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MAY CONTAIN BAD ASS CONTENT: ANALYST DISCRETION IS ADVISED

Open your store with Alteryx

James Smith • August 13, 2020

Data sets and Workflows used in Alteryx Webinar

This post contains the content and workflows used in the opening your store with Alteryx webinar.  These data sets will enable you to replicate and expand on the work to help retailers assess the impact of COVID on their business.

The full package of data and workflows is available in the attached zip file or go through and review piece by piece.    ZIP 

Get COVID Cases

The workflow for covid cases is attached below:

COVID WORKFLOW


The prerequisite data sets for this are:

1)  Snowflake data exchange share - starschema dataset (see connecting to `Snowflake)  OR feel free to plug in data from Johns Hopkins which is available HERE (might need some tweaking)

2) Census shape files - available HERE

3) Census 2019 population estimate - available HERE

GET Mobility Data

The workflow for mobility data is attached:

MOBILITY WORKFLOW


The prerequisites data sets for this are:

1)  Google mobility index 

Get Population by Block

This is easy!  Just look at the other post --> http://www.demanddata.io/basic-census-analytics

Bring it all together

The link to this workflow:

Bring it all together


Prerequisites for this workflow.   

1)  Connection to Demand Data retailer database (See post on connecting via Snowflake) -  or import your own locations of interest!!

2)  Mobility to Geo mapping table (maps the name of each county to its coordinates) --> HERE

3) Employee Data (this is dummy data but connect your own!!) --> HERE

4) US zipcodes all match to coordinates.  All US zip codes and their geographic metadata --> HERE


Then you need the ouputs from the first 3 workflows as well.




Alter Snowflake with Dynamic SQL and Python

December 17, 2020
Anyone who follows me knows how much I love the combination of Alteryx and Snowflake. Alteryx provides a logic layer that enables any analyst to manipulate data and Snowflake provides the scale needed to get some amazing insights across large data sets. However, I have found there were always a few things missing. The Alteryx In-DB tools, while comprehensive, cannot do everything. There are some gaps (most notably - ahem - any Alteryx product managers reading this - simple Pivot or Unpivot tools in-DB). There is also a lot of functionality like changing warehouses, schemas or databases that cannot be easily done from within Alteryx.

Why You Need Real Time Right Now

November 2, 2020
In our previous post, we discussed why d emand forecasting matters and how an accurate demand forecast can mathematically and quantitatively improve your service level and/or inventory levels (cost). Now it is time to focus on the biggest uncertainty of them all . Time. Advanced forecasting algorithms can help eliminate uncertainty through reducing variables that are “unknown” by understanding relationships at a scale that no human could comprehend. This helps turn the seemingly “unknown” in to a “known”. As we stated earlier, advances in math and cloud computing make finding these relationships much easier and can be computed almost instantaneously. However, the element of time is a tricky compounding factor. Even little unknowns become big over time as time compounds all errors and introduces more uncertainties. The most certainty you have is in the precise moment you make an estimate. When you look ahead 1 day, 1 week or even 1-month (as most corporate S&OP) cycles still do – then you must start to rely less and less on facts and more and more on assumptions. Assumptions mean uncertainty and uncertainty means cost. There are loads of examples of this. When I lived in Florida, every September during hurricane season , we would get the dreaded cone of uncertainty any time a hurricane formed. As below shows , the further out you get, the expected position gets wider. By day 5 - the uncertainty stretched over 500 miles – hardly accurate enough to do any planning of significance  .

In Supply Chain, Maths is cool again

By James Smith • September 14, 2020
You cannot go far these days without hearing about data science, digital transformation or AI. This is especially true in the supply chain discipline. However, when taking it back to basics, what AI, ML and Data Science are really doing are solving mathematical equations that are hardly new. These equations are just trying to optimize a series of variables to minimize cost and maximize service – something supply chain professionals have been doing for decades. So what has changed? Cloud Computing and Cloud Storage! Cloud Computing is enabling supply chain professionals to perform more math, faster. Cheap Storage is providing the ability to store more data about products and environmental variables which gives more fuel for the math to solve.
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