Smart Data Modeling: From Integration to Analytics

There are numerous reasons why smart data modeling, which is predicated on semantic technologies and open standards, is one of the most advantageous means of effecting everything from integration to analytics in data management.

  • Business-Friendly—Smart data models are innately understood by business users. These models describe entities and their relationships to one another in terms that business users are familiar with, which serves to empower this class of users in myriad data-driven applications.
  • Queryable—Semantic data models are able to be queried, which provides a virtually unparalleled means of determining provenance, source integration, and other facets of regulatory compliance.
  • Agile—Ontological models readily evolve to include additional business requirements, data sources, and even other models. Thus, modelers are not responsible for defining all requirements upfront, and can easily modify them at the pace of business demands.

According to Cambridge Semantics Vice President of Financial Services Marty Loughlin, the most frequently used boons of this approach to data modeling is an operational propensity in which, “There are two examples of the power of semantic modeling of data. One is being able to bring the data together to ask questions that you haven’t anticipated. The other is using those models to describe the data in your environment to give you better visibility into things like data provenance.”

Implicit in those advantages is an operational efficacy that pervades most aspects of the data sphere.

Smart Data Modeling
The operational applicability of smart data modeling hinges on its flexibility. Semantic models, also known as ontologies, exist independently of infrastructure, vendor requirements, data structure, or any other characteristic related to IT systems. As such, they can incorporate attributes from all systems or data types in a way that is aligned with business processes or specific use cases. “This is a model that makes sense to a business person,” Loughlin revealed. “It uses terms that they’re familiar with in their daily jobs, and is also how data is represented in the systems.” Even better, semantic models do not necessitate all modeling requirements prior to implementation. “You don’t have to build the final model on day one,” Loughlin mentioned. “You can build a model that’s useful for the application that you’re trying to address, and evolve that model over time.” That evolution can include other facets of conceptual models, industry-specific models (such as FIBO), and aspects of new tools and infrastructure. The combination of smart data modeling’s business-first approach, adaptable nature and relatively rapid implementation speed is greatly contrasted with typically rigid relational approaches.

Smart Data Integration and Governance
Perhaps the most cogent application of smart data modeling is its deployment as a smart layer between any variety of IT systems. By utilizing platforms reliant upon semantic models as a staging layer for existing infrastructure, organizations can simplify data integration while adding value to their existing systems. The key to integration frequently depends on mapping. When mapping from source to target systems, organizations have traditionally relied upon experts from each of those systems to create what Loughlin called “ a source to target document” for transformation, which is given to developers to facilitate ETL. “That process can take many weeks, if not months, to complete,” Loughlin remarked. “The moment you’re done, if you need to make a change to it, it can take several more weeks to cycle through that iteration.”

However, since smart data modeling involves common models for all systems, integration merely includes mapping source and target systems to that common model. “Using common conceptual models to drive existing ETL tools, we can provide high quality, governed data integration,” Loughlin said. The ability of integration platforms based on semantic modeling to automatically generate the code for ETL jobs not only reduces time to action, but also increases data quality while reducing cost. Additional benefits include the relative ease in which systems and infrastructure are added to this process, the tendency for deploying smart models as a catalog for data mart extraction, and the means to avoid vendor lock-in from any particular ETL vendor.

Smart Data Analytics—System of Record
The components of data quality and governance that are facilitated by deploying semantic models as the basis for integration efforts also extend to others that are associated with analytics. Since the underlying smart data models are able to be queried, organizations can readily determine provenance and audit data through all aspects of integration—from source systems to their impact on analytics results. “Because you’ve now modeled your data and captured the mapping in a semantic approach, that model is queryable,” Loughlin said. “We can go in and ask the model where data came from, what it means, and what conservation happened to that data.” Smart data modeling provides a system of record that is superior to many others because of the nature of analytics involved. As Loughlin explained, “You’re bringing the data together from various sources, combining it together in a database using the domain model the way you described your data, and then doing analytics on that combined data set.”

Smart Data Graphs
By leveraging these models on a semantic graph, users are able to reap a host of analytics benefits that they otherwise couldn’t because such graphs are focused on the relationships between nodes. “You can take two entities in your domain and say, ‘find me all the relationships between these two entities’,” Loughlin commented about solutions that leverage smart data modeling in RDF graph environments. Consequently, users are able to determine relationships that they did not know existed. Furthermore, they can ask more questions based on those relationships than they otherwise would be able to ask. The result is richer analytics results based on the overarching context between relationships that is largely attributed to the underlying smart data models. The nature and number of questions asked, as well as the sources incorporated for such queries, is illimitable. “Semantic graph databases, from day one have been concerned with ontologies…descriptions of schema so you can link data together,” explained Franz CEO Jans Aasman. “You have descriptions of the object and also metadata about every property and attribute on the object.”

Modeling Models
When one considers the different facets of modeling that smart data modeling includes—business models, logical models, conceptual models, and many others—it becomes apparent that the true utility in this approach is an intrinsic modeling flexibility upon which other approaches simply can’t improve. “What we’re actually doing is using a model to capture models,” Cambridge Semantics Chief Technology Officer Sean Martin observed. “Anyone who has some form of a model, it’s probably pretty easy for us to capture it and incorporate it into ours.” The standards-based approach of smart data modeling provides the sort of uniform consistency required at an enterprise level, which functions as means to make data integration, data governance, data quality metrics, and analytics inherently smarter.

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