#data democratization

[newfangled-polusai]

Top 5 Benefits of Low-Code/No-Code BI Solutions

Low-code/no-code Business Intelligence (BI) solutions offer a paradigm shift in analytics, providing organizations with five key benefits. Firstly, rapid development and deployment empower businesses to swiftly adapt to changing needs. Secondly, these solutions enhance collaboration by enabling non-technical users to contribute to BI processes. Thirdly, cost-effectiveness arises from reduced reliance on IT resources and streamlined development cycles. Fourthly, accessibility improves as these platforms democratize data insights, making BI available to a broader audience. Lastly, agility is heightened, allowing organizations to respond promptly to market dynamics. Low-code/no-code BI solutions thus deliver efficiency, collaboration, cost savings, accessibility, and agility in the analytics landscape.

[atozofsoftwareengineering]

Demystifying Data Engineering: The Backbone of Modern Analytics

Hey friends! Check out this in-depth blog on #DataEngineering that explores its role in building robust data pipelines, ensuring data quality, and optimizing performance. Discover emerging trends like #cloudcomputing, #realtimeprocessing, and #DataOps

In the era of big data, data engineering has emerged as a critical discipline that underpins the success of data-driven organizations. Data engineering encompasses the design, construction, and maintenance of the infrastructure and systems required to extract, transform, and load (ETL) data, making it accessible and usable for analytics and decision-making. This blog aims to provide an in-depth…

[jcmarchi]

Gartner Data & Analytics Summit São Paulo: Mercado Livre’s AI and Data Democratization in Brazil

New Post has been published on https://thedigitalinsider.com/gartner-data-analytics-summit-sao-paulo-mercado-livres-ai-and-data-democratization-in-brazil/

Gartner Data & Analytics Summit São Paulo: Mercado Livre’s AI and Data Democratization in Brazil

I had the opportunity to attend the Gartner Data & Analytics Summit in São Paulo, Brazil, from March 25-27. The conference brought together industry leaders, experts, and practitioners to discuss the latest trends, strategies, and best practices in data and analytics. Brazil’s growing importance in the AI landscape was evident throughout the event, with many insightful presentations and discussions focusing on AI adoption and innovation.

One of the interesting talks I attended was delivered by Eduardo Cantero Gonçalves, a senior Data Analytics manager at Mercado Livre (MercadoLibre). Mercado Livre is a leading e-commerce and fintech company that has established itself as a dominant player in the Latin American market. With operations spanning 18 countries, including major economies such as Brazil, Argentina, Mexico, and Colombia, Mercado Livre has built a vast online commerce and payments ecosystem. The company’s strong market presence and extensive user base have positioned it as a leader in the region.

During his presentation, Gonçalves shared Mercado Livre’s remarkable journey in democratizing data and AI across the organization while fostering a strong data-driven culture. As AI continues to transform industries worldwide, Mercado Livre’s experience offers valuable lessons for organizations looking to harness the power of AI and build a data-driven culture.

In this article, we will explore the key takeaways from Gonçalves’s presentation, focusing on the company’s approach to data democratization, empowering non-technical users with low-code AI tools, and cultivating a data-driven mindset throughout the organization.

Mercado Livre’s Data Democratization Journey

Mercado Livre’s journey towards data democratization has been a transformative process that has reshaped the company’s approach to data and AI. Gonçalves emphasized the importance of transitioning from a centralized data environment to a decentralized one, enabling teams across the organization to access and leverage data for decision-making and innovation.

A key aspect of this transition was the development of in-house data tools. By creating their own tools, Mercado Livre was able to tailor solutions to their specific needs and ensure seamless integration with their existing systems. This approach not only provided greater flexibility but also fostered a sense of ownership and collaboration among teams.

One of the most significant milestones in Mercado Livre’s data democratization journey was the introduction of machine learning tools designed for both data scientists and business users. Gonçalves highlighted the importance of empowering non-technical users to harness the power of AI and ML without relying heavily on data science teams. By providing low-code tools and intuitive interfaces, Mercado Livre has enabled business users to experiment with AI and ML, driving innovation and efficiency across various departments.

The democratization of data and AI has had a profound impact on Mercado Livre’s operations and culture. It has fostered a more collaborative and data-driven environment, where teams can easily access and analyze data to inform their strategies and decision-making processes. This shift has not only improved operational efficiency but has also opened up new opportunities for growth and innovation.

