Kaushik, one of our forum members has an interesting problem.
Need to quickly visualize 3 variables ( Company, years, Financials) in a single […] chart.
Multiple variables over several years – How to chart?
Let’s take a look at the data first.
Ways to visualize such data…
Whenever you want to visualize business data, the first thing to ask is,
- What is the purpose of this visualization?
Related: How to create right chart from your data?
Since we don’t know why Kaushik wanted to visualize all 100 numbers in a single chart, let’s define some goals for the chart.
- Yearly trends of financial metrics
- How one company compares with another over years
- How individual metrics contribute to overall revenue (all the 5 metrics add up to 100)
- What are the most interesting numbers, drastic changes or alarming trends
One way to answer these questions is thru a panel chart. It shows massive data in a concise chart and allows for exploration of inter-relationships.
See below.
Absolute trends:
Made with sparklines.
Indexed Trends:
And of course you can use indexed charts technique to explore the numbers better.
Your challenge – How would you visualize this data?
So here is something fun for you. Visualize this multiple variable data and share your charts.
What’s in it for you?
This is a contest. The two most awesome charts will $100 Amazon Gift Cards.
How to submit your entries:
- Simple. Download the data workbook.
- Create your chart in a new tab.
- Email your files to chandoo.d@gmail.com with the subject “Multiple variable challenge”
- Send in your entries before July 4th, 2016 (Monday).
- We will showcase all the entries and pick winners by July 11th.
Fine print:
- You may submit multiple entries, but you can maximum of one gift card.
- No VBA based solutions. Excel or Power BI charts are ok.
- If you cannot accept Amazon gift card (because of where you live), you can opt for $100 cash thru PayPal.
Get busy charting…
One Response to “SQL vs. Power Query – The Ultimate Comparison”
Enjoyed your SQL / Power Query podcast (A LOT). I've used SQL a little longer than Chandoo. Power Query not so much.
Today I still use SQL & VBA for my "go to" applications. While I don't pull billions of rows, I do pull millions. I agree with Chandoo about Power Query (PQ) lack of performance. I've tried to benchmark PQ to SQL and I find that a well written SQL will work much faster. Like mentioned in the podcast, my similar conclusion is that SQL is doing the filtering on the server while PQ is pulling data into the local computer and then filtering the data. I've heard about PQ query folding but I still prefer SQL.
My typical excel application will use SQL to pull data from an Enterprise DB. I load data into Structured Tables and/or Excel Power Pivot (especially if there's lot of data).
I like to have a Control Worksheet to enter parameters, display error messages and have user buttons to execute VBA. I use VBA to build/edit parameters used in the SQL. Sometimes I use parameter-based SQL. Sometimes I create a custom SQL String in a hidden worksheet that I then pull into VBA code (these may build a string of comma separated values that's used with a SQL include). Another SQL trick I like to do is tag my data with a YY-MM, YY-QTR, or YY-Week field constructed form a Transaction Date.
In an application, I like to create a dashboard(s) that may contain hyperlinks that allow the end-user to drill into data. Sometimes the hyperlink will point to worksheet and sometimes to a supporting workbook. In some cases, I use a double click VBA Macro that will pull additional data and direct the user to a supplemental worksheet or pivot table.
In recent years I like Dynamic Formulas & Lambda Functions. I find this preferable to pivot tales and slicers. I like to use a Lambda in conjunction with a cube formula to pull data from a power pivot data model. I.E. a Lambda using a cube formula to aggregate Accounting Data by a general ledger account and financial period. Rather than present info in a power pivot table, you can use this combination to easily build financial reports in a format that's familiar to Accounting Professionals.
One thing that PQ does very well is consolidating data from separate files. In the old days this was always a pain.
I've found that using SQL can be very trying (even for someone with experience). It's largely an iterative process. Start simple then use Xlookup (old days Match/Index). Once you get the relationships correct you can then use SQL joins to construct a well behaved SQL statement.
Most professional enterprise systems offer a schema that's very valuable for constructing SQL statements. For any given enterprise system there's often a community of users that will share SQL. I.E. MS Great Plains was a great source (but I haven't used them in years).
Hope this long reply has value - keep up the good work.