The Role of Data Centricity in Smart , Connected Systems
communication breakdowns , especially when legacy systems are not designed to handle dynamic growth effectively .
System interoperability is also a concern . Smart applications often involve integrating hardware and software from different vendors , each using proprietary protocols or standards . It ’ s an ongoing challenge to ensure that all components communicate efficiently without extensive custom coding , which can slow down progress and future expansion . Additionally , developers must address security risks associated with data transmission , such as unauthorized access , spoofing , or breaches . Finally , developers must contend with CPU , memory , and bandwidth limitations , especially in remote or resource-constrained environments , which can impede the timely flow of critical data that is required for generating and leveraging AI-driven insights .
Together , these challenges demand an architecture that is tailored to the demands of smart , interconnected applications . Here ’ s one approach that is gaining traction : Rather than designing the system around the process , instead design the system around its data flow . This approach to putting data at the core of the architecture is called data centricity . Data-centric architectures are now enabling a new generation of AI systems , fueled by core connectivity standards .
2 DATA CENTRICITY : ENABLING SCALABILITY , DESIGN FLEXIBILITY , AND REAL-TIME DATA DELIVERY
Data-centric architectures are system designs that prioritize data as the central element , around which all components and processes are organized . Unlike traditional architectures that focus on applications or infrastructure , a data-centric architecture designs the system around the data to emphasize data creation , storage , accessibility , and governance . It leverages scalable data sources by unifying structured and unstructured data and employing advanced pipelines for seamless data integration and transformation . Interoperability is a key feature of any data-centric architecture , enabling systems to share and utilize data effectively across domains through APIs , standard protocols , and semantic layers . Governance frameworks ensure data quality , security , and compliance , while feedback loops facilitate the continuous improvement of processes and analytics . Crucially , this approach enables real-time insights , AI and ML workflows , and crosssystem collaboration .
The basic concept of data centricity in connected applications is not new . In the 1980s for example , customer relationship management ( CRM ) software became popular , due to its ability to manage a “ single source of truth ” for each customer or prospect ’ s data . Each transaction was captured and made accessible , and the storage / retrieval of such data was used for future workflows , such as reports , campaigns or even AI-assisted analysis . In other words , data was at the heart of the system ; so technically , they were early data-centric systems . Today , we call this architecture client-server or message centric . The message-centric architecture is still popular today for predictable , stable and non-critical environments .
4 February 2025