Episode 752 - August 14 - 2023 - Data and Analytics - Part 2 of 2 - https://dulieuphiendich.com/ và Vina Blockchain

What Is Data and Analytics? Part 2 of 2 From: Gartner.com What is the future of data and analytics technologies? In the past, the group responsible for data was managed independently from the analytics and insights team. Data technologies were likewise distinct from analytics technologies. That is changing in many ways. For example, data management platforms increasingly incorporate analytics, especially machine learning. Analytics and BI platforms are developing data science capabilities and new platforms are emerging to deliver specific functionality for key activities, such as data visualization or Data and analytics governance. Cloud service providers are creating yet another form of complexity as they increasingly dominate the infrastructure platforms on which these services are used. Traditional platforms across the data, analytics and AI markets struggle to accommodate the growing number of data and analytics use cases. As a result, organizations must balance the high total cost of ownership of existing, on-premises solutions against the need for increased resources and emerging capabilities. Examples include natural language query, text mining, and analysis of semistructured and unstructured data. The future of data and analytics therefore requires organizations to invest in composable, augmented data management and analytics architectures to support advanced analytics. Modern Data and analytics systems and technologies are likely to include the following. Data management solutions Master data management is a technology-enabled business discipline in which business functions and IT work together to ensure the uniformity, accuracy, stewardship, governance, semantic consistency and accountability of the enterprise’s official shared master data assets. Data hubs are focused on enabling data sharing and governance. Producers and consumers of data connect with one another through the data hub, enabled by governance controls and common models that define effective data sharing. Master data management is an example of a data hub that is focused only on master data. Data catalogs are increasingly moving into the governance space. They too are starting to become data (and analytics) hubs. Data centers physically house servers (as opposed to warehouses, which are data structures housed on servers or in the cloud). Their future depends on the degree to which workloads can be moved to the cloud. Those migration decisions must be based on the business benefits of doing so. Data warehouses provide an endpoint for collecting transactional, detailed (and sometimes other types of) data. They support predictable analyses for data whose value is well established — that is, well-known, predefined and repeatable analytics that are scalable across many users in the enterprise. Data lakes collect unrefined data (in its native form, with limited transformation and quality assurance and intrinsic governance) and allow users to explore and analyze it in a highly interactive way. Data lakes don’t replace data warehouses or other systems of record; rather, they complement them by storing unrefined data that may hold value. The sweet spot for data lakes is the world of pure discovery, data science and iterative innovation. Data fabric Data fabric is an emerging data management design that enables augmented data integration and sharing across heterogeneous data sources. Data fabrics have emerged as an increasingly popular design choice to simplify an organization’s data integration infrastructure and create a scalable data architecture. If widely implemented, data fabrics could significantly eliminate manual data integration tasks and augment (and, in some cases, completely automate) data integration design and delivery. However, data fabrics are still an emergent design concept. No single vendor currently delivers, in an integrated manner, all the mature components needed to stitch together the data fabric.