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Nearly every data leader I talk to is in the midst of a datatransformation. As businesses look for ways to increase sales, improve customer experience, and stay ahead of the competition, they are realizing that data is their competitive advantage and the key to achieving their goals. And it’s no surprise, really.
We took this a step further by creating a blueprint to create smart recommendations by linking similar data products using graph technology and ML. In this post, we showed how an organization can augment a data catalog with additional metadata by using ML and Neptune with an automated process.
This challenge is especially critical for executives responsible for datastrategy and operations. Here’s how automated data lineage can transform these challenges into opportunities, as illustrated by the journey of a health services company we’ll call “HealthCo.” This is where Octopai excels.
We chatted about industry trends, why decentralization has become a hot topic in the data world, and how metadata drives many data-centric use cases. But, through it all, Mohan says it’s critical to view everything through the same lens: gaining business value from data. Data fabric is a technology architecture.
This involves unifying and sharing a single copy of data and metadata across IBM® watsonx.data ™, IBM® Db2 ®, IBM® Db2® Warehouse and IBM® Netezza ®, using native integrations and supporting open formats, all without the need for migration or recataloging.
This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division. The following diagram illustrates the building blocks of the Institutional Data & AI Platform.
Publish data assets – As the data producer from the retail team, you must ingest individual data assets into Amazon DataZone. For this use case, create a data source and import the technical metadata of four data assets— customers , order_items , orders , products , reviews , and shipments —from AWS Glue Data Catalog.
This means you need to: Inventory data: Know all information resources and relevant metadata. Classify data: Organize structured and unstructured data into relevant categories. Curate data and knowledge: Use active metadata management and data catalogs to organize and manage registers.
Your AI strategy is only as good as your datastrategy,” Tableau CMO Elizabeth Maxon said in a press conference Monday. But to us, it’s more than just having a datastrategy; it’s also about building a great foundation of a data culture.”
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