DataMarts secret behind McDonalds?

Data Science Nugget 🧽

Let’s dive into an interesting DataWarehouse concept with a brief case study

Case Study: McDonald’s Data Marts help the Analysts deliver quicker insights.

But what's the use of Data Marts?

Data warehouses contain data from all the business domains and have a complex schema.

Most often analysts/users would only need a subset of the data to focus on a particular line of business or department.

This is where Data Marts serve their purpose.

A Data Mart focuses on a single business line repository (for example sales, marketing, or finance) to serve a narrower user from a single department.

Data Marts's primary goal is to keep things simple, and hence the data is usually denormalized and has a simpler schema.

A Data Mart can be created in two ways:

Top-down: Data is sliced from an existing data warehouse

Bottom-up: Data is directly sourced from transactional databases from specific business domains.

McDonald’s opted for the Top-down approach, a Data Mart built on an existing database platform, Amazon Redshift.

The Data Mart solution allowed McDonald’s to dive into its data for numerous planned analyst uses quickly, but it was also invaluable for unforeseen and emerging needs.

Data Analysis Tool Spotlight 🛠️

Spotfire is a data visualization and analytics software developed by TIBCO Software.

It's designed to help businesses and organizations analyze and interpret complex data quickly and effectively.

Its low floor-to-entry is great for small companies to get started in Data.

Here are some positives about Spotfire:

• Interactive visualizations: Allows users to create dynamic, interactive dashboards and charts.

• Ease of use: Intuitive interface that enables technical and non-technical users to explore data.

• Advanced analytics: Incorporates statistical and predictive analytics tools.

• Customization: Offers extensive options for tailoring visualizations and analyses.

Q&A Section 🙋

This week, I asked my X followers(for a change) the Question:

“What’s a confidence interval?”

Ohhh boy, they didn’t disappoint!

One of my favorite explanations:

“It’s a Frequentist tool that is not a measure nor a probability. It’s a space where if the event was repeated a certain number of times, the event or the estimation could have fit“

Oh, Frequentist statistics is a type of statistical inference that concludes sample data by emphasizing the frequency or proportion of the data.

Another one:

“The length of time you're confident in your results before imposter syndrome kicks in”

People even got creative:

“If we are in a lake and the fish we want to catch is in one spot, a CI is like the method of net throwing we do to estimate where the fish could be. If we threw the net 100x in different spots, 95/100 throws we would’ve captured the fish.”

Here’s a simple one:

“CI is the range of values within which a parameter being estimated lies.“

By far the most liked one:

“In summary, it's a constraint set on distributions to assess whether an observation is different enough and randomness-free at least compared to most observations”

Which one was your favorite?