Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process. Previously, we looked at an overview of the methodology as a whole as well as the Business Understanding, Data Understanding, Data Preparation, and Modeling stages.  Next, we examine the Evaluation stage.

Lucas Wood will be presenting the webinar “Getting Started with Data Science in Spotfire” on  July 12 12:00pm CST

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Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process. Previously, we looked at an overview of the methodology as a whole as well as the Business Understanding, Data Understanding, and Data Preparation stages.  Next, we examine the Modeling stage.

We have finally reached the fun part!  We have reached the step where we can move from a descriptive look back to a predictive look forward.  In the Data Understanding phase, we discussed the 80-20 rule, which states that data professionals spend 80% of their time cleaning data, akin to the tedious hours of practice preparing for the big game.  Hopefully, we have shown through phenomena like Simpson’s Paradox that even the Data Understanding/Preparation stages can bring intriguing insight; however, the Modeling stage represents the true opportunity and most intellectually stimulating phase.

Are you looking to spice up your Spotfire dashboard with a custom KPI? One easy way to do this is to utilize value boxes in your dashboard. Value boxes allow you to display numeric or text values from Spotfire dynamic items or property controls. These are not only a great way to improve the visual quality of your dashboard, but also a great way to highlight important summary information for your users.

• Have you ever had issues with tags not recalling marked data?
• Have you named key columns but still lost tags when the data updated or changed?

Here is a quick workaround for loading files into Spotfire analyses published via the web player. Sometimes network drives and online sources can throw Spotfire Web Player for a loop, giving you the error that the data table cannot be found.

Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process.   Previously, we looked at an overview of the methodology as a whole as well as the Business Understanding and Data Understanding stages.  Next, we examine the stage of Data Preparation.

Got a question today about the Well Log visualization that I thought I would work through here. We haven’t done much write up on the tool so I thought I would take a moment and sketch out this use case.

• Have you ever wanted to get rid of a filter column in Spotfire and never, ever see it again?
• Have you been frustrated with having to organize and hide filter columns you know you will never need?

Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process.   Previously, we looked at an overview of the methodology as a whole as well as the first step, Business Understanding.  Next, we examine the stage of Data Understanding.