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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.

GDPR is in place; CCPA is coming. Are you ready?

In this contributed article, Rohit Mahajan, CTO/CPO at Io-Tahoe, suggests that firms should view GDPR and CCPA as an opportunity that can pay dividends by involving leaders throughout their organization, from the chief data officer (CDO) to the data governance team.

International Women’s Engineering Day

Today is International Women’s Engineering Day 2019, and with women making up just 16% of engineering professionals, it’s important to highlight amazing women in the field, and encourage more young women to consider a career in engineering, specifically data science and data engineering in the process.

A Deeper Look: How the 281 Data Breaches in Q1 2019 Will Impact Companies

In this special guest feature, Kevin Gosschalk, CEO, Arkose Labs, believes that machine learning will allow organizations to better monitor authentic and inauthentic traffic, identify what the incoming traffic looks like and act against the traffic if labeled as inauthentic to stop automated fraud before it happens.

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.

Survey Reveals Widespread Lack of IT Planning for AIOps

A recent survey of IT, business, security and operations executives shows that 76 percent of IT teams have not yet implemented artificial intelligence technologies to improve data center operations, despite the benefits of AIOps for business efficiency. In addition, just over half of survey respondents still have no budgets planned for AIOps projects in the next one to three years. Further, the survey reveals that most IT leaders are still struggling to implement effective strategies for AIOps due to a lack of clarity about their own technology expectations and business objectives.

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?

The Key to Future-Proofing any Enterprise Data Architecture: Create Optionality

In this special guest feature, Justin Borgman, Co-founder and CEO at Starburst Data, outlines how can IT future-proof the enterprise for the next big trend – by creating optionality. Specifically, he offers three strategic steps toward future-proofing the cloud data lake.

AI-Driven Data Catalogs: How to Find the Right one for Your Business

The commoditization of data has opened a world of opportunities up for countless enterprises. But as big data explodes, metadata initiatives are failing, and data discovery and retrieval is getting more and more difficult. A new white paper from IO-Tahoe explores data catalogs as a potential answer to this challenge.