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Top 7 Data Governance Blog Posts of 2018

erwin

The driving factors behind data governance adoption vary. Whether implemented as preventative measures (risk management and regulation) or proactive endeavors (value creation and ROI), the benefits of a data governance initiative is becoming more apparent. Defining Data Governance.

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How Data Ethics Supports Governance & Monetisation

Alation

And, while change at large organisations is tough, data leaders would be wise to reframe such transformations as business opportunities rather than burdens. In other words, ethics and governance aren’t just about mitigating risk; with the right approach, they can boost profits, productivity, and ROI. Ethics in Regulation.

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What is the Future of Business Intelligence in the Coming Year?

Smart Data Collective

Business intelligence software will be more geared towards working with Big Data. Data Governance. One issue that many people don’t understand is data governance. It is evident that challenges of data handling will be present in the future too. Increase in ROI. Identify Opportunities.

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Financial Dashboard: Definition, Examples, and How-tos

FineReport

Dupont Analysis Dashboard FineReport simplifies the implementation of the Dupont analysis model, enabling an in-depth examination of an enterprise’s financial condition through Return on Investment (ROI). Data Quality and Consistency : Maintaining data quality and consistency is essential for reliable financial insights.

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Driving Business Value and ROI from a Hybrid Cloud Data Lake

Alation

With an on-premise deployment, enterprises have full control over data security, data access, and data governance. earthquake, flood, or fire), where the data collected does not need to be as tightly controlled. Get the latest data cataloging news and trends in your inbox. Conclusion.

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The path to embedded sustainability

IBM Big Data Hub

A company cannot report on scope 3 category 7 of employee commute without employee data from HR or facilities management data, or without the technology platform and data governance to have an auditable view of that data.

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The 6 Layers of an IoT Solution

CDW Research Hub

To that end, there are eight data planning considerations to keep in mind when designing your IoT data management model: Derived insight response time: How quickly do you need insight from the sensor? Data modeling: Modeling is necessary to normalize this data across all platforms and sensor groups. Layer 6: Applications.

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