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Hermann Romanov
Hermann Romanov

Data Governance: How To Design, Deploy And Sust...


The data council, supported by the DMO, should prioritize domains based on transformational efforts, regulatory requirements, and other inputs to create a road map for domain deployment. Then the organization should rapidly roll out priority domains, starting with two to three initially, and aim for each domain to be fully functional in several months.




Data Governance: How to Design, Deploy and Sust...


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The current transition to a post-Moore era is an opportunity to look well beyond power efficiency and make GHG and other sustainability metrics first-order concerns in computing. This requires a paradigm shift towards design for environmental sustainability that treats sustainability impacts as first-order metrics and on equal standing with performance, reliability, usability, and operational energy efficiency. It is critical to consider sustainability across multiple dimensions (emissions, pollution, renewable versus limited resource usage, embodied costs, supply-chain impacts, etc.) in every layer of the computing stack; across the computing and networking spectrum from high-performance computing to smart mobile devices; through decision making about computing from adoption, through use, and ultimately disposal; and in application to various sized communities from rural to urban environments. The DESC program seeks fundamentally new and disruptive research across all aspects of computing including foundations, algorithms, modeling, design, reuse, programming, data management, fault tolerance, operation, data management, graceful degradation and decisions about use cases of digital and computing-based technologies and their associated infrastructure.


This article is a guide to successfully deploying Microsoft Purview (formerly Azure Purview) into production in your data estate. It's intended to help you strategize and phase your deployment from research to hardening your production environment, and is best used in tandem with our deployment checklist.


These best practices cover the deployment for Microsoft Purview unified data governance solutions. For more information about Microsoft Purview risk and compliance solutions, go here. For more information about Microsoft Purview in general, go here.


Regulatory compliance is also a major driver of data governance (e.g., GDPR, CCPA, HIPAA, SOX, PIC DSS). While progress has been made, enterprises are still grappling with the challenges of deploying comprehensive and sustainable data governance, including reliance on mostly manual processes for data mapping, data cataloging and data lineage.


Discover data in unstructured file shares, structured databases, Big Data storage, SaaS applications, and other cloud solutions. The OneTrust unique data discovery architecture allows for discovery and classification across your business systems. Find personal, sensitive, and other data in all file types including text, CSV, PDF, Zip, and images with full OCR capabilities. Flexible deployment options allow for data discovery at scale without the use of agents.


Sustainable software engineering (or sustainable software development) is an approach to software design, implementation, and deployment that emphasizes energy efficiency and environmental sustainability. The goal of sustainable software is to minimize the impact that applications, and the infrastructure that hosts them, have on the planet.


Rapidly build and deploy data pipelines for end-to-end data integration and analytics at enterprise scale. Integrate data lakes, data warehouses, and devices, and orchestrate data integration flow across all environments.


Your architecture affects the sustainability of your workload. Optimize workload placement, and optimize your architecture for user, software, data, hardware, and development and deployment patterns to increase energy efficiency. Each of these areas represents opportunities to employ best practices to reduce the sustainability impact of your cloud workload by maximizing utilization, and minimizing waste and the total resources deployed and powered to support your workload.


  • Consider the following practices: Test and validate potential improvements before deploying them to production. Account for the cost of testing when calculating potential future benefit of an improvement. Develop low-cost testing methods to enable delivery of small improvements.

  • Up-to-date operating systems, libraries, and applications can improve workload efficiency and enable easier adoption of more efficient technologies. Up-to-date software might also include features to measure the sustainability impact of your workload more accurately, as vendors deliver features to meet their own sustainability goals.

  • Use automation and infrastructure as code to bring pre-production environments up when needed and take them down when not used. A common pattern is to schedule periods of availability that coincide with the working hours of your development team members. Hibernation is a useful tool to preserve state and rapidly bring instances online only when needed. Use instance types with burst capacity, Spot Instances, elastic database services, containers, and other technologies to align development and test capacity with use.

  • Managed device farms spread the sustainability impact of hardware manufacturing and resource usage across multiple tenants. Managed device farms offer diverse device types so you can support older, less popular hardware, and avoid customer sustainability impact from unnecessary device upgrades.


A key goal of data governance is to break down data silos in an organization. Such silos commonly build up when individual business units deploy separate transaction processing systems without centralized coordination or an enterprise data architecture. Data governance aims to harmonize the data in those systems through a collaborative process, with stakeholders from the various business units participating.


Governing big data. The deployment of big data systems also adds new governance needs and challenges. Data governance programs traditionally focused on structured data stored in relational databases, but now they must deal with the mix of structured, unstructured and semistructured data that big data environments typically contain, as well as a variety of data platforms, including Hadoop and Spark systems, NoSQL databases and cloud object stores. Also, sets of big data are often stored in raw form in data lakes and then filtered as needed for analytics uses, further complicating data governance.


Despite hosting some of the brightest academics in data science, statistics, user interface design, and organizational innovation, a college or university may be far from innovating on these fronts when it comes to tracking, analyzing, and feeding back information to improve teaching and learning. Although paradoxical to an outsider, this apparent dysfunction is all too familiar to insiders: the incentives are not there for academics to work on their own institution's strategic teaching and learning problems. As a result, research-active analytics groups are generally not responsive to their institution's analytics needs. Academics do not want to be branded with the dreaded badge of service center, which has connotations of not being research-worthy. Various tensions are in play here.


Strimmer: The consumption layer in our Strimmer data pipeline can consist of an analytics service like Databricks that feeds from data in the warehouse to build, train, and deploy ML models using TensorFlow. The algorithm from this service then powers the recommendation engine to improve movie and series recommendations for all users. 041b061a72


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