Empowering Non-Technical Users with Low-Code AI Tools

One of the key highlights of Mercado Livre’s data democratization journey is their focus on empowering non-technical users with low-code AI tools. During his presentation, Gonçalves emphasized the importance of enabling business users to experiment with AI and machine learning without relying heavily on data science teams.

To achieve this, Mercado Livre developed an in-house tool called “Data Switch,” which serves as a single web portal for users to access all data-related tools, including query builders, dashboards, and machine learning tools. This centralized platform makes it easier for non-technical users to leverage AI and ML capabilities without needing extensive programming knowledge.

Gonçalves specifically mentioned that Mercado Livre introduced low-code machine learning tools to allow business users to run experiments independently. By providing intuitive interfaces and pre-built models, these tools enable domain experts to apply their knowledge and insights to AI-powered solutions. This approach not only democratizes AI but also accelerates innovation by allowing more people within the organization to contribute to AI initiatives.

The impact of empowering non-technical users with low-code AI tools has been significant for Mercado Livre. Gonçalves noted that the company saw a substantial increase in the number of active users, data storage, ETL jobs, and dashboards following the introduction of these tools.

Mercado Livre’s success in this area serves as a valuable case study for other organizations looking to democratize AI and empower their workforce. By investing in low-code AI tools and providing the necessary training and support, companies can unlock the potential of their non-technical users and foster a culture of innovation.

Fostering a Data-Driven Culture

In addition to democratizing data and AI tools, Mercado Livre recognized the importance of fostering a data-driven culture throughout the organization. Gonçalves highlighted several key initiatives that the company undertook to cultivate a mindset that embraces data and AI-driven decision-making.

One notable step was the creation of a dedicated Data Culture area within Mercado Livre. This team was tasked with promoting data literacy, providing training, and supporting data-driven initiatives across the organization.

To measure the success of their data culture efforts, Mercado Livre developed a “Data Driven Index” that tracks user engagement with data tools. This index provides a quantitative measure of how well employees are adopting and leveraging data in their daily work. By regularly monitoring this index, the company can identify areas for improvement and adjust their strategies accordingly.

Another key initiative was the “Data Champions” program, which aimed to train advanced users who could then help multiply the data-driven culture throughout the organization. These champions serve as advocates and mentors, promoting best practices and assisting their colleagues in leveraging data and AI tools effectively. By empowering a network of champions, Mercado Livre was able to scale its data culture efforts and drive adoption across the company.

Lessons Learned from Mercado Livre’s Experience

Mercado Livre’s journey in democratizing data and AI offers valuable lessons for other organizations looking to embark on a similar path. One of the key takeaways from Gonçalves’s presentation was the importance of executive sponsorship in promoting a data-driven culture. Having strong support and advocacy from leadership is critical in driving organizational change and ensuring that data and AI initiatives are given the necessary resources and priority.

Another important lesson is the value of collaborating with HR to integrate data-driven culture into employee onboarding and development programs. By making data literacy and AI skills a core part of employee training, organizations can ensure that their workforce is well-equipped to leverage these tools effectively. Mercado Livre’s partnership with HR helped them to scale their data culture efforts and make it a fundamental part of their employees’ growth and development.

Finally, Gonçalves emphasized the importance of continuously measuring and iterating on data-driven initiatives. By tracking key metrics such as the Data Driven Index and regularly seeking feedback from employees, organizations can identify areas for improvement and make data-informed decisions to optimize their strategies. This iterative approach ensures that data and AI initiatives remain aligned with business objectives and drive meaningful impact.

As organizations navigate the challenges and opportunities of the AI era, Mercado Livre’s experience serves as a valuable case study in democratizing data and AI while fostering a data-driven culture. By empowering employees at all levels to leverage these tools and cultivating a mindset that embraces data-driven decision-making, companies can position themselves for success in our AI-driven world.

[elsa16744]

Exploring the Latest Data Analytics Trends | SG Analytics

The data analytics industry has evolved, delivering new solutions to business problems. This post discusses the latest data analytics trends.

