The remarkable ascent of headless commerce architecture has enabled companies to disengage their e-commerce storefronts (frontend presentation layers) from their backend commerce APIs, leading to significantly enhanced flexibility and scalability in comparison to inflexible monolithic platforms.
The adoption of advanced APIs that enable modern systems to communicate with each other, exchange information, and share services has led to the development of contemporary commerce solutions that empower businesses to offer digital experiences that were impossible in traditional architectures.
The Need for Data and API Orchestration
However, the rise of composable architectures has prompted a reevaluation of how content is modeled and shared across the enterprise for various channels and digital products. In recent weeks and months, there has been a growing focus on the topics of Data and Content Orchestration as organizations struggle with an ever-growing number of data silos exacerbated with the transition towards composable architecture. The big question is, what is the best way to deliver meaningful, consistent and connected experiences to the end user on a variety of digital touchpoints when your data, content and business logic resides in multiple systems of record.
In the past, I’ve spoken a lot about real-time API orchestration in a composable tech stack. I’m a big proponent of orchestrating data and content straight from its source, when it makes sense. The preconditions for real-time orchestration are as follows:
- Backend systems are accessible via APIs
- Performance and scalability can be achieved as the data is published on a CDN or cached close to the edge, which is typically the case with headless CMSs, Commerce Engines, Promotion engines, etc.
- The data in the system of record is ready to be consumed by downstream systems i.e it doesn’t require further metadata and semantic enrichment, classification, tagging, and cleansing.
- The logic to stitch together data from multiple systems into a data model is not overly complex
- The data and content that is sourced from back end systems relies on real-time context of the customer or there are dependencies between API inputs and outputs.
When Real-time API Orchestration alone is not enough
Legacy and homegrown systems oftentimes don’t meet these preconditions. You may be wondering, why not just migrate to a CMS in that case? If you’ve done a migration project of any kind, you probably know how painful, expensive and time consuming it can be. In some cases, it actually doesn’t make sense to migrate at all because there is reliance on these systems for use cases outside of the current digital experience initiative. Organizations also have no real appetite to spend huge amounts of time and money on something that doesn’t offer immediate ROI. The thought process goes like this, if it ain’t broken, don’t fix it.
In such scenarios, an operational data store that sits on top of your legacy systems is a better way to go. In a retail architecture, an ODS can be used to store data from various sources such as point of sale systems, inventory management systems, and customer relationship management systems. It can be used as a central repository for all operational data, providing a single source of truth for the entire organization.
One of the most common use cases for an ODS in retail is product catalog management. Retailers often have large and complex product catalogs, with many attributes and relationships between products. An ODS can help to manage and make sense of this data, allowing retailers to easily navigate the catalog and identify relationships between products. For example, an ODS could be used to model the relationships between products and their attributes, such as color, size, and material, and provide a powerful search and filtering interface for customers.
Benefits of Operational Data Stores (ODS)
Here are the benefits that this approach provides:
* Gives you a way to gradually migrate away from legacy systems, which is less disruptive to the business operations
* Allows you to connect data and content through relationships that may not be as straightforward as a foreign key in traditional relational databases
* Eliminates the need to migrate and lift/shift content from its source in the short term
* Connects data and content from more than one source through dynamic relationships that can be resolved in advance, prior to making the content available to real-time consumers
* Provides a way for business users and data owners to view, search and validate all of your content from all the backend systems in one centralized interface
* You can enrich the content with additional metadata, tags and taxonomies for personalization and relevance
Why should your Operational Data Store (ODS) be a Knowledge Graph?
A knowledge graph is optimized for modeling and representing complex relationships between different entities. It provides a flexible and powerful way to represent data that goes beyond the traditional flat and rigid structure of a relational database. Here are some benefits of a knowledge graph over a relational database:
Flexibility: Unlike a relational database, which requires data to be structured in a specific way, a knowledge graph allows for more flexible data modeling. This means that it can more easily handle complex relationships between entities, including many-to-many relationships.
Better Query Performance: Knowledge graphs can provide faster query performance than traditional relational databases, especially when dealing with complex queries and large amounts of data. This is because they use advanced indexing and caching techniques that are optimized for graph-based data structures.
Semantic understanding: A knowledge graph can represent data using semantic concepts and relationships, which can be more easily understood by machines and humans. This can help with tasks such as natural language processing and sentiment analysis.
Data Integration: Knowledge graphs can easily integrate data from multiple sources, including structured and unstructured data. This makes it easier to combine data from different sources and create a more complete and accurate picture of the data.
Better Insights: By representing data as a graph, it becomes easier to identify patterns and connections between entities that may not be immediately apparent in a traditional database. This can lead to better insights and more informed decision-making.
Conclusion
Depending on the business requirements and the various technologies in your stack, you may need a combination of both real-time API orchestration and knowledge graphs to effectively orchestrate data and content in a composable tech stack. Overall, the key to success is to have a solid strategy in place that allows you to break down data silos and deliver meaningful, consistent, and connected experiences to your end users.