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.

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  .

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.

This post will give you all the tools and details on how to use weather to drive business/sales to your organization. The high level process will involve taking locations of interest (in our sample, we will use retail locations of major chains) and then establish a custom weather criteria. The output is a list of locations meeting that criteria upon which an action can be taken. We will explore actions in a later post but such actions include things like: creating a custom facebook territory for a campaign, sending customers targeted weather specific offers, inputting into a retailers target stock program, adjusting your stores forecast....the list goes on).

The flexibility and scale of Snowflake enables any user to bust through any misconceptions about data availability or data size. However, it does require a good deal of SQL knowledge to get insights and is more suited to engineers that analysts as direct users. When paired with Alteryx, the power of the Snowflake data engine plus all of the data sets in the Snowflake marketplace become instantly accessible in an intuitive drag and drop interface.

Now that you have connected to the census data, you can begin to explore it in huge detail using Alteryx. In this post, we can use the spatial tools to find local information about any area or zip code in the US. The following link contains the workflow in Alteryx. It is based on a connection to the safegraph data set in the data exchange post . US_POPULATION_BY_BLOCK If you have followed the previous posts and setup a file connection, all you need to do is go to Options-->Advanced Options-->Workflow Dependencies and replace the my connection file with the file you setup to connect to your instance.

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