[analyticspursuit]

6 Future Trends in Data Analytics You Need to Know

In this video, we're looking at 6 future trends in data analytics that you need to know. From the rise of big data to the explosion of machine learning, these trends will have a big impact on how we use data in the future.

If you're interested in data analytics and want to stay ahead of the curve, then you need to pay attention to these trends! By understanding what's happening in data analytics today, you'll be able to better prepare for tomorrow's challenges. So make sure to watch this video and learn about the future of data analytics!

[aviralz]

Unlocking Game-Changing Business Value with Data Democratization

Today, technology is advancing at a much faster rate than ever before, and enterprises are scrambling to get their hands on the latest technology in order to stay on top of the latest trends, including various services such as AI and data analytics. It is becoming increasingly important for organizations to democratize their data and take advantage of its many benefits.

Data is the lifeblood of the digital economy, and organizations must work diligently to extract greater value from the data they acquire from customers and across their entire operating landscape. However, not all businesses are ideally positioned to maximize the value of their data assets.

The Need for an Overarching Data Strategy

To extract all of the insights they require, businesses must have a well-coordinated data and analytics strategy in place. The most effective data strategies are those that are incorporated into the entire business plan and that provide consistent and repeatable practises, and processes for controlling and disseminating data across the organization. Furthermore, if the entire organization is involved from the start, they will be more willing to help drive the strategy forward.

The Right Technology Stack is Crucial

The right tech stack can help companies ensure that the end product is fit-for-purpose. When evaluating choices, they should consider where they want to go in the future and how they might empower more people to use data to generate insights and support decision-making.

Fine-Grained Access Controls Enable Users to Access Data

It’s critical to provide users with data sets that are relevant to their jobs. Regardless of the democratization model selected, it is critical to ensure data security and avoid duplication or misrepresentation. This can be accomplished by giving users fine-grained access controls to data. Read More Data Democratization

For More  b2b media company

[knowledgehound]

What Is Level of Significance in Hypothesis Testing and How Can Businesses Use It?

There are so many strategies and methods for collecting data from a target market or group of users. Some methods are passive, interpreting data collected by website cookies and tracking pixels to build an understanding of user behavior. Other methods are more proactive, taking the form of thoughtful marketing surveys to get behavior and attitude insights directly from consumers.

Whatever the data collection method, it is then up to researchers and insights teams to study that data, which leads us to hypothesis testing. Hypothesis testing is a way of life for many researchers when it comes to quantitative and qualitative research studies. Although learning about the level of significance is important, the overall question of what is the purpose of survey research and why researchers, especially in business situations, perform it, are a point to focus on as well.

What Are Some of the Benefits of Survey Research?

While it’s important to understand hypothesis testing in the context of business research, it’s also important to understand this statistical research method in the context of survey data and the important role surveys play in making transformative marketing decisions that impact a target market.

In most cases, the purpose of survey research is to gather information from a large collection of individuals to glean information about prospective customers, current users, and competitors or the competitive landscape. And while surveys are an extremely useful tool for conducting research, they come with a lot of benefits outside of simply gathering data.

For one, because surveys have morphed from in-person settings with pens and paper into digital venues with online forums and discussions, the cost of survey research has drastically diminished. If a business is looking for a cost-effective way to gain data about future client prospects, possible consumers, or brand loyal customers, survey research is a great way to find that information and to then make data-driven business strategy decisions.

Another perk is that survey research is extremely accessible and dependable. Because surveys are often used to directly collect information from a large number of individuals, they must be a highly versatile medium, able to be conducted on desktop and laptop computers, as well as smartphone and tablets when respondents are on the go. The dependability and accessibility of this research medium, in turn, makes survey data a dependable source for researchers to pull their insight.

Not only do some benefits of survey research include cost, accessibility, and dependability, survey research is also valuable in terms of unearthing business solutions through decisive customer data points. With KnowledgeHound’s survey data analysis experience, researchers can get to these important data cuts while eliminating information silos. Find and access key data points with a simple search through KnowledgeHound’s easy-to-use interface and share valuable insight with other members of your team.

An Intro to Hypothesis Testing: What Is P-Value in Research?

There are a lot of letters thrown about in research and hypothesis testing including something called the P-Value. If you’re someone with a curious mind or have upcoming research to take part in and need to know things, like what the p-value in research is, we’re here to break it down for you. Understanding the purpose and benefits of survey research are one thing, taking part in the analysis of the data from the research is another.

