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For many organizations, this centralized data store follows a datalake architecture. Although datalakes provide a centralized repository, making sense of this data and extracting valuable insights can be challenging.
While sometimes at rest in databases, datalakes and data warehouses; a large percentage is federated and integrated across the enterprise, introducing governance, manageability and risk issues that must be managed.
To better understand and align data governance and enterprise architecture, let’s look at data at rest and data in motion and why they both have to be documented. Documenting data at rest involves looking at where data is stored, such as in databases, datalakes , data warehouses and flat files.
While sometimes at rest in databases, datalakes and data warehouses; a large percentage is federated and integrated across the enterprise, management and governance issues that must be addressed. When an organization knows what data it has, it can define that data’s business purpose.
Developers, data scientists, and analysts can work across databases, data warehouses, and datalakes to build reporting and dashboarding applications, perform real-time analytics, share and collaborate on data, and even build and train machine learning (ML) models with Redshift Serverless.
It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It encompasses riskmanagement and regulatory compliance and guides how AI is managed within an organization. Capture and document model metadata for report generation.
Do we know the business outcomes tied to datariskmanagement? Once you have data classification then you can talk about whether you need to tokenize and why, or anonymize and why, or encrypt and why, etc.” Indeed, automation is a key element to data catalog features, which enhance data security.
Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadatamanagement which rank nearby. in lieu of simply landing in a datalake.
Forrester describes Big Data Fabric as, “A unified, trusted, and comprehensive view of business data produced by orchestrating data sources automatically, intelligently, and securely, then preparing and processing them in big data platforms such as Hadoop and Apache Spark, datalakes, in-memory, and NoSQL.”.
What are you seeing as the differences between a Chief Analytics Officer and the Chief Data Officer? Value Management or monetization. RiskManagement (most likely within context of governance). Product Management. Does Data warehouse as a software tool will play role in future of Data & Analytics strategy?
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