Lately, consultancies with little QlikView experience have asked me to review the feasibility of using QlikView for a variety of projects. It was obvious after only a quick glance of the projects’ goals that they did not take into consideration the strengths and necessities of QlikView. I had come to believe people understood the concept of data discovery and that we were past the idea that QlikView was a just a quick reporting tool, but I was mistaken.
Many still believe QlikView only stands for fast implementation time, ease of use and a visual display. They try to adapt what they understand of BI to what they’ve heard about QlikView. Of course, you can’t blame them because we humans naturally interpret new information based on past experiences.
So, I’ve decided to write a series of blog posts that explain the strengths of QlikView so that we can understand how to use it effectively in our organizations. I will then conclude the series by detailing the reasons why QlikView projects sometimes go awry. We’ll add an extra part in each post about how Qlik Sense may or may not change how we use QlikView.
First, let’s explain the concept of data discovery and how we should go about implementing data discovery projects.
Data discovery is
learning something new as a result of an active interaction with data.
Yet data discovery is only a part of the story because you can perfectly communicate a discovery with a static story, report or infographic. The tools that help us perform data exploration that results in discovery are the real innovation behind data discovery. These data exploration tools focus more on the interaction between people and data, rather the production of static reports, imitation car dashboards and flashing stoplights.
Data exploration and discovery is not new. How many of us have used SQL queries or excel to look for answers to our questions? I’m not referring to SQL stored procedures or monthly excel reports; but rather, when we use these tools to resolve new questions we have never answered before. Sometimes this process of searching for the answer lasts days, but we now have a set of Data Discovery tools available that make data exploration and discovery easier.
In comparison with previous Business Intelligence tools, these tools have made data exploration and discovery easier in the following ways.
- Agile implementation
- Easier data integration and modeling
- Real-time analysis
- Data visualization
- Data navigation
Traditional business intelligence tend to first focus on organizing, cleaning and defining data before distributing well-defined reports or cubes that allow for limited data exploration and discovery.