2024: Week 10 - Preppin' for Pulse

Challenge by: Jenny Martin

Tableau Product Manager, Libby Knell has challenged us to create a Preppin' Data challenge that gets data in shape for working with the newly released Tableau Pulse. Pulse empowers every employee to make better, faster decisions by tracking a metric’s current value, compared to the past — so what does this mean for the shape of our data? Currently, Pulse works best with:

  • Data that is up to date and recent - the last 1 or 2 years and changes regularly - daily or weekly preferable
  • Data that is complete, without gaps
  • Data that is as granular as possible
  • The names of fields are human readable - let’s not use acronyms that lack context!
  • String values in the data are consistently named
Chin & Beard Suds Co are excited to get started using Tableau Pulse so their employees can make smarter decisions for their flagship store. Their store manager is always on the go and works off their phone daily. With the insights Pulse provides, they’ll be better able to keep track of which products are selling well and might need to be restocked sooner than anticipated. Even better, digests on Email and Slack, as well as Tableau Pulse on Tableau Mobile, meets them where they are so they can stay on top of their daily changing data!

Inputs

1. Transaction Data 

2. Product Table 

3. Loyalty Table 

Requirements

  • Input the data
  • Filter to the last 2 years of data i.e. only 2023 and 2024 transactions
    • This will allow for year on year comparison
  • Create additional rows of data for the days the store was closed, ensuring all other fields will have null values
    • The store is closed on Sundays and Public Holidays
  • Update the Cash_or_Card field so that:
    • 1=Card
    • 2=Cash
  • Join the Product Table
    • You'll need to prepare the join clause fields first
  • Calculate the Quantity of each transaction 
    • Defined as the Sales_Before_Discount / Selling_Price
  • In the Loyalty Table:
    • The Customer_Name is currently reading Last Name, First Name. Update it to read First Name Last Name in Title case
      • e.g. knell, libby becomes Libby Knell
    • Group together the Loyalty_Tiers into Gold, Silver and Bronze
    • Update the Loyalty_Discount to be a numeric field
  • Join the Loyalty Table
  • Create a Sales_After_Discount field to apply the Loyalty_Discount for transactions with a Loyalty_Number
  • Calculate the Profit, defined as:
    • Sales_After_Discount - (Unit_Cost * Quantity)
  • Update the field names to remove all underscores and replace them with spaces
  • Remove any unnecessary fields
  • Output the data
    • If you're working in a tool that allows you to create a Published Data Source, that would be best, as we're preparing the data for Pulse!

Output

  • 14 fields
    • Transaction Date
    • Transaction Number
    • Product Type
    • Product Scent
    • Product Size
    • Cash or Card
    • Loyalty Number
    • Customer Name
    • Loyalty Tier
    • Loyalty Discount
    • Quantity
    • Sales Before Discount
    • Sales After Discount
    • Profit
  • 39,337 rows (39,338 including headers)
You can download the output from here. If you want to check your results. 

After you finish the challenge make sure to fill in the participation tracker, then share your solution on Twitter using #PreppinData and tagging @Datajedininja, @JennyMartinDS14 & @TomProwse1

You can also post your solution on the Tableau Forum where we have a Preppin' Data community page. Post your solutions and ask questions if you need any help! 

Popular posts from this blog

2023: Week 1 The Data Source Bank

Image
 Created by: Carl Allchin Welcome to a New Year of Preppin' Data. These are weekly exercises to help you learn and develop data preparation skills. We publish the challenges on a Wednesday and share a solution the following Tuesday. You can take the challenges whenever you want and we love to see your solutions. With data preparation, there is never just one way to complete the tasks so sharing your solutions will help others learn too. Share on Twitter, LinkedIn, the Tableau Forums or wherever you want to too. Tag Jenny Martin, Tom Prowse or myself or just use the #PreppinData to share your solutions.  The challenges are designed for learning Tableau Prep but we have a broad community who complete the challenges in R, Python, SQL, DBT, EasyMorph and many other tools. We love seeing people learn new tools so feel free to use whatever tools you want to complete the challenges.  A New Year means we start afresh so January's challenges will be focused on beginners. We will use dif
Read more

2023: Week 2 - International Bank Account Numbers

Image
Challenge By: Jenny Martin For week 2 of our beginner month, Data Source Bank has a requirement to construct International Bank Account Numbers (IBANs), even for Transactions taking place in the UK. We have all the information in separate fields, we just need to put it altogether in the following order: Inputs A list of the transactions, with information about the receiving bank account  A lookup table for the SWIFT Bank Codes  Requirements Input the data In the Transactions table, there is a Sort Code field which contains dashes. We need to remove these so just have a 6 digit string ( hint ) Use the SWIFT Bank Code lookup table to bring in additional information about the SWIFT code and Check Digits of the receiving bank account ( hint ) Add a field for the Country Code ( hint ) Hint: all these transactions take place in the UK so the Country Code should be GB Create the IBAN as above ( hint ) Hint: watch out for trying to combine sting fields with numeric fields - check data types Re
Read more

How to...Handle Free Text

Image
Free isn't always a good thing. In data, Free text is the example to state when proving that statements correct. However, lots of benefit can be gained from understanding data that has been entered in Free Text fields. What do we mean by Free Text? Free Text is the string based data that comes from allowing people to type answers in to systems and forms. The resulting data is normally stored within one column, with one answer per cell. As Free Text means the answer could be anything, this is what you get - absolutely anything. From expletives to slang, the words you will find in the data may be a challenge to interpret but the text is the closest way to collect the voice of your customer / employee. The Free Text field is likely to contain long, rambling sentences that can simply be analysed. If you count these fields, you are likely to have one of each entry each. Therefore, simply counting the entries will not provide anything meaningful to your analysis. The value is in
Read more