Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method's effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning.

My first task as a Chief Data Officer (CDO) is to implement a data strategy. Over the past 15 years, I've learned that an effective data strategy enables the enterprise's business strategy and is critical to elevate the role of a CDO from the backroom to the boardroom. A company's business strategy is its strategic vision to achieve its business goals. Data that can be managed, protected, and monetized effectively will provide insights into how to achieve those goals. A CDO works in collaboration with senior executives to steer a business to its strategic vision through a data strategy.

For decades, managing data essentially meant collecting, storing, and occasionally accessing it. That has all changed in recent years, as businesses look for the critical information they can pull from the massive amounts of data generated, accessed, and stored in myriad locations, from corporate data centers to the cloud and the edge. Given that, data analytics – helped by such modern technologies as artificial intelligence (AI) and machine learning – has become a must-have capability and in 2022, the importance will be amplified. Enterprises need to rapidly parse through data – much of it unstructured – to find the information that will drive business decisions. They also need to create a modern data environment in which to make that happen.

For decades, managing data essentially meant collecting, storing and occasionally accessing it. That has all changed in recent years, as businesses look for the critical information that can be pulled from the massive amounts of data being generated, accessed and stored in myriad locations, from corporate data centres to the cloud and the edge. Given that, data analytics – helped by such modern technologies as artificial intelligence (AI) and machine learning – has become a must-have capability and in 2022, the importance will be amplified. Enterprises need to rapidly parse through data – much of it unstructured – to find the information that will drive business decisions. They also need to create a modern data environment in which to make that happen.

Data management agility has become a mission-critical priority for organizations in an increasingly diverse, distributed, and complex environment. "The emerging design concept called "data fabric" can be a robust solution to ever-present data management challenges, such as the high-cost and low-value data integration cycles, frequent maintenance of earlier integrations, the rising demand for real-time and event-driven data sharing and more," says Mark Beyer, Distinguished VP Analyst at Gartner. Gartner defines data fabric as a design concept that serves as an integrated layer (fabric) of data and connecting processes. A data fabric utilizes continuous analytics over existing, discoverable and inferenced metadata assets to support the design, deployment and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms. Data fabric leverages both human and machine capabilities to access data in place or support its consolidation where appropriate. It continuously identifies and connects data from disparate applications to discover unique, business-relevant relationships between the available data points.

The post-big data landscape has been shaped by two emergent, intrinsically related forces: the predominance of cognitive computing and the unveiling of the data fabric architecture. The latter is an overlay atop the assortment of existing distributed computing technologies, tools and approaches that enable them to interact for singular use cases across the enterprise. Gartner describes the data fabric architecture as the means of supporting "frictionless access and sharing of data in a distributed network environment." These decentralized data assets (and respective management systems) are joined by the data fabric architecture. Although this architecture involves any number of competing vendors, graph technology and semantic standards play a pivotal role in its implementation.