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Hello from a new user!

Hello all!

My name is Alex Smith. I'm starting a career change from a design and marketing role of 15+ years into a product manager role on the Innovation and Data Management team here at a large nonprofit in Houston, TX.

I've enjoyed art and design quite a bit, but I'm interested in shifting direction to a data visualization role. Feels like drinking from a firehose to learn all about the data architecture within the organization before I can start being self-sufficient in both user requests as well as anything I'd like to build myself from scratch.

Some things I'm doing to try to make up ground as fast as possible:

• Self-paced learning - going through the tutorials to get familiar with the environment
• Attending live trainings and workshops - currently going through the New Power User Orientation as well as attending webinars (signing up even with scheduling conflicts in order to get access to the recording), and hands-on trainings (found the sandbox workshop a little tough to keep up with)
• Going through existing ETLs to understand in-context use cases for my organization by renaming icons to be more explanatory and filling in the notes in order to articulate what's happening. Plan to review those later with my supervisor to ensure accurate understanding.

On a lighter note (per the introduction suggestions), one of my hobbies is one of naturalist studies. Going out and making photographic observations via apps like iNaturalist, where I can make species lists relative to particular areas, is pretty exciting. I would love to be able to use that data to tell interesting stories or share reports with fellow hobbyists. There are also related websites like the Global Biodiversity Information Facility which could also provide data to play with.

Thanks for reading!

Domolex

3

Re: Cumulative Sales between December and January not calculating correctly.

The problem here is that when you use DATE_FORMAT the output is no longer a date but a STRING, and the sorting of it is not the same as the sorting you're displaying in your table. You'll need to play with your ORDER BY clause to make sure that 01/01 comes after 12/31 (I'd usually use a year indicator like 01 and 02), haven't tried but something like this might work:

SUM(SUM(CASE WHEN DATE(created_at) < '2023-02-01' THEN order_net END ))

OVER (PARTITION BY YEAR(created_at) ORDER BY

CASE

WHEN YEAR(created_at) = '2024' AND DAYOFYEAR(created_at) <= DAYOFYEAR(CURDATE()) THEN DATE_FORMAT(created_at,'02/%m/%d')

WHEN DATE(created_at) > '2023-02-01' THEN DATE_FORMAT(created_at,'01/%m/%d')

WHEN YEAR(created_at) = '2023' AND DAYOFYEAR(created_at) <= DAYOFYEAR(CURDATE()) THEN DATE_FORMAT(created_at,'02/%m/%d')

WHEN YEAR(created_at) = '2022' AND DAYOFYEAR(created_at) > DAYOFYEAR(CURDATE())  THEN DATE_FORMAT(created_at,'01/%m/%d')

END)

marcel_luthi

1

Re: List 2 Time Periods in a Table

Depending on how your data is structured, you can use the pivot table chart to do this. Put your date field in the columns section and then your Interest Earned, Net Book Balance and Accrued Interest in the values section and your account number in the rows section. You can use the date range filter and graph by to group the data by month if necessary.

MarkSnodgrass

1

Domo Custom Connector: 3 Design Tips for Building an Effective Product

If you are struggling to build a viable and effective custom connector, a team of Domo users just published an amazing article on Graphable that will help you solve your problem! 💥CODE SAMPLES INCLUDED💥

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GrantStowell

7

Re: Average Calculation less than Trend Points during time period

David,

Ultimately I found this portion below of your answer to be true. Thank you to all for responding to this question.

DAVID's Reponse:

Since your calculation is using "count distinct" in the
denominator and numerator, it could also be theoretically possible for
the monthly and total averages to be different than the monthly averages
if the "distinctness" is distributed differently across months than it
is across supervisors:

In this example dataset, the count(distinct Sales)/count(distinct
Weight) would be 2/1 for each supervisor and 1/3 for each month (very
different!). The overall average would be 2/3, another very different
average that appears to disagree with the by supervisor and by month
trends.