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To understand the risks posed by AI, follow the money

O'Reilly on Data

It’s difficult to argue with David Collingridge’s influential thesis that attempting to predict the risks posed by new technologies is a fool’s errand. However, there is one class of AI risk that is generally knowable in advance. We ought to heed Collingridge’s warning that technology evolves in uncertain ways.

Risk 214
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Product lifecycle management for data-driven organizations 

IBM Big Data Hub

The key foundation of a strong PLM strategy is healthy and orderly product data, but data management is where enterprises struggle the most. To take advantage of new technologies such as AI for product innovation, it is crucial that enterprises have well-organized and managed data assets.

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Enabling a data-driven IT modernization strategy

CIO Business Intelligence

The big picture : In the midst of a rush to technology modernization, it’s critical to ensure the organization’s data assets are not overlooked. Why it matters:  Data-driven business decisions must factor prominently in modernization efforts. The bottom line:  Don’t leave data behind.

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Aiding Architecture & Engineering Firms with Data-Driven Learning

Smart Data Collective

Data analytics is incredibly valuable for helping people. More institutions are recognizing this, so the market for data analytics in education is projected to be worth over $57 billion by 2030. We have previously talked about the many ways that big data is disrupting education.

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Data-Driven Marketing: 4 Key Advantages

Smart Data Collective

We have talked about the many different sectors that have been shaped by big data in recent years. The bedrock of today’s advertising methods and a requirement for delivering personalized experiences is data-driven marketing. Using data-driven marketing can connect you to customers. It is marketing’s future.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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

Rocket-Powered Data Science

I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data.