Saturday, May 27, 2017

Data Profile Tool

I’ve written in this blog before about how I’m often called on to validate a new data file from a client or to examine a new data extract. My first step is to load data into a copy of Steve Dark’s Data Profiler (Steve's original post is useful - search on quickintelligence data profiler). The data profiler gives me information about exactly what values exist in each of the fields.

I’ve made a few modifications to Steve’s original document:
  • Added a table box that adjusts itself to the fieldnames in the file
  • Added a small macro that updates the Statistics Box with the fieldname automatically as different fieldnames are selected. A trigger detects the change and automatically configures the Statistics Box
  • Added a tab to help search for duplicate values in either a field or combination of fields
  • A check in the loadscript to see if the file exists and if it does not exist then it opens a msgbox from the loadscript using a function

You can download an example copy of the file data profiler that I use by clicking HERE

I also keep a version of the data profiler that is tailored for loading data from an Oracle table and a version of the profiler tailored for loading data from a qvd file. I think I use the data profiler tools almost every day to examine data in files and tables to help with QlikView document design and other reporting requirements.

These two examples have no data loaded - you will have to do a reload yourself from your own data:
  • You can download an example copy of the profiler I use for qvd files by clicking HERE
  • You can download an example copy of the profiler I use for Oracle tables by clicking HERE


Sunday, May 14, 2017

Have you got a function similar to SQL NVL ?

I work with a group of people who are all experts in SQL. They all also have varying levels of technical skills and QlikView skills. One question I get frequently is whether QlikView has a function like the SQL NVL function.

For those of you not familiar with relational database SQL language, the NVL function takes two arguments: a field name or expression and a default value to be returned if the first argument is null. For example, a SQL database query may include the function like this:
And that would tell the SQL processor to look at the value of ORD_DISCOUNT and if it is null then return 0 as the function value otherwise return ORD_DISCOUNT.

When people ask about achieving the same thing with QlikView, I usually start by telling them that they can code an “if” statement like this:
and then I explain that there is a built-in QlikView function that can be used similar to NVL as long as the field you are checking is supposed to be numeric. It is the Alt function.
The Alt function accepts any number of arguments and looks at each of them going from left to right and returns whichever one is a valid number. So, repeating our example, if ORD_DISCOUNT is null then the following function will return a zero but if ORD_DISCOUNT is a valid number then the function returns ORD_DISCOUNT:
  Alt(ORD_DISCOUNT, 0)   

The Alt function will treat the last or rightmost argument as an “else” condition and return that value if none of the preceding arguments are numeric. The rightmost value need not be numeric so you could code something like this:
  Alt(ORD_DISCOUNT, 'Discount is missing')

The Alt function may be used in the loadscript code or in chart expressions.
If you are interested, the QlikView Help (search in Help for Conditional Functions) shows an interesting example for how the Alt function can be used to identify a date when the date value may be any one of several different date formats.  


Wednesday, April 5, 2017

Converting Edited Number Text into a Numeric Field

My co-worker, Naveen, had a requirement for a document that would load some financial data from a file that a client had sent to us. The client had created the file with a “screen scraping” type of application that captured the number fields as edited numeric text. For example, a field might contain  ($3,046.10)   So, that example includes a dollar sign, comma as a thousands separator, period as a decimal point, and parenthesis to indicate a negative number or credit.

Naveen needed to load the data as a number. Here’s how it was done:
We used two functions. A Num# function converted the text string into a dual field containing both a text portion and a numeric portion. Then, an outer Num function extracted just the numeric portion. For example, one of the number fields was named AUG. In the loadscript, the line that converted the edited number into a simple numeric field looked like this:
Num(Num#(AUG, '$#,##0;($#,##0)'))

The edit string or format code, the part within the single quotes, can vary depending on your requirement. The edit string I used in the example is a good one for the kind of editing you might find in a financial spreadsheet.

Here's a few other examples of edited number text and how it looks after using this expression:
What now?
         (this is a null)


Wednesday, December 28, 2016

Straight Table with Different Expressions on Each Row

We had a requirement a few months back for a document that would show a table with different expressions on each row along with some subtotals and expressions that reference other rows and columns. It needed to be a single table that could be sent to Excel. It was obviously a requirement that grew out of existing, legacy reporting solutions using Excel but many corporations are wedded to Excel and the larger the corporation, the harder it is to find anyone who believes they have the authority to make changes or do something different.

