1/31/2024 0 Comments Trust issues![]() Accessing data faster, cleaner and with more confidence This layer enables the consumer to view raw data from potentially hundreds of linked databases quickly and easily via a single pane of glass. Instead of laboriously moving data to a warehouse or lake, these repositories are linked using modern lightweight cloud-native federated query or data-visualization tools to something called the semantic layer. This is the first viable paradigm that unifies data at the compute level instead of the storage level. There is no replication of data and no data entropy as a direct result. The secret of data fabric’s success is that each data type or data domain is left to reside in its original data storage mechanism – as long as that repository is fit for purpose. Because EY reduced replication, throughout these downstream processes, where the data has come from, how it’s being used, and where it’s going is never in doubt. Much of that telemetry data from the edge is referenced in place to provide insights on everything from product improvement opportunities to enhanced and predictive diagnostics on equipment failures. Each solution was an improvement on its predecessor, but they were both fundamentally flawed because they failed to solve any of the issues around data replication, lineage, entropy and reconciliation.ĭata fabric, however, promises to solve all these issues of data trust, unlocking digital transformation and placing solutions such as enterprise-wide analytics and industrial-scale artificial intelligence (AI) within reach. First came the data warehouse in the 1980s and then the data lake around 2010. From warehouses to lakes – data-trust issues aboundĭata scientists have been trying to solve this challenge of data trust for decades. It’s clearly impractical to attempt to clean up data in this way every time you want to make a business decision. It also introduces a temporal difference in the data while it’s being checked. The problem is, with every replication you need to reconcile the data and that can be an incredibly time-consuming and complex process and is achieved with various levels of success. To clean up data, it’s necessary to track it back to the raw source. The same dynamic is in play when data is replicated across multiple systems – it is transformed in various ways and a kind of entropy takes place, which erodes trust. Trying to unravel data lineage is a bit like the old game of “Telephone” we used to play at school, where a message is whispered between children and over time becomes increasingly garbled. ![]() Do you know where your data comes from, are you dealing with the most reliable version of that data, and if it’s not in its raw state, how has it been transformed? One of the greatest fundamentals of data trust is lineage. It stitches together disparate databases, making a single view of enterprise-wide data accessible via just about any tool of choice.ĭata fabrics minimize data replication through virtualization, giving consumers speedy access to high-trust raw data that resides in its native state. ![]() Now, however, a new architectural pattern, called data fabric promises to deliver the high-confidence data that companies need.
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