Using Prebuilt Data Models? You Should Be

In 1978, LEGO introduced a brand new line of construction sets branded LEGO Space. The sets in the series included parts and features built for science fiction adventure and were among the first to include the now-iconic LEGO minifigure. Winning toy of the year in Germany and the UK in 1979, LEGO Space helped drive a 50% increase in sales for LEGO that year.

Using prebuilt data models
Image by: Stepan

LEGO’s strategy for LEGO Space – and every other theme that LEGO has launched since that early success – is above all to engage customers by making it easy to open a box and quickly experience the joys and benefits of a finished product. Through detailed instructions and clear pictorials, the toy company made it easy for builders to get started quickly, without limiting their future ability to create just about anything their imagination allowed with the individual bricks included in each set.

This same guided approach to building can be applied to data models, especially useful for achieving fast, low-risk and low-cost models suited for specific vertical industries like retail commerce.

In data engineering, one might equate individual data elements along with their semantic meaning, labels and metadata as the buildable “bricks,” with analytical or behavioral models landed in a data warehouse, ready for visualization or orchestration as the finished sets.

For data practitioners across industries, there are many benefits of having predefined and using prebuilt data models for analysis and activation. The key is implementing a data architecture that supports prebuilt models without restricting the future customization of models and outputs in the process.

Here are a few key benefits of predesigned, using prebuilt data models:

Time and cost savings: Generally employing predefined and prebuilt data models can save time especially while achieving useful analytical and behavioral models. Analysis-ready models eliminate the need for manual data engineering, science and analysis, all of which can be time-consuming and prone to error. Analysts save time that would otherwise be spent developing data models from scratch.

Read the full article on using prebuilt data models: Retail TouchPoints