Remove Data Governance Remove Data Integration Remove Data Warehouse Remove Definition
article thumbnail

Cloud Data Warehouse Migration 101: Expert Tips

Alation

It’s costly and time-consuming to manage on-premises data warehouses β€” and modern cloud data architectures can deliver business agility and innovation. However, CIOs declare that agility, innovation, security, adopting new capabilities, and time to value β€” never cost β€” are the top drivers for cloud data warehousing.

article thumbnail

Financial Dashboard: Definition, Examples, and How-tos

FineReport

There are also some other key challenges that will often be encountered during the process of creating financial dashboards: Data Integration : One of the primary challenges is integrating data from various sources. Ensuring seamless data integration and accuracy across these sources can be complex and time-consuming.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

SAP Datasphere review: turning data from a technical problem to a business data product.

Jen Stirrup

However, to turn data into a business problem, organizations need support to move away from technical issues to start getting value as quickly as possible. SAP Datasphere simplifies data integration, cataloging, semantic modeling, warehousing, federation, and virtualization through a unified interface. Why is this interesting?

Metadata 121
article thumbnail

The DataOps Vendor Landscape, 2021

DataKitchen

Because it is such a new category, both overly narrow and overly broad definitions of DataOps abound. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. .

Testing 300
article thumbnail

Four Use Cases Proving the Benefits of Metadata-Driven Automation

erwin

Organization’s cannot hope to make the most out of a data-driven strategy, without at least some degree of metadata-driven automation. The volume and variety of data has snowballed, and so has its velocity. As such, traditional – and mostly manual – processes associated with data management and data governance have broken down.

article thumbnail

Data Preparation and Data Mapping: The Glue Between Data Management and Data Governance to Accelerate Insights and Reduce Risks

erwin

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. But the attempts to standardize data across the entire enterprise haven’t produced the desired results.

article thumbnail

Constructing A Digital Transformation Strategy: Putting the Data in Digital Transformation

erwin

The solution is data intelligence. It improves IT and business data literacy and knowledge, supporting enterprise data governance and business enablement. Organizations need a real-time, accurate picture of the metadata landscape to: Discover data – Identify and interrogate metadata from various data management silos.