Remove Dashboards Remove Document Remove Metadata Remove Optimization
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AI Governance: Break open the black box

IBM Big Data Hub

Platforms and practices not optimized for AI. This includes capturing of the metadata, tracking provenance and documenting the model lifecycle. The ability to track and share model facts and documentation across the organization provides backup for analytic decisions. This is due to: An inability to access the right data.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It also lets you choose the right engine for the right workload at the right cost, potentially reducing your data warehouse costs by optimizing workloads. Increase trust in AI outcomes.

Risk 73
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Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. Cold storage is optimized to store infrequently accessed or historical data. Organizations often need to manage a high volume of data that is growing at an extraordinary rate.

Data Lake 116
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CRM’s Have a Big Data Technical Debt Problem: Here’s How to Fix It

Smart Data Collective

Over the years, Snapshot has grown into the industry’s most comprehensive toolset for org management, relational data migration, documentation, technical debt removal, and more. Once you have accounted for all the CRM’s data, and metadata type, administrators will then need to take a comprehensive snapshot of the account,” added Mercer.

Big Data 131
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Enhance your analytics embedding experience with the new Amazon QuickSight JavaScript SDK

AWS Big Data

Amazon QuickSight is a fully managed, cloud-native business intelligence (BI) service that makes it easy to connect to your data, create interactive dashboards and reports, and share these with tens of thousands of users, either within QuickSight or embedded in your application or website. SDK Feature overview The QuickSight SDK v2.0

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

As quality issues are often highlighted with the use of dashboard software , the change manager plays an important role in the visualization of data quality. It involves: Reviewing data in detail Comparing and contrasting the data to its own metadata Running statistical models Data quality reports. 2 – Data profiling.

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Case study: Policy Enforcement Automation With Semantics

Ontotext

Data-centric approach In the data-centric approach, metadata serves as a layer of interoperability between the data sources. This powers numerous applications, insight generations, dashboards, and tools. Data products – optimization of data assets that drive business, improving data quality and interoperability.