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Sisu 101

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Welcome to Sisu Data! Whether you are new to data analysis, or you’re well-versed in the subject, this article is a good place to start in orienting yourself to Sisu and how it can help you get the most out of your data.



Data-Driven Decision Making

The purpose of data analysis is to help inform your business decisions based on facts you learn from your data. Exploring, analyzing, and fully understanding these data-driven facts help you and your team make more informed decisions, enact better policies, and implement more productive practices that serve to effectively “move the needle” of your organization’s success in the right direction.

Sure, you can make these decisions based on “gut feeling” and anecdotal evidence -- and you may have gotten along pretty well by doing so in the past. However, hidden deep within your data are valuable insights that you simply can’t see on your own. For example, did you know that sales of chocolate-peanut butter granola bars dropped among women under 30 in your New York stores, but are on the rise among the same customer cohort in Los Angeles? And the impact of that fact is affecting your Los Angeles stores quite heavily? Maybe it’s time to make a product offering change in Los Angeles, and promote the same product more heavily in the Northeast with targeted marketing to young women.

Additionally, more and more organizations around the world (possibly your competitors) are waking up to the power of data. Congratulations on making the same decision they are!

So how do you begin to make data-driven decisions? This graphic provides an overview of the basic steps. The highlighted steps are the ones Sisu helps you with.

Data-Driven_Decision_Making_2.png

First you define a Metric, which is basically the question you want to answer with your data (e.g., Average Sales Order Value). You then collect all of your data (you can either upload a CSV file or connect to your data warehouse)... and Sisu takes it from there!

Your Sisu dashboard provides you with Facts about Subgroups in your data as well as the degree to which they Impact your selected Metric. From there, your Analyses might expose further questions you want to answer. You can make adjustments to your Analysis as needed and Run it as many times as you want to iteratively analyze your data until you reach the answers you need.

 

Designing Your Data

In analyzing data, generally you start with a business question you want answered (i.e., what is affecting my average sales order value). You then define a Metric that Sisu uses to develop the Analysis to answer that question. You can further define the Analysis by adding filters or writing a SQL query to overlay the data.


Design_Your_Data.png

 

Ideal data sets must consider:

  • Granularity: Is the data at the right grain for the metric definition? What does each row mean?
  • Enrichment: Is the data rich enough to contain the factors of interest?
  • Actionability: Are the factors present in the data actionable to the stakeholder? Are these actions under your control?
tip_icon_-_small.png For more information, refer to Understanding Data Design.



The Sisu Framework

Sisu helps you organize your Analyses so you can run and maintain multiple projects, metrics, and results.

 

Sisu_Framework.png

You’ll begin with a Project, which is a collection/folder of Metrics. You can create multiple Projects. 

Within each Project, you’ll define one or more Metrics. Sisu is metric-centric, which means you can configure and run multiple different Analyses within each Metric.

Sisu offers three different types of Analyses:  General Performance, Time Comparison, and Group Comparison.

 

 

Next Steps

We recommend that you read through the remaining articles in the Getting Started section to ensure that you have a solid understanding of Sisu’s basic concepts.

Once you’re ready, you can configure your first Analysis!

 

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