Data Architecture is the key contract between the business and technology in an enterprise. I experienced the value of Data Architecture during my tenure of the Enterprise Data Hub development initiative.
Data Architecture is designed in such a way that the real business data is handled smoothly across the layers of the enterprise architecture. It plays the key role/artifact to develop and implement governance supporting the enterprise data strategy. It collaborates/connects with the various enterprise objects, such as hardware, applications, networks, tools, technology choices, and data.
To support a variety of the commonly used enterprise applications and business improvement activities, the framework/layers of the Data Architecture is designed as follows:
As depicted in the preceding image, Data Architecture has three layers of components based on its operational strategy, namely Strategic, Tactical, and Operational. As self-described, most of the ground-level operations are executed in the lower components--Enterprise Application Integration (EAI), Business Intelligence (BI), and System Rationalization. Data is tactically architecture at the middle layer using the BPI program. The top layer of Data Architecture is getting involved in the Data Strategy of the underlying enterprise.
Let me illustrate with a real life example to easily understand enterprise data architecture. Our business use case is to build the inventory management system of a production factory. Consider the following image:
As depicted in the preceding image, the inventory management workflow is aimed towards the process of supervising and controlling stock items for the production in an efficient way. Let's get into the details of Data Architecture with this example.
In operation level, raw material information is fed into the inventory core system (Warehouse) in different formats/sources. EAI (tools such as Informatica) is the core component to ingest the incoming source data in a clean/expected layout. Rationalization is the process of extraction of the master data from the various systems of record of both internal and external systems. After processing, to produce the cleansed raw data using EAI and Rationalization in Warehouse, the BI layer takes the execution responsibilities. BI analyzes the enterprise's raw data from the various sources of the system.
Therefore, the lower operational layer of Data Architecture deals with the processing of inventory data from end to end, ranging from raw material to shipping the finished products. Thus, the operational layer cuts across the entire phase of the business.
The next tactical layer BPI is used to improve the existing business operation to accomplish significant improvement in production. In our use case, let's say the raw materials are sourced from various locations around the globe. In doing the various analysis methodologies, the BPI system can come up with an efficient way of sourcing the raw materials for the inventory. Of course, the existing raw data is essential for any prediction/analysis. Effective BPI generates promising results operational efficiency and customer focus, which in turn improves the productivity and profitability of the business.
By definition, enterprise data strategy is the comprehensive vision and actionable foundation for an organization's ability to harness data-related or data-dependent capability. To emphasize the importance of Data Strategy, let me share an interesting answer by Bill Gates of Microsoft. When he was asked a question--"What is the most important asset of your company?" he replied--"Data". In our use case, by doing Data Strategy of the inventory system, it drives the business to be a customer-centric data driven culture. In general, legacy systems produce data silos that will get in the way of understanding customers. This is a big challenge; without a Data Strategy, it is next to impossible for any inventory system. Due to the characteristic of relevancy, which is contextual to the organization, evolutionary, and expected to change on a regular basis, enterprise data strategy is essential to build the comprehensive strategies necessary to make a real difference for the organization.