Data Modeling Mastery for AI and Beyond

An inordinate amount of some of the most vital aspects of Artificial Intelligence—from data engineering to data science, data preparation to machine learning—rely on one indispensable prerequisite: data modeling.

Without effective data modeling, organizations can’t integrate data across sources to build advanced analytics models. Data modeling is foundational to assembling training datasets, utilizing specific data for end user applications, and scaffolding predictive cognitive computing models.

Consequently, it behooves companies to make the modeling process as efficient as possible to achieve the following three benefits that optimize their modeling endeavors—and the advanced analytics applications and use cases they support. These advantages include:

  • Standardization: When organizations use standardized data models predicated on business meaning, they become machine readable and primed for machine intelligence.
  • Reusability: The capacity to reuse certain aspects of schema considerably expedites the data modeling process while underpinning a broad array of enterprise use cases. Unfortunately, according to Stardog CEO Kendall Clark, “A lot of data modeling is tied to where the data is and how it’s expressed in terms of storage, so it’s not reusable.”
  • Interoperability: Interoperable data is connected and utilitarian for a variety of applications. Semantic interoperability makes application building simpler and easier than it otherwise it.

These advantages are difficult, if not impossible, to realize with traditional relational approaches to data modeling. However, when organizations leverage the schema of semantic knowledge graphs, each of them become immediately accessible, leading to more meaningful AI deployments.

More importantly, perhaps, they also help organizations “be resilient and adaptable and flexible in the face of new challenges for things that come up that we’re not planning for, like long term responsiveness to shifts and market requirements, regulatory requirements, and consumer behavior,” Clark commented.


The standards-based settings of semantic knowledge graphs are immensely helpful for all data modeling needs—whether they directly or indirectly pertain to cognitive computing. Models based on unique identifiers for data, universal standards all data conform to, and established vocabularies and taxonomies solidify data’s meaning beyond any application or use case.

The aforementioned settings are “based on standards around, in particular, graph-based data modeling with a high level of automated assistance from the computer [and] Artificial Intelligence techniques around helping people understand the implications of the data model they built,” Clark explained. For data scientists and data modelers, these implications include staples of “whether they’re correct, whether they meet standards, or if they’re consistent,” Clark mentioned. There are three other immediate gains of this approach, including:

  • Business definitions: The underlying models are based on business definitions and terminology average enterprise end users understand.
  • Reuse: These standards support the reusability of schema and individual parts of their data models. Clark referenced the use of this approach from a large media provider, ITV, for “building some internal standards. Not in the big, fancy, formal legal sense, but the ways they talk about and think about parts of the business. It’s very much in the business’ best interest to align or converge on those standard, reusable ways.” ITV utilizes this standards-based method to connect diverse data sources for content rights management to deliver tailored content to its customers.
  • Machine Intelligence: The machine understandable nature of these data is perfect for heightened machine intelligence.


The ability to reuse different parts of schema and other salient aspects of data models (like vocabularies) is significant for numerous reasons. It accelerates the data modeling process by enabling organizations to effectively leverage the same work across multiple use cases. “When you’re reusing a schema across different use cases, the value there is you’re just, strictly speaking, having to do a lot less work,” Clark confirmed. “And when you reuse you also get that added benefit which is maybe hard to quantify: we’re not just reusing the schema and doing less work, we’re reusing a vetted schema that we have high confidence is correct.”

Additionally, reusing parts of the same schema helps to future-proof the enterprise against whatever changes lie ahead. Schema reusability is widely attributed to the fact that data models aren’t specific to individual applications, but exist at the more granular data layer for use in any app. “When you’re able to reuse schema over time across different parts of your business, you do get that future-proofing benefit, but you get it with high assurance and ROI as well,” Clark concluded.


Ultimately, the employment of semantic standards and reusable schema results in interoperable data for a plethora of applications. These include machine readable apps for low latent analytics and transferring data in the Internet of Things, edge computing deployments, and sophisticated AI processing. Such interoperability is also ideal for rapidly integrating data for training machine learning models or loading applications with multi-domain data for optimizing procurement or supply chain management, for example. As previously mentioned, interoperability also renders applications simpler to build.

From a broader perspective, interoperability means “a move away from app-centricity and a move towards data-centricity,” Clark reflected. “There’s still applications, but the focus is on what the data means at the data layer, what it means to the business, [and this] changes where you put what we used to call business logic.” With this approach, the logic is readily available to all users, transparent, and simplifies creating, deploying, and using applications.

Stellar Data Management

With traditional methods, data modeling is one of the most formidable chores for preparing, integrating, and employing data for predictive models or individual applications. Organizations can reduce the efforts and time this work takes by reusing schema with business understandable knowledge graph standards that culminate in interoperability. Doing so maximizes the value in data management and in the ensuing cognitive computing techniques it supports.

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