two explicit selections applied.
Figure 2-5: Green, white, and gray
Selections are made in the Product Group and Warehouse list boxes for Casual and Memphis, respectively. What can you learn from this simple selection?
Based on QlikView’s display defaults:
● Selected values are highlighted in green
● Associated values are highlighted in white
● Excluded values are highlighted in gray
From this, you can infer the following:
● There are no products from the Spring collection in the results
● There are no customers from ID, HI, NE, NM, RI, or VT
● There are no products from the Q-Tee Golf brand in the results
Using only list boxes, QlikView can visually communicate meaningful associations within the data. Seeing which data is associated (and which is not) can confirm a hunch or prompt the user to look under previously un-turned stones. A question of, “Why are there no Q-Tee Golf products shipping out of Memphis?” may lead the user to one of these conclusions:
●