Prior to and when survey research is being conducted are when you’ll be paying attention to the P-Value. Simply put, the P-Value is the value of calculated probability. When looking to find out that a survey research’s data is statistically significant or if the data is not statistically significant, a researcher will go straight to the P-Value to gauge whether the value found is less than 0.05 or greater than 0.05.

The P-Value is calculated when a researcher is taking part in hypothesis testing that includes a null hypothesis, or rather, if the hypothesis test understands there to be no difference between two groups that are partaking in the testing.

So what is level of significance in hypothesis testing, then? It’s simply the singular value that researchers discover through data analysis to be either statistically significant (where p is <0.05) or insignificant (where p is >0.05). Most researchers will look to have a significance level of 95% (also known as statistically significant) in hypothesis testing and research.

With a significance level of 95% or greater, a researcher or non researcher will understand that the insights gathered from the data are not readily interpreted by happenstance. Put another way: the insights found and taken through the data are not mere coincidences. In addition, if the data is statistically significant, a researcher can dismiss the null hypothesis.

Hypothesis Testing in Business Research

Data-driven analytics and insights are increasingly important in business decisions for an organizational framework and future strategy development. You don’t have to work in science or in a scientific field of inquiry to use hypothesis testing. Not only is hypothesis testing in business research an important part of building out a strategy, it is also vital to use the data from the testing to verify that the strategy is working and then make adjustments, where needed, to improve results.

Even with that information, you might be thinking, “How should I practically use hypothesis testing in business research?” Well unfortunately, there’s no simple answer because hypothesis testing can be used in any variety of situations from managing sensitive financial information of a client to determining the effectiveness of a company’s social media strategy.

Even though both scenarios are distinctly different, hypothesis testing can help determine what an end-product would be with regard to an unproven question. What’s more, hypothesis testing in business research, especially when it comes to finances and a company’s internal and external (think clients) fiscal responsibility, is a proven method to guide next steps and decision-making within whole corporations, specific offices of an organization, or even one team of 10 at a large company.

Whether a researcher or non-researcher partakes in hypothesis testing in business through survey research, what’s critical to remember is that the data is what’s most important. However, because the raw numbers hold no meaning until they’re put into context, having a platform where data visualization takes place alongside data discovery including categorization and organization in an easily-digestible and user-friendly platform is essential. Learn how KnowledgeHound helps brands and businesses capitalize on data to further not just business objectives but also relationships with consumers.

ALSO READ: What Is the Difference Between a T-Test and a Z-Test?

[alwaysbewoke]

here is the sheet. it's an excel spreadsheet so it will download to your computer. this is really important for all the people who constantly say "democrats and republicans are the same." if they're not willing to do the work to either prove or disprove that notion, they're NOT someone ANYONE should be taking political advice from.

[newfangled-polusai]

What advantages does PolusAI provide in terms of speed and decision-making? PolusAI accelerates data analytics processes, offering nine times faster generation of dashboards and insights, enabling swift data-driven decision-making. This rapid processing allows businesses to act on insights five times faster than traditional methods, enhancing responsiveness and strategic agility. PolusAI’s homegrown NLP engine provides real-time insights, ensuring decision-makers have up-to-date information. By streamlining data analysis and reducing the time from data collection to actionable insights, PolusAI significantly improves operational efficiency and decision accuracy, helping businesses maintain a competitive edge and quickly adapt to market changes.

[qqueenofhades]

Going to cautiously posit that if they're moving toward calling Kentucky for Beshear (incumbent Democratic governor) in a blindingly red state Trump won by 30 points, in an off-year election, about 30 minutes after polls close statewide, that is a Good Sign.

[jcmarchi]

Joe Regensburger, VP of Research, Immuta – Interview Series

New Post has been published on https://thedigitalinsider.com/joe-regensburger-vp-of-research-immuta-interview-series/

Joe Regensburger, VP of Research, Immuta – Interview Series

Joe Regensburger is currently the Vice President of Research at Immuta. Aleader in data security, Immuta enables organizations to unlock value from their cloud data by protecting it and providing secure access.

