Build an Effective Data Governance Framework

DATAVERSITY

Data Governance frameworks are used to structure the core components of a Data Governance program. Frameworks add significant value for those organizations getting started and improve or address missing components for programs already in place.This month’s RWDG webinar with Bob Seiner will focus on dissecting a common Data Governance framework and customizing the framework to match the needs of your organization. Frameworks can be complex to describe but, in this case, the framework will become the self-describing face of your program.
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Spotlight

Data science is about using data to provide insight and evidence that can lead business, government and academic leaders to make better decisions. However, making sense of the large data sets now becoming ubiquitous is difficult, and it is crucial to use appropriate tools that will drive smart decisions.The beginning and end of nearly any problem in data science is a visualization—first, for understanding the shape and structure of the raw data and, second, for communicating the final results to drive decision making. In either case, the goal is to expose the essential properties of the data in a way that can be perceived and understood by the human visual system.

OTHER ON-DEMAND WEBINARS

Top 10 Data and Analytics Trends That Will Change Your Business

Gartner

• The top ten strategic data and analytics technology trends and what they enable. How these trends enable you to build an intelligent and emergent data and analytics portfolio of capabilities that scale to the needs of digital business. Why these trends are growing and having an impact now. How these trends will change your organization, data and analytics program and skills needed. Strategic technology trends have significant disruptive potential over the next 5 years. You must examine your business impacts of these trends and appropriately adjust investments, business models and operations or else your company is at risk of losing competitive advantage to those who do. Data and analytics leaders cannot afford to ignore these 2019 top data and analytics trends.
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Data Modeling at the Environment Agency of England Case Study

Global Data Strategy Ltd

The Environment Agency uses data models as a key part of their digital journey in reporting scientific results for water quality, fisheries, conservation and ecology, flood management, and more. Join special guest Becky Russell from the Environment Agency along with host Donna Burbank as they discuss how they were able to gain buy-in from various departments across the organization using data models and data standards
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Sharing and Deploying Data Science with KNIME Server - February 2019

KNIME

You’re currently using the open source KNIME Analytics Platform, but looking for more functionality - especially for working across teams and business units? KNIME Server is the enterprise software for team based collaboration, automation, management, and deployment of data science workflows, data, and guided analytics. Non experts are given access to data science via KNIME Server WebPortal or can use REST APIs to integrate workflows as analytic services to applications, IoT, and systems.
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Building a Modern Operational Data Warehouse

tdwi.org

With data coming from so many different sources nowadays (both old and new, both internal and external), it is inevitable that data will arrive in many different structures, schema, and formats, with other variables for latency, concurrency, and requirements for storage and processing. When data types are extremely diverse and combined, we now call it “hybrid data.” This usually drives users to deploy many types of databases and different platforms to capture, store, process, and analyze the data, which in turn results in hybrid data management architectures.
Watch Now

Spotlight

Data science is about using data to provide insight and evidence that can lead business, government and academic leaders to make better decisions. However, making sense of the large data sets now becoming ubiquitous is difficult, and it is crucial to use appropriate tools that will drive smart decisions.The beginning and end of nearly any problem in data science is a visualization—first, for understanding the shape and structure of the raw data and, second, for communicating the final results to drive decision making. In either case, the goal is to expose the essential properties of the data in a way that can be perceived and understood by the human visual system.

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