The solution was to build a document with a straight table with a single dimension that is loaded in the loadscript with an inline load like this:

1, Transportation:
2, Intermodal % of Miles
3, Sea %
4, LTL %
5, Avg Pallets/Truck
7, Total Diesel Cost
8, Diesel $/Gallon
9, Diesel # of Gallons
10, Average distance to Customers

The straight table used KPI_TYPE as the dimension so those text values get listed in the leftmost column like any other dimension. The chart expression had to be slightly different for each column -- some columns were fiscal months, some fiscal quarters, one was a year-to-date column -- but they all looked something like this:
'  '.
'    ',

This is simplified from the actual document.
  • Note that the Pick function in the expression uses KPI_NO which is defined in the inline load. The Pick function avoids “if” statements that can affect speed performance for the table as users change their selections.
  • The dimension isn’t a real data field. Each line in the expression must use the appropriate field names and set analysis to satisfy the requirement for the row. In the example above, variables are used for each row to help make the overall expression understandable at a glance.
  • Because each row is a different expression, the formatting must be done within the expression in the variable using the Num function. Some of those rows are integers, some are in thousands, some are money and some are percentages.
  • Because some of the expressions referred to other rows and columns (using the Above and RangeSum functions), I had to turn off the ability to drag and drop the columns. I also had to lock in the selection of all KPI_TYPE values so that the user wouldn’t accidentally make selections on KPI_TYPE. If the expressions were not using functions like Above then allowing the user to make selections on KPI_TYPE would make the chart more flexible.
  • Note that some of the KPI_TYPE values can be labels and the corresponding row in the expression is just a blank line. (Make sure the chart properties are not going to hide a row of all blanks.)
  • As Aaron mentions in a comment below, something similar can be achieved with a synthetic dimension using ValueList. I think ValueList is better suited to a small number of values but it offers the advantage that a user can't accidentally select one of the values by clicking on the chart; a downside is that it may consume more resources (versus an actual field dimension) when used with a large data model.


Sunday, December 4, 2016

Calendar Table and Preceding Load

Here’s an example of a calendar table built with a series of preceding load statements. The preceding load technique isn’t essential – this calendar table could have been built with a set of resident tables – but it is a good illustration of preceding load where each level of the load is a major revision of the level below.

This example code comes from a loadscript where the earlier part of the script has already loaded 50 million rows of transaction data. This calendar table part runs in a few seconds. The client for this example uses a July through June fiscal year and I left in the calculations for fiscal month, fiscal quarter and fiscal year (they use a label like FY15/16 for fiscal year).

Remember to read the comments starting at the bottom since that is how the preceding load logic works. Note the Dual function used to make sure the fiscal months sort properly from July to June in charts.

//Calendar table is for all Monday WeekStart dates for the transaction data  
Month(FDATE) as CAL_MO,
dual(Month(FDATE),if(num(Month(FDATE))>6,num(Month(FDATE))-6,num(Month(FDATE))+6))  AS FISCAL_MO,
dual('FY' & if(num(Month(FDATE))>6,   right(Year(FDATE),2)&'/'&right(Year(FDATE)+1,2), right(Year(FDATE)-1,2)&'/'&right(Year(FDATE),2)),
   if(num(Month(FDATE))>6, right(Year(FDATE),2),right(Year(FDATE)-1,2))) as FISCAL_YR,
Pick(Month(FDATE),'Q1','Q1','Q1', 'Q2','Q2','Q2', 'Q3','Q3','Q3', 'Q4','Q4','Q4') as QTR,
Pick(Month(FDATE),'Q3','Q3','Q3', 'Q4','Q4','Q4', 'Q1','Q1','Q1', 'Q2','Q2','Q2') as FISCAL_QTR;
//now, make a complete set of FDATEs between min and max
//without missing any weeks. Autogenerate keyword not needed  
Date(MINFDATE+(IterNo()*7)) as FDATE
While MINFDATE+(IterNo()*7) <= MAXFDATE;
//get the minimum (oldest) FDATE and maximum (newest) FDATE
Weekstart(Min(FDATE)) as MINFDATE,
Weekstart(Max(FDATE)) as MAXFDATE;
//First, get all of the distinct FDATE values that have been loaded 
Load date(FieldValue('FDATE', RecNo())) as FDATE 
AutoGenerate FieldValueCount('FDATE');

I didn’t use it here but a WHERE clause will work with preceding load (WHERE EXISTS… is often useful) The ORDER BY clause seems to cause an error with proceding load but you can use logic like this example where the FDATE values are built and loaded in ascending order.


Friday, November 18, 2016

MonthsBetween Function

A few days ago I needed a calculation in a loadscript that would provide the number of months between two dates. This kind of calculation comes up sometimes with supply chain planning data when the difference between the month a sales forecast is developed and the month that the sales forecast is meant to be applied, sometimes called a monthly lag, can be used to help measure forecast accuracy. QlikView provides a number of date functions but not one that can calculate the number of months between two dates.

