The Business Impact of Data Modeling Strategies

0 April 21, 2024

Today, Businesses spend a lot of time, effort, and money on datasets supporting their business operations and analytics. Can your data modeling strategy impact that time, effort, and money expenditure? Absolutely! Dave McComb has stated in his book “The Data-Centric Revolution”, that 40% to 70% of most IT budgets are spent on integration. Therefore, an organization’s cost savings could be in the millions of dollars by eliminating the need for data integration. Beyond that, organizations are facing a high rate of data integration project failures.

Recently, Maxxphase has introduced compatible, directly interoperable datasets. Compatible datasets eliminate your need for complex data transformation-based data integration efforts. With this simplification, you can quickly achieve better results by seamlessly blending these interoperable datasets on demand. In addition, compatible datasets spontaneously form an analytics-ready modular data fabric when properly designed. The resulting compatible data fabric also eliminates your need for traditional data warehouse projects, master data management efforts, and data fabric initiatives.

 

The Disparate Data Modeling Strategy vs. The Compatible Data Modeling Strategy

Traditional data modeling, dimensional data modeling, and data vault modeling styles all belong to the disparate data modeling strategy. The disparate modeling strategy results in instantiation of a disparate dataset foundation where referential integrity between datasets is not enforced. This lack of data integrity between datasets results in the formation of isolated datasets, which are often characterized as siloed datasets.

You can enhance any data modeling style to produce compatible data models and then instantiate compatible datasets. First, compatible data models are characterized as modular plug-and-play data models. Whenever you wish, you can ‘plug’ multiple compatible data models together to form a single data model. Then, using the Compatible Data Modeling strategy results in the instantiation of compatible, directly interoperable datasets. The resulting interoperable datasets have referential data integrity enforced between them. These compatible datasets are best characterized as analytics-ready modular plug-and-play datasets. Compatible datasets spontaneously form a compatible data fabric.

Is the extra design work needed for Compatible Data Modeling worth it from a business perspective? The compatible data modeling strategy eliminates the need for subsequent data transformation-based data management efforts. Maxxphase Data Compatibility Standard (DCS) data entities are specifically designed to support all the data functionality a business needs. Beyond the savings in time, effort, and money, the resulting compatible dataset foundation is far superior to the disparate dataset foundation. The compatible dataset foundation is more agile, reliable, and scalable, with higher-quality data content, better security for sensitive data, reduced complexity, end-to-end data integrity, and better support for modern technologies such as AI/ML.

 

Transitioning to a Compatible Data Modeling Strategy

The transition from a traditional data modeling strategy to a compatible data modeling strategy is relatively easy and noninvasive. Compatible data models require a framework composed of DCS data entities. New as well as existing data models can each be encapsulated within the Data Compatibility Framework. It is this framework of specifically designed DCS data entities that provide all the added data functionality desired from a compatible data model. This added data functionality includes the plug-and-play connectivity, all the capabilities of a data warehouse, and complete master data standardization. The business impact is reduced data foundation complexity while relying far less on hand-crafted data transformations software.

Data Compatibility is a smarter way to work. You remove the data challenges inherent in your isolated disparate datasets by committing more effort to data design. With data compatibility, your business impact is a streamlined IT organization with a more agile data foundation.