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The benefits of leveraging information-centric enterprise architecture - Part 3

Dr. Steven Schilders
AVP and Senior Principal Consultant, Enterprise Architecture
Marie-Michelle Strah, Ph.D.
Senior Principal Consultant, Enterprise Architecture

Continuing our three-blog series on information-centric architecture, this blog highlights the benefits of the data-first approach. While explaining how this approach drives agility, we want to emphasize that these blogs do not advocate a complete implementation of information-centric architecture. Rather, we are presenting an alternate view on the two most prevalent architecture paradigms.

In Part 1 and Part 2 of this series, we explored how organizations typically implement systems based on business capabilities rather than data. Such an approach invariably creates extreme data segmentation because system capabilities dictate what data is stored, how many copies are stored and how it is accessed. In today's age, no organization can succeed with fragmented data as data and its relationships - both direct and indirect - are the lifeblood of an organization.

Integration challenges in data warehousing solutions

Data warehousing solutions are quite popular for data integration. However, these solutions involve lengthy processing making it difficult to forge business-critical data connections, thereby diminishing the value of data. Further, data warehousing approaches - and the assigned 'data architects' - become tied to vendor data models. We use the term data architect loosely here. Invariably, these architects behave as vendor-specific master data management (MDM) or enterprise data warehouse (EDW) specialists rather than actual 'enterprise information architects'. Needless to say, this type of centralized and hierarchical approach nullifies any benefit that can be achieved through indirect data relationships such as artificial intelligence (AI) and machine learning.

To be able to make real-time decisions and scale quickly in highly competitive markets, you need to transform your enterprise into a hyper-connected and composable  organization. The danger from delayed decisions cannot be overstated in such an environment. To give you an idea of how important this is, we have put together a graph that illustrates the extent of value lost when there is a delay between a business event and action taken.


Despite these acute disadvantages, application data architecture is often prioritized over enterprise information architecture. In some cases, this is because vendor-provided platforms and COTS products pre-determine data models and data access. In other cases, capability-based architectures that claim to represent business capabilities are actually application or technical architectures that collapse business capabilities. For example, consider how ERP systems tend to represent either finance, accounts payable (AP) or human capital capabilities.

This traditional approach exponentially delays the delivery of business insights and decision-making because data must be collected and copied across silos to get actionable information. Further, point-to-point integrations across multiple applications with disparate data architecture becomes an effort-intensive process for enterprise architecture as well as data teams. Finally, developing and maintaining these brittle and tightly-coupled architectures exacerbates the delay in the decision-to-value cycle.

Now, let us see how information-centric architecture unlocks value from hidden data to enable business-as-a-service capabilities in digital ecosystems.

Step 1: Integrate data across the organization

First, organizations must integrate data whether it resides in commercial-off-the-shelf (COTS) products, custom applications or microservices. In our earlier blog, we had proposed a layered information architecture approach (see figure below). Here, information architecture is not tied to either application or platform architectures that prioritize technical architecture. Instead, it lays the foundation for composable architecture by leveraging a hub model.



Information Centric EA: Layered Information Architecture


Step 3: Use fit-for-purpose data hub models to gain business-specific insights


Our previous blog also illustrated how information-centric architecture can be used in COTS as well as custom-built applications. Here is how the data integration hub architecture works in both cases (see figure below). The data hubs provide representations of data that are optimized for the specific needs of the business. For example, key-based data is leveraged for key-based entity relationships, graph-based data is used to analyze complex interdependencies, time-series-based data is used for sequential analysis, search-based data can be used for complex queries, and so on. Thus, information-centric enterprise architecture reduces the decision-to-value curve because data is grouped contextually and data hubs provide the relevant data attributes in a form that optimizes value creation.



Step 4: Apply AI and BI on insights to achieve decision-as-a-service


Data integration hubs and contextual data grouping allow enterprises to design business intelligence (BI) capabilities and machine learning systems that merge programmed intelligence and AI. Further, BI capabilities can extend the base data with specific data requirements needed for analytics. They are exposed through BI services or decision-as-a-service executed for a consumer-specific data context. The key aspect of this design is that business-intelligent capabilities and services can be created, modified or removed without impacting the core and contextual data assets.


The end result?

  • Transitioning from traditional data warehouses to a fit-for-purpose model of multiple data hubs helps organizations leverage traditional BI capabilities and next-generation AI and machine learning
  • Prioritizing layered information-centric enterprise architecture makes data and decision-making organizational and architectural priorities


Simply put, adopting a strategic model instead of a retrofit model enables AI, faster access to enterprise insights and real-time decision-making. In an era where data is king, these are the key capabilities that enterprises need to become service-enabled.


Keep watching this space for enterprise-level case studies and best-practices of information-centric design in microservices, AI and data science.


In case you missed the previous blogs in this series, here are the links:

Part 1

Part 2

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