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Leveraging Data, Blockchain and AI to Help Agriculture Meet Growing Global Demand

Given the scale of the world’s food supply, there aren’t many industries that lend themselves to the power of data science and analytics than agriculture. This is the thinking behind a new research paper from a group of data scientists who make a case for finding new ways to use blockchain, AI and API management to enable “smart agriculture.” The paper, “Agricultural Digital Transformation,” has been published in the OR/MS Today journal from the Institute for Operations Research and the Management Sciences (INFORMS).

“Above the Trend Line” – Your Industry Rumor Central for 7/1/2019

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

New Syncsort Trillium Software Delivers Data Quality at Scale

Syncsort, a leader in Big Iron to Big Data software, unveiled Trillium DQ for Big Data, providing best-in-class data profiling and data quality capabilities in a single solution, designed to work natively with distributed architectures. Using Trillium DQ for Big Data, organizations can apply data quality to large volumes of enterprise data on-premises or in the cloud, delivering trusted data for business insights and realizing the full potential of emerging technologies to meet their data governance and compliance requirements.

Human Guided Machine Learning is the Golden Path to Golden Records

In this special guest feature, Matt Holzapfel, Solutions Lead at Tamr, addresses how a bottom-up approach recognizes connections in the vast pools of data to identify not only the most reliable data for each entity in the ecosystem, but the means by which that data can reliably be updated — a far faster path to data mastering, and a better way to make sure that data stays golden.

GDPR and the Transparency Revolution

In this contributed article, David Thomas, CEO of Atlanta-based Evident ID, discusses the one year anniversary of GDPR and how the regulation has driven a great deal of meaningful conversations around consumer privacy and enterprise data management policies.

How to Stop BI Team Members from Dropping Like Flies

Data is the backbone of every company. So keeping BI teams happy is a top priority… Right? In reality, BI teams’ needs are largely ignored by companies, with a 21% turnover rate and 36% of employees reporting dissatisfaction with their work environment. What’s the next step – what can be done to address the dire straits currently facing BI teams and what companies must do to staunch the analyst exodus and keep their key teams thriving?

Addressing Demographic Pay Gaps with Data-driven Solutions

In this special guest feature, Dr. Margrét Vilborg Bjarnadóttir, Assistant Professor of Management Science and Statistics at the University of Maryland Robert H. Smith School of Business, suggests that despite massive cultural and societal pushes for gender equality, the gender pay gap in the workplace remains as strong as ever. Data analysis and visualization could play a vital role in helping to resolve this important issue.

Data Privacy and Blockchain in the Age of IoT

In this contributed article, freelance human Avery Phillips discusses how transparency is a foundation of blockchain technology, and one of its greatest assets when it comes to the healthcare industry and HIPAA compliance. This is where blockchain, big data, and IoT collide — big data analytics requires quality, accurate input.

Acquisitions for Big Data Startups are Hot. What’s Driving the Money?

In this contributed article, Ben Bloch, CEO of Bloch Strategy, discusses some of the reasons for the recent M&A activity we’re seeing in the big data space, and what we might see in the future.

Addressing Governmental Challenges when Engaging AI, ML and Data Analytics

Gartner recently stated that all industries and levels of government agree the top three game-changing technologies today are AI/machine learning, data analytics/predictive analytics and cloud technologies. However, there are some primary sticking points when it comes to innovation in these areas. Government organizations continue to encounter challenges when trying to pursue these initiatives due to complex security and compliance requirements, poor scalability of legacy IT infrastructure, and perceived risks associated with cloud and IT modernization efforts. How can these challenges be addressed?