Cloud Data structuring Government Risk 
erwin
OCTOBER 8, 2020
More and more companies are looking at cloud migration. So if you’re going to move from your data from on-premise legacy data stores and warehouse systems to the cloud, you should do it right the first time. Automated Cloud Migration. Historically, moving legacy data to the cloud hasn’t been easy or fast.
Gimmal
JUNE 10, 2022
Craig Carpenter and Dean Gonsowski , Gimmal’s CEO and CRO respectively, spoke with Ari about information discovery, migration, governance, and compliance, and how Gimmal helps organization address their biggest information governance challenges. It started as a GRC — governance risk and compliance — consulting firm.
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erwin
SEPTEMBER 10, 2020
Not Documenting End-to-End Data Lineage Is Risky Busines – Understanding your data’s origins is key to successful data governance. Not everyone understands what end-to-end data lineage is or why it is important. Data Lineage Tells an Important Origin Story.
IBM Big Data Hub
DECEMBER 16, 2022
An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.
erwin
MAY 14, 2021
Due to the prevalence of internal and external market disruptors, many organizations are aligning their digital transformation and cloud migration efforts with other strategic requirements (e.g., compliance with the General Data Protection Regulation). Comparing SQL and NoSQL.
Security Affairs
JULY 3, 2023
Understanding the different types of data is crucial for organizations as it helps them devise appropriate data protection and management strategies. Data can be classified into; Structured Data: Structured data refers to information that is organized in a predefined format.
Collibra
FEBRUARY 3, 2022
As cloud data storage and advanced analytics become the norm, the quality of data gets critical. Especially when you want your data lake to power trusted analytical results. How poor data quality can lead to bad decisions. The cost of poor data quality is not always measurable.
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