Ravel: The Goldilocks Program for Data
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Ravel is a whole new way to make sense of data. Before Ravel, there were three main tools for analysing numerical information:
· Spreadsheets, where Excel dominates the market;
· Business Intelligence programs like Tableau and Microsoft Power BI; and
· Data-oriented programming languages, like Python, Julia, and R.
Rather like the beds in Goldilocks and the Three Bears, these existing programs are either too hard, or too soft:
· Anyone can use a spreadsheet. But though it’s easy to load data, analysing it involves writing unintelligible cell reference formulas and tiresome manual formatting of data;
· Business Intelligence programs use Pivot Tables to extract data. Very few people master Pivot Tables, so these programs are the province of data professionals.
· Programming languages like Python are for coders only.
Ravel is the Goldilocks data analysis program. At its heart is an intuitive tool for manipulating data that we call a Ravel, which anyone can use:
A Ravel is far easier to manipulate than a Spreadsheet. Here is the raw data for the USA’s inflation-adjusted house price index and house price change in Excel.
Graphing this would involve several more tedious steps. Here is the same data, which is easily selected using a Ravel, and quickly graphed:
Ravel also includes easy to write and read, self-documenting flowchart equations: this makes it ideal for anyone who wants to explain their analysis to anybody else—which is everyone. It is especially useful for anyone who is legally required to document their work—such as auditors and actuaries.
Ravel’s formulas are far more powerful, and far easier to write, than Excel cell-reference formulas. A cell reference formula return a value for a single cell. Ravel’s formulas operate on variables, and return values for every element in a variable.
For example, one of the many databases maintained by the Bank of International Settlements records debt by several dimensions, including the country, the sector doing the borrowing, and by date. This is a slice of that file:
The data on debt is very useful, but it would be good to have data on change in debt too—both the annual change in debt in domestic currencies, and as a percentage of GDP.
You can work out GDP by dividing debt in domestic currency by debt as a percentage of GDP, and multiplying the result by 100; then you can work out the annual change in debt (“Credit”) by subtracting one quarter’s data from the data four quarters earlier; finally, you can calculate the ration of Credit to GDP by dividing Credit by GDP and multiplying by 100.
The cell formula to work out GDP for the 4th quarter of 2023 in the truncated file below is “=100*LD2/LD3”, the formula for credit is “=LD2-KZ2”, and the formula for credit as a percentage of GDP is “=100*LD5/LD4”. After you’ve replicated these formulas across the 316 columns of data, you can then produce the graph shown below, for private debt and credit as a percentage of GDP for the USA.
That’s for one of the over 40 countries in the BIS database.
The Ravel file below produces exactly the same graph. But unlike Excel, the formulas involved are both visible and easily read.
Secondly, Ravel’s formulas have done the calculations for all quarters and all countries. To see another country—say, Spain—all you have to do is move the selector dot on the Ravel. Ravel’s easy to write and read formulas replace thousands of difficult to write and invisible cell reference formulas.
As a brand-new program, Ravel still has some rough edges. Our plots aren’t as fancy as Excel or Tableau, and there are some plot types—like Pie Charts—that we don’t yet support.
But we’ve got the basics right, with the easiest and most powerful tool ever for manipulating data, and formulas that are easy to write and read. The bells and whistles will come.
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