6 data modeling best practices for better business intelligence

It’s crucial to understand data modeling when working with big data to solidify important business decisions. Although specific circumstances vary with each attempt, there are best practices to follow that should improve outcomes and save time. Here are six of them.

The sheer scope of big data sometimes makes it difficult to settle on an objective for your data modeling project. However, it’s essential to do so before getting started. Otherwise, you’ll waste money or end up with information that doesn’t meet your needs.

Focusing on your business objective may be easier if you think about problems you’re trying to solve. An emergency health care facility became frustrated while having to rely on its IT department to run reports based on big data insights. After working with a consultant, it implemented a way for end users to independently run reports and see the information that mattered to them, without using the IT department as an intermediary.

Data modeling makes analysis possible. After realizing the difficulties that arose when working with the data, the health care company decided its business objective was to make the data readily available to all who needed it.

Pick a Data Modeling Methodology and Automate It When Possible

There are various data modeling methodologies that exist. You might go with a hierarchical model, which contains fields and sets to make up a parent/child hierarchy or choose the flat model, a two-dimensional, single array of elements.

After deciding which data modeling method works best, depend on it for the duration of a project. Sometimes, you may use individualized predictive models, as with a company that dealt with five million businesses across 200 countries. That entity used 35 workers to create 150 models, and the process often took weeks or months.

After switching to a fully automated approach, the company increased output to 4,800 individual predictions supported by five trillion pieces of information. If you often realize current methodologies are too time-consuming, automation could be the key to helping you use data in more meaningful ways.

Just as a successful business must scale up and meet demand, your data models should, too. Consider working with companies that provide tools to help you quickly modify your existing processes.

A major American automotive company took that approach when it realized its current data modeling efforts were inefficient and hard for new data analysts to learn. It remedied the problem using a tool that relied on an automation strategy for both data validation and model building. After implementing that solution, data analysis professionals could design new models in days instead of weeks, making the resulting models more relevant.