Modern data strategy: Data Mesh - should you adapt?
The truth is that not every company will be a good fit for the installation of a Data Mesh. The main target market for Data Mesh is larger businesses that deal with unpredictability and change in their environments and operations. This article explains what a data mesh is, what its fundamental components are, how data-driven enterprises benefit from them, and whether a data mesh is a good fit for you. Remember – if your organization's data needs are low and stable over time, a Data Mesh is undoubtedly a waste of money.
Regulation-Compliant Data Processing
Today regulation-compliant data processing is a major challenge, driven by regulatory bodies across the world. In this blog post, we analyzed data transfer regulations from the perspective of GDPR and discussed key research challenges for including compliance aspects in federated data processing. Compliance is at the core of our blossom data platform. In the next blog post, we will discuss how Databloom's blossom data platform addresses some of the aforementioned challenges and ensures regulation-compliant data processing across multiple clouds, Geo-locations, and data platforms.
Why is federated learning so crucial for AI?
Federated learning (FL) is critical in a company's digital transformation since it enables decentralized, privacy-preserving machine learning model training. FL enables businesses to train models on decentralized data from multiple sources, resulting in more robust, scalable, and accurate models. Furthermore, FL assists businesses in overcoming data privacy concerns by allowing models to be trained without sharing raw data, lowering the risk of sensitive information being exposed or misused. FL also reduces the need for centralized data storage, processing, and maintenance, which lowers costs and increases efficiency. Businesses can also use FL to stay ahead in the fast-paced digital world and meet the changing needs of their customers and stakeholders.