Immuta is architected to integrate seamlessly into your cloud environment, providing native integrations with the leading cloud vendors. Following the NIST cybersecurity framework, Immuta covers the majority of data security needs for most organizations.

Your educational background is in physics and applied mathematics, how did you find yourself eventually working in data science and analytics?

My graduate work field was Experimental High Energy Physics. Analyzing data in this field requires a great deal of statistical analysis, particularly separating signatures of rare events from those of more frequent background events. These skills are very similar to those required in data science.

Could you describe what your current role as VP of Research at data security leader Immuta entails?

At Immuta, we are focused on data security. This means we need to understand how data is being used, how it can be misused, and providing data professionals with the tools necessary to support their mission, while preventing misuse. So, our role involves understanding the demands and challenges of data professionals, particularly in regards to regulations and security, and helping solve those challenges. We want to lessen the regulatory demands, and enable data professionals to focus on their core mission. My role is to help develop solutions that lessen those burdens. This includes developing tools to discover sensitive data, methods to automate data classification, detect how data is being used, and create processes that enforce data policies to assure that data is being used properly.

What are the top challenges in AI Governance compared to traditional data governance?

Tech leaders have mentioned that AI governance is a natural next step and progression from data governance. That said, there are some key differences to keep in mind. First and foremost, governing AI requires a level of trust in the output of the AI system. With traditional data governance, data leaders used to easily be able to trace from an answer to a result using a traditional statistics model. With AI, traceability and lineage become a real challenge and the lines can be easily blurred. Being able to trust the outcome your AI model reaches can be negatively affected by hallucinations and confabulations, which is a unique challenge to AI that must be solved in order to ensure proper governance.

Do You Believe There is a Universal Solution to AI Governance and Data Security, or is it more case-specific?

“While I don’t think there is a one-size-fits-all approach to AI governance at this point as it pertains to securing data, there are certainly considerations data leaders should be adopting now to lay a foundation for security and governance. When it comes to governing AI, it’s really critical to have context around what the AI model is being used for and why. If you’re using AI for something more mundane with less impact, your risk calculator will be a lot lower. If you’re using AI to make decisions about healthcare or training an autonomous vehicle, your risk impact is much higher. This is similar to data governance; why data is being used is just as important as how it’s being used.

You recently wrote an article titled “Addressing the Lurking Threats of Shadow AI”. What is Shadow AI and why should enterprises take note of this?

“Shadow AI can be defined as the rogue use of unauthorized AI tools that fall outside of an organization’s governance framework. Enterprises need to be aware of this phenomenon in order to protect data because feeding internal data into an unauthorized application like an AI tool can present enormous risk. Shadow IT is generally well-known and relatively easy to manage once spotted. Just decommission the application and move on. With shadow AI, you don’t have a clear end-user agreement on how data is used to train an AI model or where the model is ultimately sharing its responses once generated. Essentially, once that data is in the model, you lose control over it. In order to mitigate the potential risk of shadow AI, organizations must establish clear agreements and formalized processes for using these tools if data will be leaving the environment whatsoever.

Could you explain the advantages of using attribute-based access control (ABAC) over traditional role-based access control (RBAC) in data security?”

Role-based access control (RBAC) functions by restricting permits or system access based on an individual’s role within the organization. The benefit of this is that it makes access control static and linear because users can only get to data if they are assigned to certain predetermined roles. While an RBAC model has traditionally served as a hands-off way to control internal data usage, it is by no means indestructible, and today we can see that its simplicity is also its main drawback.

RBAC was practical for a smaller organization with limited roles and few data initiatives. Contemporary organizations are data-driven with data needs that grow over time. In this increasingly common scenario, RBAC’s efficiency falls apart. Thankfully, we have a more modern and flexible option for option control: attribute-based access control (ABAC). The ABAC model takes a more dynamic approach to data access and security than RBAC. It defines logical roles by combining the observable attributes of users and data, and determining access decisions based on those attributes. One of ABAC’s greatest strengths is its dynamic and scalable nature. As data use cases grow and data democratization enables more users within organizations, access controls must be able to expand with their environments to maintain consistent data security. An ABAC system also tends to be inherently more secure than prior access control models. What’s more, this high level of data security does not come at the expense of scalability. Unlike previous access control and governance standards, ABAC’s dynamic character creates a future-proof model.”

