6 Data Modeling Tips You Should Know

6 Data Modeling Tips You Should Know

354
0
SHARE

Even the best data is totally useless if it cannot be consumed by your organization’s systems. Per Forrester, as much as three-quarters of data in an enterprise goes unused for analytics.

Most companies are not intentionally neglecting data. Many would like to harness their raw information but struggle mightily to understand what they have because of poor analytical solutions.

The process of defining and categorizing data, otherwise known as data modeling, is vital to make sure information can be used to improve company performance in a variety of areas.

Data modeling from a strategic standpoint is concerned with figuring out what data is needed for specific business processes. Analytical modeling emphasizes categorizing and describing existing data.

According to Dataversity, no matter the purpose, a good data model is scalable, quickly consumed, and always offers predictable performance. The overall goal of any data model should be to help an organization operate more efficiently.

Data modeling must lead to results that satisfy end-user requirements and questions in an accessible manner.

Here are six data modeling tips to keep in mind to get the most utility for your business.

Automate Whenever Possible

There are a wide variety of modeling methodologies to choose from. A flat model is a simple 2D array of data. Others, like a data vault model, incorporate hub, satellite, and link tables to record long-term historical data from multiple sources.

Don’t feel like you have to shop around once you find something suitable, though.

Automating a reliable data modeling methodology has saved many organizations a lot of time and money. One business that serves more than 20 million executives once dedicated 35 workers to build 150 data models in a process that often took weeks or months.

This drawn-out system slowed down business operations.

After turning towards an automated approach, output rose to 4,800 individual predictions supported by five trillion pieces of information. Automation allowed for more meaningful use of data and enhanced the organization’s capabilities.

Create Pertinent Data Definitions

Models with clear and concise definitions are easier to digest, especially if you have a team of people who were previously unfamiliar with the information.

Broadly defining data with why it is useful and how it will be leveraged brings broader context to the information.

Worthwhile definitions also make it easier to turn data into different visual interfaces, like charts, graphs, and maps, that are accessible to non-coders.

Check Closely for Mismatches

Data mismatch is one of the most common data modeling mistakes. Information that is not in the right format, like the number “4” (a string) in a “number of items” property, is going to hinder your ability to process and work with the data.

In general, use numbers instead of strings to make sorting results easier for data models.

Make Your Models Able to Evolve

Data models should never be set in stone because priorities (and technology) change over time.

Storing data models in an accessible repository makes it possible to swiftly update or expand a data model to tackle a new task. It is also smart to use a good data dictionary to keep abreast of pertinent information about formatting.

Look at Data Across Timespans

Data modeling with the element of time is a powerful way to get insights that can play a crucial role in business decisions. The type of business you operate will dictate how granular of timespans you want to track, but in our digital economy, the question is probably a matter of milliseconds, not seconds.

Modeling data with time using platforms AI-driven platforms like ThoughtSpot not only makes it much easier to understand performance characteristics through various business environments but also alerts users of hidden trends as well. This helps mitigate against hasty business decisions often carried out in the absence of concrete information.

Verify Judiciously and Carefully

Every action in a data model should be double-checked before moving on. This will help mitigate potential errors and let you home in on problems before the data reaches end users. Data models with a lot of mistakes can quickly become unmanageable and complex to the point where the information becomes useless.

Big data is vital to making sound business decisions. Good modeling practices will allow actionable information to shine through and improve business outcomes. A good grasp on data modeling makes it easy to overhaul current methods or come up with a new model for a particular situation.