Bad Data & ML, Open Data, Dark Data Concern, GIS & ML, Data Quality, Database Trade-offs

This week, we begin with an article that talks about the high costs associated with bad data & the steps that can be taken to utilize the power of machine learning to solve the issues. Next is an analysis that discusses the capabilities of Open Data for Social Impact Framework, developed by Microsoft. Then, we have a piece on the concerns regarding the issue of dark data that has become more prevalent due to the work-from-everywhere model. Following that, we have an essay about the applications of geospatial data in numerous industries & how the use of ML may help give simple, high-quality data for businesses and society to make informed decisions. Next, is an article describing the crucial role played by data quality, data trust, and bias detection for organizations to securely make decisions based on the data they gather. Lastly, we have a story on issues of handling data with challenges like data portability and how the use of cloud databases helps to overcome them.

A $3T-Per-Year Problem With A Solution

A few years ago, IBM reported that businesses lost $3 trillion dollars per year due to bad data. Today, Gartner estimates $12.9 million to be the yearly cost of poor-quality data. Funds get wasted in digitizing sources as well as organizing and hunting for information — an issue that, if anything, has increased now that the world has shifted to more digitized and remote environments. 

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Microsoft Research Introduces Open Data for Social Impact Framework

The Open Data for Social Impact Framework, developed by Microsoft, is a roadmap to assist companies in using data to get new insights, make better decisions, and enhance efficiency while addressing urgent social concerns. “The ability to access data to enhance outcomes entails much more than technological tools and the data itself,” the framework relies on the fundamental learning from the Open Data Campaign. 

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Security Leaders Voice Concerns Over Dark Data

Dark data, the data that organizations are unaware of but which can still be highly sensitive or critical, is a major worry, with 84% of organizations “extremely concerned” about it, according to a BigID survey of 400 enterprise technology leaders. The survey also revealed that eight out of 10 organizations consider unstructured data the hardest to manage and secure and found more than 90% of organizations struggled with enforcing security policies around sensitive or critical data. 

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Training Data For GIS Applications Of Machine Learning

The intersection of GIS and Machine Learning is evolving and bringing new use cases and applications of ML to the fore. These applications, spanning both the private and public sectors, are powered by large volumes of data captured by satellites, drones, cameras, LIDAR sensors, and more, all of which come together to provide a comprehensive view of the world. The sheer volume and variety of data create complexity in its management and usage.

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Why Businesses Should Know The Importance Of Data Quality

Organizations can harness great benefits from data, but understanding the importance of data quality, trust and avoiding bias allows them to make decisions and create profit. At a fundamental level, data trust is when an enterprise has confidence that the data it is using is accurate, usable, comprehensive and relevant to its intended purposes. On a bigger-picture level, data trust has to do with context, ethics and biases.

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Netflix’s Problem Is Everyone’s Problem Now: How To Eliminate Database Trade-offs For Scale

For growing numbers of enterprises data is at the heart of what they do: simply, it’s where the value is. So thinking about how to handle that data is one of the most consequential decisions any organisation can make: “The problems that were Facebook’s problem and Netflix’s problem, and Apple’s problem in 2012 are now turning into everyone’s problem today,” Patrick McFadin, VP for Developer Relations at Datastax, tells The Stack.

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