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.

Define the Business Objective

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.

Make Your Data Models Scalable

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.

Consider Time As an Important Element in Your Data Model

Time-driven events are very useful as you tap into the power of data modeling to drive business decisions. Use datetime enrichment to examine your data in accordance with 11 different properties. View your data by the minute, hour or even millisecond. It’s useful to look at this kind of real-time data when determining things like how many visitors stopped by your page at 2 p.m. yesterday or which hours of the day typically have the highest viewership levels.

One large online retailer regularly evaluates customer behaviors when it launches new products or checks satisfaction levels associated with the company. You could do something similar by using a time-based data model to determine how many people come to a certain section of your website that relates to a new product, for example.

By looking at data across time, it’s easier to determine genuine performance characteristics. Based on what you see, it may be less likely you’ll abort business plans due to hasty judgments.

Avoid Misleading Data Visualizations

There are various ways you could present the information gleaned from data modeling and unintentionally use it to mislead people. For example, you might generate a chart that has a non-zero y-axis. If people don’t look at the left side of the graphic carefully, they may misunderstand the results and think they are overly dramatic. Using colors in certain ways or scaling your charts improperly can have the same effects.

A company involved in aircraft maintenance has recognized the value of presenting data modeling results to stakeholders and regularly uses those insights to make decisions about product development, risk management and contracts.

The brand takes time to analyze things consistently and present content to stakeholders in straightforward ways. This approach facilitates getting external parties on board with new projects and keeping them in the loop about other happenings.

When showcasing data from a model, make sure it’s distributed as clearly as possible. Provide further clarification as necessary in the moment during presentations, too.

Create Valuable Data Definitions

Worthwhile definitions make your data models easier to understand, especially when extracting the data to show it to someone who does not ordinarily work with it. Instead of just creating basic definitions, uphold a best practice and define your data in broader ways, such as why you need the data and how you’ll use it.

People who are not coders can also swiftly interpret well-defined data. Consider that a leather goods retailer with over 1,000 stores needed to analyze data through graphical interfaces rather than complex strings of code. A consulting company specializing in the business and technology sectors came up with a solution to achieve that goal, and informative data definitions likely aided the process.

After poring over these case studies and the associated tips, you’ll be in a strong position to create your first data model or revamp current methods. Anticipate associated knowledge that propels your business.