Data Discovery in QlikView – Part 1 – Agile Implementation

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

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
  • Usability

Agile implementation

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.AgileImplemenatationDataDiscovery

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Learning QlikView Data Visualization – SVG Map Extension Download Update

Since Learning QlikView Data Visualization was published, the url to download the SVG map extension created by Brian Munz and highlighted in chapter 7 has changed to https://github.com/brianwmunz/svgReader-QV11.

If you have any questions about the content of the book, don’t hesitate to ask.

See you around,

Karl

Command line statement from QV Publisher

Steve Dark has recently created askQV.com where you can search several QlikView blogs and the QlikCommunity.  It’s a great resource and I congratulate Steve for his initiative.

It’s not uncommon that I found a solution to a problem only to forget it and then not know where to find it again when I need it in the future.  How to run a command line statement from QV Publisher is one of those infrequent questions that has no clear solution in QlikCommunity, and after performing a search on askQV.com, apparently nobody has blogged about it either.

If you have QlikView Publisher and you go to the System (1) tab of the QlikView Management Console there will be a link to Supporting Tasks (2).  If you then click on External Programs (3) you can then create a command line statement.

ExternalProgramQVPublisher

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Quick Set Analysis Tip – Selecting Current Selections

Wow! I think it’s about time to post something new since the post about Packt has been the latest post for two months now.  I’ve been quite busy lately trying to launch new initiatives in the company and I’ve recently decided to write a follow-up book about more advanced QlikView data visualization and analysis.  If you have any requests on what you would like to see in this new book, please send me your comments

As I start investigating the topics and writing, I occasionally stumble upon tips that I’ve forgotten about.  Have you ever done something like the following to respect the current selection of a field in set analysis while ignoring the selections in other fields?

=num(sum({1<[MasterCalendar Year]={$(=concat([MasterCalendar Year],’,’))}>} Amount),’#,##0′)

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Great Opportunity to Stock Up on Qlik Books from Packt Publishing

Packt Publishing is celebrating its 2000th title, so if you were waiting for a reason to buy all those Qlik books from Packt Publishing, here’s a great opportunity to buy one and get another one free.

Of course, there’s my book Learning QlikView Data Visualization and I’m going to take this opportunity to buy QlikView Server and Publisher and QlikView Scripting, which are the two Qlik books I’m missing from my library.  If you’re one of the few that doesn’t have the now classic QlikView 11 for Developers or QlikView Developers Cookbook then this is an offer you shouldn’t let go by.

A couple years ago there were no books about Qlik.  I don’t know the story behind why Packt decided to publish all these QlikView books, but thanks to them and a couple other publishers Qlik now has a growing list of books to choose from.  I hope this is just the start.

Act fast though.  The offer is good until March 26th.

Karl

Advanced Functions – Exhaustive DSO using dollar-sign expansion with parameters

As everybody probably knows by now, the book “QlikView 11 for Developers” by MIguel Garcia and Barry Harmsen is an excellent resource.  Other than the cover photo and the title that could lead someone to think developing Qlik involves hardcore programming; and the space wasted on showing the pie, grid, and funnel chart, I enjoyed reading what is to date the best didactic material about Qlik.

Even though I’ve worked with Qlik for eight years, I learned a few tidbits from the book.  For example, I hadn’t noticed that you could add parameters to variables, and with the help of dollar-sign expansion, pass parameters to those variables.

I avoid using techniques in Qlik that make it feel like you’re programming because I believe Qlik should be as simple and as open as possible for business users.  However, sometimes the benefit of using advanced techniques is too great to ignore.

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Set Analysis – New and Lost Customers with Nested Advanced Search

I love the analysis that can be done using Qlik’s Set Analysis, but its syntax leaves too many business users lost and confused.  Those same business users that use Excel today for their analytical needs and aren’t completely convinced that Qlik is accessible enough to perform their own custom analysis.

I believe that one day applying simple and complex business rules will be made easier, but until then, I’m going to share tips and tricks to keep Set Analysis syntax as simple as possible.  Additionally, I will use examples of commonly used indicators that you can re-use for your own Qlik applications.

One of the confusing attributes of Set Analysis syntax is knowing when to use double quotes ( ” ) and when to use single quotes ( ‘ ).  According to Qlik Help (a great resource),

A search is always defined by the use of double quotes, e.g. <Ingredient = {"*Garlic*"}> 
will select all ingredients including the string ’garlic’.

A search can also be based on a formula or a range.  For example, <Customer = {“=sum(Sales)>10000″}> will select all customers that have a sum of sales greater than ten thousand and <Year={“>=2008<=2010″}> will select all including between and including 2008 and 2010.

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