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The Ancient Art of Data Science

By | Cool Analysis, Featured

We think of data science as a modern discipline that came into prominence with the rise of the information age. The term ‘data science’ only goes back to the 1960s – but the practice of collecting information from which you can understand, interpret, and extrapolate real-world phenomena is as old as human societies. The Ancient Egyptians stored administrative data on papyrus; the Normans used the Doomsday book for census taking and tax purposes. But less commonly know is the Incan quipu.

An administrative quipu from 1400-1532. Photo: ©Jonathan Dorado, Brooklyn Museum

Imagine holding a necklace from which hangs a series of colorful, knotted strands. But it’s not for decoration – what you are holding is a database. In the right hands, it could tell you about the lives of the people who lived in the Incan Empire. They could record the census, taxes obligations, payments, dates of rituals- all stored in a 3-D system. More importantly, a series of cotton strings which could be wrapped up was much easier to transport across a far-flung mountainous empire than sheets of delicate paper.

The quipus (sometimes spelled khipu) are known as the talking knots. Made from cotton or other fibers, these color-coded strings are looped into knots, encoding data on a ‘place-value system.’ The main cord would branch off into sub-cords. The kind of knots would represent a specific decimal value, meaning that any number could be represented.

Want to learn to read knots? Here’s a video.

Reading quipus was a specialized position with the knowledge passed down through generations. That specialized knowledge was lost with the invasion and subjugation by the Spanish Empire, who squashed many cultural practices. But there are still plenty of hard-working scientists, historians, artists, and archeologists dedicated to finding and preserving these ancient databases.

Image of Cecilia Vicuña’s fascinating installation Quipu desaparecido (Disappeared Quipu). ©Brooklyn Museum, 2018

What can we learn from quipus?

  • At the Room40 Group, we believe that data does not equal information. It’s only in the right hands that the quipus began to ‘talk’ and tell us about the immense data they contained.
  • Visualizing and analyzing data can be an art as much as a science. This a great example of a world of data represented not just clearly, but beautifully.
  • Data can be represented by more than just pie charts. When you expand your perception of how data can be tracked and visualized, you open your mind to innovation.

Map of Students of Color in Higher Education

By | Cool Analysis, Featured, Growth strategy

Do you or your organization serve post-secondary students of color?

We can help you find your beneficiaries! We created a visualization of the concentration and distribution of college students of color for one of our clients. Here’s what we found:

Students of color concentration in the United States
  • The individual bubbles represent all colleges and universities in the US that grant at least a bachelor’s degree.
  • The bubble size shows the total enrollment of all students at the academic institution
  • The bubble color shows the percentage of students of color relative to all students with dark blue being closer to 0% and dark red closer to 100%.

A Quick Guide To Developing New Programs and Revenue

By | Cool Analysis, Featured, Growth strategy

Jumping from problem to solution to application is very tempting, but you know what they say about assumptions…

We developed this graphic to demonstrate how nonprofits can grow opportunities for new programs to help their beneficiaries or new sources of revenue. Using a multi-step cycle like this one is a great way to organize your project and bring your ideas to fruition.

Develop New Programs Nonprofit
  1. Opportunity Identification:

    Simply put, you’ve discovered a problem (and they aren’t too hard to find), and you’d like to do something about it.

  2. Idea Generation:

    Get creative and put down as many ideas for solutions as you can. Don’t get stuck on your first practical idea and stop there! Embrace wild, crazy, and out-there. You’ll pair them down later.

  3. Discovery & Investigate:

    Put your best ideas to the test. Research, ask questions and expose your designs to as many people as possible. Don’t get discouraged – ideas need to be road tested so you can go back and improve them.

  4. Prototype Development:

    Start putting your plans into the physical space. The goal isn’t the most perfect prototype but one that clearly explains your problem so you can better input. It can be made out of cardboard as long as people can picture what you’re talking about.

  5. Pilot Launch:

    By this point, you’ve cycled between steps 2-4 more than a few times (if you haven’t- get back in there!). It’s a big deal to get to launch your product or service to a broader audience but keep in your mind that you are still looking for the holes and cracks so you can continue to improve.

  6. Later Versions:

    You’ve got an idea, and it works! You can start to think about creating new versions or replicating your program somewhere else as long as you go back to the discovery stage. Don’t assume your idea will work everywhere.