Data & Finance for Work & Life
In biology, taxonomy means naming, defining, and classifying organisms. In data, it’s the same, only the process doesn’t apply to all possible data in the world–only the data within a given set. It’s easy to understand why data taxonomy is useful. If you’ve ever looked at a data table, the last thing you want to […]
Intro to Data Taxonomy: Definition and Uses Read More »
Do you write “data are,” or “data is”? What is a “datum”? Is it pronounced “day-tuh” or “da-tuh”? How is this different across industries and cultures? This article shows you how to write and pronounce the word “data,” and how it differs across cultures. Plurality: “Data are” vs. “Data is” Strictly speaking, data is a
Data vs data: “data are/data is” & “day-tuh/da-tuh” Read More »
Data normalization is a crucial element of data analysis. It’s what allows analysts to compile and compare numbers of different sizes, from various data sources. And yet, normalization is little understood and little used. The reason normalization goes under-appreciated is probably linked to confusion surrounding what it actually is. There are easy normalization techniques, such
Data Normalization Techniques: Easy to Advanced (& the Best) Read More »
If you have worked a desk job, you know the importance of good file names. They can be as simple starting with “YYYY-MM-DD,” or as complex as a 5-part underscore-deliminated system. When you have them, you take them for granted. When you don’t have them, you kick yourself when someone asks you to track down
Data Naming Conventions: Get These 5 Types Right or Get Lost Read More »
Data omission is not as spooky as it sounds. When I first read the term, my response was negative, as if “data omission” suggested that someone, somewhere, had criminally sabotaged an important dataset. In reality, data omission is almost always an unintentional error. It’s an expected part of input flaws that should be caught in
What is data omission (in accounting, auditing, & more)? Read More »
Data is all around us, but few understand how to conceptualize it. There are data sets, data tables, data attributes, data fields, databases, data lakes, data marts, data objects, and a whole lot more. You shouldn’t let these terms overwhelm you. Data itself is quite simple. But because it’s so simple, analyzing it’s different faces
Data Item: Definition, Examples, & Use in Coding Languages Read More »
Data fishing is a real threat to the reputation and integrity of data analysis, which explains why analysts get frustrating when they see fishing used to persuade audiences who don’t understand what it is. To understand why data fishing is so frustrating for analysts, just take a look at the history of analytic decision making:
Data Fishing: Definition & Avoiding Bias in Your Analysis Read More »
The first thing that comes to mind when I think of data is information. It’s easy to forget that data is simply a digital reflection of real-world objects and ideas. Just as objects and ideas fade in the “real world,” so too does the data that represents them. This concept is called the life of
Useful Life of Data: Define & Optimize Read More »
Have you ever seen a graph that seemed too good to be true? Or heard a percent-based fact that felt too high or too low? You’re not the only one. The reality is that unbelievable graphs and statistics are often correct, but warped. Their creators use a series of techniques to represent the data in
Data Distortion: What is it? And how is it misleading? Read More »
(Updated) Not all data is good quality, and a common driver of poor quality is a lack of data completeness. Completeness is one of 10 standards for data quality: Accessibility, Accuracy, Comparability, Consistency, Credibility, Relevance, Timeliness, Uniqueness, Reasonableness, and Completeness In a sentence, data completeness is the percent of all required data currently available in
Data Completeness: Definition, Testing, & Quality Assurance Read More »
