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iostudio delivers key metrics to public sector recruiters with Amazon QuickSight

AWS Big Data

Our previous solution offered visualization of key metrics, but point-in-time snapshots produced only in PDF format. In this post, we discuss how we built a solution using QuickSight that delivers real-time visibility of key metrics to public sector recruiters.

Metrics 97
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You Can’t Regulate What You Don’t Understand

O'Reilly on Data

Should we risk loss of control of our civilization?” If we want prosocial outcomes, we need to design and report on the metrics that explicitly aim for those outcomes and measure the extent to which they have been achieved. Should we automate away all the jobs, including the fulfilling ones?

Metrics 284
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5 signs your agile practices will lead to digital disaster

CIO Business Intelligence

They are afraid of failure and the uncertainty of knowledge work, and so that’s stressful. Agile is an amazing risk management tool for managing uncertainty, but that’s not always obvious.” The key is recognizing that planning must be an agile discipline, not a standalone activity performed independently of agile teams.

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Why CIOs should invest in digital through economic headwinds

CIO Business Intelligence

Resilient cybersecurity Despite the clamour for new digital investments, Gartner’s analysts did recognise that this would represent a new cybersecurity risk, with some attributing the increased spending in security over the next year down to ongoing uncertainty regarding Russia’s invasion of Ukraine.

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Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise

Rocket-Powered Data Science

The latter is associated primarily with “watching” the data for interesting patterns, while precursor analytics is associated primarily with training the business systems to quickly identify those specific patterns and events that could be associated with high-risk events, thus requiring timely attention, intervention, and remediation.

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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.”

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In AI we trust? Why we Need to Talk About Ethics and Governance (part 2 of 2)

Cloudera

Surely there are ways to comb through the data to minimise the risks from spiralling out of control. Systems should be designed with bias, causality and uncertainty in mind. Uncertainty is a measure of our confidence in the predictions made by a system. We need to get to the root of the problem. System Design. Model Drift.