The problem with Data Discovery

Despite the power of data discovery tools like Qlik, Tableau, and Power BI, a vast majority of businesses are failing in their efforts to become data driven. Qlik estimates that for their existing customers, there is only a 25% adoption rate (Qonnections 2016). That means that even for companies that have invested in becoming data driven, 75% of their employees are just winging it. Without the right data in the right format for each type of business user, these businesses can end up with entire departments that are not benefiting from data discovery.

There are 2 core reasons why this adoption rate is so low: 1) the right data is not available at all within the Qlik environment; and 2) the data that is available within Qlik is not refined in such a way that it can be useful to the 75%’ers.

If you are struggling with these challenges, you should check out TimeXtender (disclaimer: TimeXtender is an awesome company; disclaimer 2: I work for TimeXtender:). TimeXtender provides software that gets complex data into Qlik, and enables business intelligence (BI) teams to integrate, join, cleanse, denormalize, and otherwise refine the data for each business user. TimeXtender does the job of 3 tools: ETL software (like Informatica and SSIS), database management software (like SQL Server Management Studio), and semantic modeling (like Qlik Data Load Editor). Not only does TimeXtender do the work of these 3 tools, but it does it without required BI teams to write a single line of code.

If you are struggling to increase adoption of data discovery tools like Qlik within your organization, consider whether you are providing all of your users with the data they need and in the format that makes sense to them. If not, take a look at TimeXtender.



Quick Qlik Tip: How to Automatically Identify Relationships in Data Models

Have you ever dreamed of being able to visually explore your data? If you have, Qlik is your dream come true. Qlik, and in particular Qlik Sense, allows you to perform data discovery on just about any data set with relative ease. In this post, I am going to walk you through my favorite way let Qlik help you create valid relationships between tables in an unfamiliar data set.  All told, this article should take 5 minutes to read, and less than 30 minutes if you are following along with the actual Qlik app.

Qlik’s First Question: to Add Data or Not to Add Data

Whenever you create a new Qlik application, you are presented with an option of “Add Data” and “Data load editor.”


The way I think about these differences is that “Add data” is easy, and “Data load editor” is for people who enjoy punishment (they will claim that they prefer greater control over the import process). Both approaches have their strengths, but if you are just looking to jump in with both feet and start exploring your data, I would start with Add data.

If you want to play with a simple dataset, you can download this Excel file that I created to demonstrate some of the fundamentals of adding data to Qlik Sense. I will be using this data for the rest of this post.

Associate Data in Qlik Sense

After you add data, you are going to be slapped in the face with a simple truth: Qlik Sense, which allows you to bring visual meaning to very complex data, has some very complex user interface (Qlik is radically improving this in Qlik Sense 3!). Unless you are just dealing with a single set of data, the first thing you need to do after adding data is associate the data you added. Here is a screen shot that you are presented with…do you see how to create the relationships between your data? Of course you do! It is that little tiny “Associations” button at the bottom of the screen. 🙂


Once you have clicked on the Associations tab, you are presented with an option to associate each of the tables together. For each table to table relationship (e.g., Customers – Theaters), you need to select either “No Association” or one of the recommended associations. For the Customers – Theaters combination, there is no relationship, so you select “No Association” and then select the down arrow to advance to the next table.


NOTE ABOUT OVER-JOINING: Notice how Qlik recommends the association between Customer and Theater with 100% confidence.  There is no relationship between customers and theaters (a person can purchase a ticket at any theater, and the only proper relationship is through the purchase entity). But, because Customer and Theater both have a city field and these have the same values, Qlik recommends a relationship 100%. I still love Qlik for recommending these joins, because it is a lot easier to see a recommendation and determine that it is invalid than having to try to imagine what relationships might exist – particularly on a large and complex data model.

Now that we advance to the Purchases – Theaters table, we see some pretty amazing smarts on display by Qlik: we have the purchaser’s name as “name” and the theater’s name as “name” – but a person’s name (e.g., Matt) is not the same as a theater’s name (e.g., Regal 8). Qlik recognizes that the proper join is between Purchase.Theater and Theater.Name and suggests this association with 100% confidence.


After you go through all of the possible associations, you should have a pretty good object model.


And if you switch over to the Data Load Editor, you will see a nice clean script that Qlik created for your data.

[Name] AS [Theater],
[City] AS [Theaters.City],
[State] AS [Theaters.State],
[Rating] AS [Theaters.Rating];
FROM [lib://Desktop/Movies.xlsx]
(ooxml, embedded labels, table is Theaters);

[FirstName] AS [FirstName],
[City] AS [Customers.City],
[State] AS [Customers.State];
FROM [lib://Desktop/Movies.xlsx]
(ooxml, embedded labels, table is Customers);

[Name] AS [FirstName],
[Movie] AS [Title],
[Theater] AS [Theater],
[Price] AS [Price];
FROM [lib://Desktop/Movies.xlsx]
(ooxml, embedded labels, table is Purchases);

[Title] AS [Title],
[Movie Length] AS [Movie Length],
[Rating] AS [Movies.Rating];
[Movie Length],
FROM [lib://Desktop/Movies.xlsx]
(ooxml, embedded labels, table is Movies);

If you have any questions, drop a comment below.