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Business data elements / domains

One of the biggest areas of time spent is in basic data cleaning for raw data - this can be dramatically simplified by taking a hint from the large ETL / Master data Management vendors and making this core Alteryx.

 

Server Side

- Allow the users of the server & connect product to define their own Business Types (what Microsoft DQS calls "Domains")

       - Example may be a currency code - there are many different synonyms, but in essence you want your data all cleaned back to one master list

- Then allow for different attributes to be added to these business types

       - Currency code would have 2 or 3 additional columns: Currency name; Symbol; Country of issue

- Similar to Microsoft DQS - allow users to specify synonyms and cleanup rules.    For example - Rupes should be Rupees and should be translated to INR

- You also need cross business type rules - if the country is AUS then $ translates to AUD not to USD.

- These rules are maintained by the Data Steward responsibility for this Business Type.

- This master data needs to be stored and queryable as a slowly changing dimension (preferrably split into a latest & history table with the same ID per entry; and timestamps and user audit details for changes)

 

Alteryx Designer:

- When you get a raw data set - user can then tag some fields as being one of these business types

      - Example: I have a field bal_cur (Balance Currency) - I tag this as Business Type "Currency"

- Then Alteryx automatically checks the data; and applies my cleanup rules which were defined on the server

- For any invalid entries - it marks these as an error in the canvas; and also adds them to a workflow for the data steward for this Business Type on the server -  value is set to an "unmapped" value.    (ID=-1; all text columns set to "unmapped")

- For any valid entries - it gives you the option to add which normalised (conformed) columns you want - currency code; description; ID; symbol; country of issue

 

Data Steward Workflow:

- The data steward is notified that there is an invalid value to be checked

- They can either mark this as a valid value (in which case this will be added to the knowledge base for this business type) or a synonym of some other valid value; or an invalid value

 

Cleanup Audit & Logs:

- In order to drive upstream data cleaning over time - we would need to be able to query and report on data cleanups done by source; by canvas; by user; by business type; and by date - to report back to the source system so that upstream data errors can be fixed at source.

 

Many thanks

Sean

5 Comments
16 - Nebula
16 - Nebula

cc: @NickJ

 

thanks for the time we spent together talking this through at Inspire Nick - hope you have a safe trip back.

Alteryx
Alteryx

@SeanAdams - thanks for the great conversation too!

 

I've written a rather detailed response that's going to be published via one of the Community Blogs in the coming week or so - stay tuned!

 

Cheers,

Nick

Alteryx
Alteryx

And here's the post: https://community.alteryx.com/t5/Engine-Works-Blog/Patterns-for-Managing-Reference-Data-using-the-Al... - let me know if this is taking your ideas in the right direction?

 

Cheers,

Nick

Status changed to: Inactive
 

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