3 Key Benefits of Pragmatic Data Modeling

In 2024, companies have developed a renewed interest in the benefits of Data Modeling, engaging in pragmatic planning and activities around diagramming requirements. Organizations want to document data architectures to get good Data Quality and overcome challenges. 

Notably, the resolution to each data incident has risen significantly by 15 hours between 2022 and 2023. Furthermore, 80% of data executives and business leaders say cultural impediments — people, business processes, and organizational alignment — prevent a data-driven approach.

However, past efforts in diagraming data architectures have proven difficult. Many organizations attempt to model the entire enterprise system or fail to understand their data solution. Compounding these issues, some companies still rely on older Data Modeling tools, which can intimidate businesspeople.

Consequently, frustration grows within companies, leading to a tendency to skip the modeling process until after a data solution has been built – a code-first approach – or to a rudimentary understanding of their data architectures through tribal knowledge. Unfortunately, these situations often result in a painful process of comprehending data systems and fixing problems retroactively.

To change this experience, pragmatic Data Modeling promises a smoother and more efficient design-first approach, empowering businesses to establish a shared understanding of the meaning and context of their data. Pascal Desmarets, founder and CEO of Hackolade, discussed the benefits of pragmatic Data Modeling and shared his expertise in creating visual tools for NoSQL or non-relational databases to show how such a modern approach leads to better experiences.

Adapting to NoSQL Data Architectures

Modern technologies embrace NoSQL database systems that scale quickly and speedily process large amounts of data. However, they speak different languages.

So, data modelers needed to take on a different mindset. Desmarets explained:

“If organizations do their Data Modeling as they did in the past, with a relational database management system (RDBMS), they waste time. While different RDBMS speak the same language with different SQL dialects, newer technologies communicate very differently. A graph data system written with the Neo4j Cypher is uniquely constructed, and differs from an Avro schema used to serialize and exchange data. Both have nothing to do with the Open API documentation.”

Integrating NoSQL technologies thoughtfully into the larger data infrastructure is crucial for businesses seeking to seize new opportunities and mitigate emerging threats promptly. Despite the steep learning curve for modelers, the proliferation of these systems offers more options for event-driven architectures and microservices, a collection of services that provides a set of functional features for an application.

Data Architecture intricacy will only increase as developers apply open plug-in data structures or write their apps to get more boutique services. Moreover, many firms have a mosaic of different technologies in their data stacks and pipelines. For organizations to make sense of what they are building, Desmarets advises that data modeling tools must speak the language of all these technologies and adapt with consistent translations, known as polyglot persistence.

The Benefit of Polyglot Persistence

Polyglot persistence prevents organizations from losing or having inconsistent or wrong data due to bad translations among Data Architecture components or schemas. Both factors are involved when AI applications hallucinate, create incorrect recommendations, or retrieve the wrong results.

Desmarets explained:

“Schemas represent the data contracts used between data producers and consumers. These contracts need to enforce Data Quality and consistency. Data application systems evolve so quickly, with changes made during sprints. So, a data modeling tool like Hackolade, which supports over thirty target technologies, is indispensable for polyglot persistence and data exchanges.”

Since schemas fly around in all types of directions with so many different non-relational technologies, polyglot persistence is necessary. That way, people and systems can communicate their data concepts effectively.

Purposeful Modeling

Managers often seek solutions to their complex problems that are cost-effective and purposeful. In pursuit of this, some may be tempted to rely solely on industry models, predesigned data mappings that are tailor-made for a business sector, or other generative AI solutions. 

However, starting with industry models or generative AI leads to more work rather than a good result. Desmarets observes that teams overestimate their ability to get a competent model and think they no longer need subject matter experts (SMEs). 

Implementing these agnostic models can result in a mere academic exercise that fails to address business needs. Businesspeople fill in the gaps because they know the vocabulary, terms, and purpose behind the data.

Desmarets suggested using industry models or generative AI resources after engaging with business experts. Then, consulting these technologies as a checklist to ensure necessary functionality is included. He stated:

“Include industry models or generative AI in data modeling, but not as a free-flow prompt that is your starting point. These tools do not function as a magic wand – say where you are a bank manager and ask technology to spit out a data model. That approach is never going to work.”

By involving SMEs in the data modeling process, organizations can ensure that the resulting model serves the specific purposes of their business. They are invaluable resources for creating a data model with a clear and meaningful purpose.