I decided to make my own MonthsBetween date function using a document variable with input arguments. A variable like that can be used like a user defined function. I created a variable in the QlikView document and named it MonthsBetween. This is what I typed for the content of the variable (you, of course, can copy and paste from this blog post):
((Year($2)*12 + Month($2)) - (Year($1)*12 + Month($1)))

The $1 and $2 get replaced by two input arguments (which some people might call parameters) when the variable is used. The arguments must be dates. My loadscript (I was loading data from an Oracle database table) looked something like this:

Select * from BUDGET_HIST_TABLE;

For example, if the CREATE_DATE is 1-APRIL-2016 and the APPLY_DATE is 1-JULY-2016 then MONTH_LAG is 3. If the second date is later than the first date then the result will be a positive integer number of months. If the two dates are from the same month then the result is a zero. For my data, the APPLY_DATE is always later than or equal to the CREATE_DATE so MONTH_LAG is always 0 or greater. If CREATE_DATE was later than APPLY_DATE then MONTH_LAG would be a negative number.

The calculation is nothing special -- it must have been re-created thousands of times. But, putting it into a variable makes the calculation easy to use in many places and easy to share. Test it out in a chart expression or advanced search expression

When a variable contains expression code like that and the variable might be used as part of a larger expression in a chart, then it is a good idea to enclose the entire variable contents within parentheses as I did above. It helps prevent QlikView from processing the operators in the larger expression in the wrong order.


Wednesday, August 31, 2016

Is My KPI Getting Worse or Getting Better

We have a lot of QlikView documents that measure various kinds of business planning performance. The measures are usually a calculation of the difference between planned values and the actual values. Then the calculation is normalized by dividing it either by the planned or actual values so that the measure can be shown on a document as a percentage.

Key measures or key performance indicators (KPI) like that, if they are well chosen and well designed, give people an idea of how well a business process is performing. I believe that the measure can be even more useful if we add to it an indication of whether the measure has been getting “better” or getting “worse” over time.

Most of the performance measures we use are aggregation calculations that can be used with various dimensions like customer, product, or geographic region. When you show a measure like that in a line graph where the x-axis is time (for example, weeks or months) the line is usually jagged with up and down measures and maybe a “hump” or two. 

The first thing you can do to help someone figure out if the measure is getting better or worse is to use a trendline. In a line graph, go to the chart properties and pick the expression where you’d like to see the trend, find the trendlines section of the expression dialog and click the Linear check box. Now, click OK and you should see that QlikView has drawn a straight line across your jagged line graph. The upward or downward slope of the line can give a person some idea of whether the measure has been getting better or worse.
Note: I usually modify the Presentation tab of the chart properties and set Trendline Width to a narrow line (maybe 0.5) so it doesn’t compete with the main graph line for attention. 

If you stop reading this post at this point, then you’ve already picked up something useful.

Some users of these documents say that they would like the document to point out the areas where a particular measure is getting very bad so they know where to spend their time or resources. We can use a function already built in to QlikView to provide us with a number that would, if it was a trendline in a graph, be the slope of the line. 
If the line is going uphill from left to right, then that would be a positive slope and if it is going downhill then that would be a negative slope. A flat, level line would have a slope of zero. I’m going to describe here how to build a calculated dimension for a chart that can show the 20% of things where a measure has been deteriorating the worst over time.

First, to simplify the description, imagine that you have taken the performance measure expression from your straight table and put it into a variable. We’ll call the variable vKPI.
The time dimension field in the example is PWEEK.
And we are going to use this calculated dimension in a straight table where it will be labeled 20% of Projects Where KPI Has Deteriorated the Most
The linest function is used to calculate the slope of the imaginary graph line. 
The Rank function will help identify the 20% of worst performing PROJECTs. 
Aggr is used to create an array of PROJECT values for our chart dimension. In this example, the higher values indicate a deteriorating measure but you can flip the calculation around if your logic is reversed. 

,1)-1) / Count(distinct total [PROJECT]) < 0.20, [PROJECT]), [PROJECT])

I know this is one of the more complex topics I’ve tackled in this blog but the result in a document that analyzes key performance indicators is valuable. In the documents where I’ve used this concept I usually provide a method for the user to choose what dimension field they would like to use, so it is not limited to PROJECT so the user could choose PRODUCT, SALESPERSON, or REGION, etc. And then for even more flexibility, I put the calculated dimension into a cycle group made up of other calculated dimensions that identify things that need attention.

In my next post, I’ll show how a related calculation could be used to color code items in a chart so that the items where performance has been getting worse are tagged with red and items where performance has been getting better are tagged with green.