Business Discovery and Data Integration Simplified | Turning raw data into actionable information

An article I submitted to is4profit.com was approved and published:

If you participate in activities that include searching for that “right” business intelligence tool, you will find that most organizations have these top 3 challenges in common:

Use manual tasks that include desktop query and reporting tools, to answer their business questions.
Have “something” in place that they are not really happy with or is becoming out of date and cost prohibitive.
Have difficulty managing data within multiple silos and have the need to access, consolidate and optimise it.

Hence, they are usually looking for a reasonably cost

Recap

In my last article Metadata Management the Customer’s Way – Part 1, I covered a customer’s specific metadata challenge and proposed a QlikView Expressor (QVE) solution to address it. To summarize, our customer needed to know which existing QlikView applications (.qvw) were in compliance with the newly established rules appointed by the organization’s Business Intelligence Competency Center.

This article and companion video (below) will provide some details about the proposed solution along with QlikView and QlikView Expressor samples. (attached in this post)

We know from the previous article that the customer’s metadata, such as column labels and validation flags, are stored within database tables as part of their metadata management application. For simplicity I will use an Excel spreadsheet to simulate the customer’s metadata repository and focus on the two fields “ColumnName” and “Validated”.

Leveraging the QlikView Governance Dashboard

I’ve implemented this solution using data files produced by the QlikView Governance Dashboard (QVGD) scanning process and a QlikView Expressor Dataflow. The QVGD working data files (*.qvx) contain QlikView deployment metadata such as column labels, expressions and field names. Typically, this metadata creates the associative data model read by the QVGD’s information sheets. Leveraging these data files with QVE’s QlikView Read operator provides significant advantages when developing custom QlikView Metadata Management solutions such as this. Since we already have the data files from the QVGD scan, there’s no need to create a custom program to extract QlikView label metadata from the QlikView applications.

The QlikView Expressor Solution

Using the QlikView Expressor Design Studio a multi-step Dataflow can be created that will:

Extract and load the custom metadata into a QVE Lookup table for later processing
Read and join the appropriate QVGD data files on their appropriate keys
“Lookup” what labels have been validated or not
Provide appropriate business terms to all validated, non-validated and missing labels
Capture the labels that are not in the custom metadata repository and write them back to the repository for later approval
Write the results to a new QlikView data file to be used for analysis in QlikView

Michael Tarallo
Senior Product Marketing Manager
QlikView and QlikView Expressor
@mtarallo

Turning raw data into actionable information

“Well, we haven’t really thought about that”

Metadata Management the Customer’s Way – Part 2: The Solution