In a real working environment, there would be more nuance to the data structure that you would investigate before attempting any data transformation. The sample data below is intentionally simple. Use case example 1 - scaffolding for headcount This may require that distinct scaffolding techniques be applied. People data may certainly belong to multiple hierarchies as well, but product data will be structured differently to people data. Where Product is Sold: product ID > store X > location X
Product Detail: product ID> category > broader categoryī. In addition to reflecting different dimensions of time, data related to a product may belong to multiple hierarchies, something like (but not limited to):Ī. People data is likely to be reported at a daily level (meaning you want to know ‘how many people do we have?’ each day), whereas sales and inventory data could be reported at an hourly, daily, or weekly level, or something else entirely. Products are sold and returned stock runs down and is replenished. Customers join and exit an organization on a regular basis and that organization likely wants to know how many customers they have on a given day, as well as how many were gained or lost each day.ģ) Sales and inventory data is a little different from people-related data. They may also want to see this trend for each department, or by other meaningful categories, to understand what is happening in different areas of the business.Ģ) Customer headcount data, similar to HR headcount. But ideally, a business wants to see how this information changes over time. Every business wants to know how many people are employed, hired, or terminated on any given day and this is often available as a point-in-time view.
While there are numerous areas of business where scaffolding can be applied, I’ll discuss three areas here:ġ) Human Resources (HR) data, specifically employee headcount tracking. What kinds of data could this technique be applied to? In the examples below, I use Alteryx for data transformation and Tableau Desktop for visualization and analysis. Another option is using Excel in conjunction with some Tableau Desktop join calculations, however this may not be practical for large data sets, for situations where the Excel file needs to be refreshed periodically, or for when one of your data sources is from Tableau Server. Tableau Prep and Alteryx are two that I have used.
There are a number of tools that you can use for data scaffolding. What tools can you use for data scaffolding? This is an opportunity to use data scaffolding. While this is an efficient way to store data from a database administrator’s perspective, it presents a challenge for analysts like you. However, new rows of data are only written to your data source in order to log changes, specific activities, or updates. You create and then insert records that you can infer must exist (albeit abstractly) or details that must be true, based on existing records.Ĭonsider this typical scenario: you are asked to help discover trends over time to inform business decisions. Merriam-Webster’s online dictionary defines the word scaffold as:ġ) a temporary or movable platform for workers (such as bricklayers, painters, or miners) to stand or sit on when working at a height above the floor or groundĭata scaffolding is, quite literally, that platform or framework to facilitate a more thorough analysis of your data.
What is data scaffolding and why would you need it?ĭata scaffolding is a technique used to fill in pieces of data that are missing from your data source in order to help with analysis and visualization. Plenty of other folks have written about scaffolding ( Phillip Lowe, Carl Allchin, Ken Flerlage), but I’d like to provide some of my own use cases for applying the technique in business settings. You may have even had a data problem in the past where scaffolding could have helped, but you just weren’t aware of it. Even if you’ve never heard of data scaffolding, it’s likely that you have (or will face!) a data problem of your own for which scaffolding is the answer - or at least part of it.
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