Data User Experience: First Few Columns Can Make or Break you
Your Data Experience Feels Broken. Why?
The first look at a Data Model can either result into adoption or detraction. There are multiple factors building a robust data model. This can range from data quality, precision, even performance. But the first one that can make or break you…is just a first glance.
“We Need Order“
A valuable strategy for your analytics team and business is the data design and user experience of your data stack, and standardization of column ordering within data models! This minor, but impactful, decision will yield enhanced consistency and inherently intuitive interactions with your data models. Conversely, neglecting these choices can swiftly result in friction, data misinterpretation, and detractors
The following breakdown is a recommendation that we employ or adapt for every project we undertake. It’s straightforward, versatile, and brings predictability to your data models.
The Process
By implementing this strategy, you empower your team to take an active role in sculpting an effective data ecosystem. The standardized column ordering not only enhances usability but also streamlines comprehension and maintenance of data models. In this era of data-driven decision-making, a predictable and intelligently designed data ecosystem sets your analytics team up for success
Primary Key: This singular field signifies the table's granularity. Placing it first ensures utmost clarity regarding the table's essential nature.
Foreign Keys: Including zero or more fields that denote connectable tables follows next. This step aims to elucidate the role of the table within the broader data model.
Dates: Fields conveying factual timing or temporal attributes hold paramount importance. Placing them third acknowledges their foundational role and aids in early comprehension.
Note: Timestamps related to metadata are an exception and are addressed in the final section.
Dimensions: Incorporating zero or more fields spotlighting dimensional insights about the table or entity comes later. This category can even be subdivided to streamline fields by data type—ranging from strings and booleans to semi-structured types like arrays. Given the potential variance in the number of columns in this section, its placement further down the order supports effective scanning.
Metrics: Fields housing numeric information pertaining to the table or entity occupy this category. Typically, these fields are either additive or semi-additive in nature.
Note: Certain numeric data might be non-additive, like ratios or ranks. Depending on their function, they can be grouped with metrics or details.
Metadata: The concluding section encompasses fields denoting the table or entity's source or metadata details. Though these fields might not be pivotal for analysis, they offer utility in maintenance, debugging, error identification, or warnings.
Note: The data types of these metadata fields can encompass numerics, timestamps, and various others. They are grouped based on their function rather than their data type.
By implementing this strategy, you empower your team to take an active role in sculpting an effective information architecture. The standardized column ordering not only enhances usability but also streamlines comprehension, interaction, and maintenance of the data model. In this era of data-driven decision-making, a harmonized and intelligently designed data model sets the stage for organizational success.