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A step-by-step guide to setting up a data governance program

IBM Big Data Hub

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture.

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How Your Finance Team Can Lead Your Enterprise Data Transformation

Alation

Building a Data Culture Within a Finance Department. Our finance users tell us that their first exposure to the Alation Data Catalog often comes soon after the launch of organization-wide data transformation efforts. After all, finance is one of the greatest consumers of data within a business.

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Top 6 Benefits of Automating End-to-End Data Lineage

erwin

Replace manual and recurring tasks for fast, reliable data lineage and overall data governance. It’s paramount that organizations understand the benefits of automating end-to-end data lineage. The importance of end-to-end data lineage is widely understood and ignoring it is risky business.

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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. Creating a High-Quality Data Pipeline.

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What is Data Lineage? Top 5 Benefits of Data Lineage

erwin

Business terms and data policies should be implemented through standardized and documented business rules. Compliance with these business rules can be tracked through data lineage, incorporating auditability and validation controls across data transformations and pipelines to generate alerts when there are non-compliant data instances.

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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

Data processes that depended upon the previously defective data will likely need to be re-initiated, especially if their functioning was at risk or compromised by the defected data. These processes could include reports, campaigns, or financial documentation. Accuracy should be measured through source documentation (i.e.,

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Automate discovery of data relationships using ML and Amazon Neptune graph technology

AWS Big Data

This allows for a new way of thinking and new organizational elements—namely, a modern data community. However, today’s data mesh platform contains largely independent data products. Even with well-documented data products, knowing how to connect or join data products is a time-consuming job.