What are the key steps in expanding data access while maintaining robust data governance and security?

Controlling data access is used to restrict the access, permissions, and privileges granted to certain users and systems that help to ensure only authorized individuals can see and use specific data sets. That said, data teams need access to as much data as possible to drive the most accurate business insights. This presents an issue for data security and governance teams who are responsible for ensuring data is adequately protected against unauthorized access and other risks. In an increasingly data-driven business environment, a balance must be struck between these competing interests. In the past, organizations tried to strike this balance using a passive approach to data access control, which presented data bottlenecks and held organizations back when it came to speed. To expand data access while maintaining robust data governance and security, organizations must adopt automated data access control, which introduces speed, agility, and precision into the process of applying rules to data. There are five steps to master to automate your data access control:

Must be able to support any tool a data team uses.

Needs to support all data, regardless of where it’s stored or the underlying storage technology.

Requires direct access to the same live data across the organization.

Anyone, with any level of expertise, can understand what rules and policies are being applied to enterprise data.

Data privacy policies must live in one central location.

Once these pillars are mastered, organizations can break free from the passive approach to data access control and enable secure, efficient, and scalable data access control.

In terms of real-time data monitoring, how does Immuta empower organizations to proactively manage their data usage and security risks?

Immuta’s Detect product offering enables organizations to proactively manage their data usage by automatically scoring data based on how sensitive it is and how it is protected (such as data masking or a stated purpose for accessing it) so that data and security teams can prioritize risks and get alerts in real-time about potential security incidents. By quickly surfacing and prioritizing data usage risks with Immuta Detect, customers can reduce time to risk mitigation and overall maintain robust data security for their data.

Thank you for the great interview, readers who wish to learn more should visit Immuta.

[sher-ee]

What do you think?

*Qasim Rashid, Human Rights Attorney

[dontmeantobepoliticalbut]

Young women are more likely to identify as liberal now than at any time in the past two decades, a trend that puts them squarely at odds with young men.

44% of young women counted themselves liberal in 2021, compared to 25% of young men, according to Gallup Poll data analyzed by the Survey Center on American Life. The gender gap is the largest recorded in 24 years of polling. The finding culminates years of rising liberalism among women ages 18 to 29, without any increase among their male peers.

Several societal forces have conspired to push young women to the left in recent years, including the #MeToo movement, former President Trump, rising LGBTQ identification and, most recently, abortion policy. Slower-cooking trends in marital status and educational attainment have also nudged the needle.

“I think there is a big generational shift that happened with Generation Z women who were really coming of age in the last five years,” said Kelsy Kretschmer, a sociologist at Oregon State University who studies gender politics.

The rift between young men and women may widen further. Earlier this year, the Supreme Court overturned Roe v. Wade, a precedent that had protected abortion as a constitutional right for nearly half a century. The ruling has energized young women. New survey data, released this week, shows that 61% of young women consider abortion a critical issue, compared with 36% of all Americans.

“I would always choose a candidate that’s pro-abortion,” said Rose Merjos, 21, a government major at Wesleyan University in Connecticut who is an avowed liberal. “Almost everyone either knows someone who has had an abortion or has had one themselves. This is something everyone can relate to.”

The share of men who identify as liberal has held fairly steady for almost 25 years, according to annual Gallup surveys. Roughly one-quarter of men ages 18 to 29 term themselves liberal, year after year.

Meanwhile, among young women, liberalism has exploded. In the late 1990s and early 2000s, fewer than 30% of women identified as liberal. The liberal camp grew through the second term of former President George W. Bush. It expanded further during the tenure of former President Obama. It reached 39% in 2017 with the inauguration of Trump. In the last two years, liberalism surged anew.

“Young women today are much more liberal than young men,” Daniel Cox wrote in a June newsletter of the Survey Center on American Life, a project of the American Enterprise Institute. His work documents “a growing political rift” between young women and men.

Merjos attends a university long associated with both liberalism and activism. These days, though, she senses more of both among the women.

“In all of my government classes, there are probably two men out of 18 people,” she said. “ACLU [American Civil Liberties Union], that’s mostly women. I’m wondering if women are maybe just more inclined to be involved in the community, engaged in the community. And that liberalizes them.”

Ezra Meyer, 22, is a senior at the George Washington (GW) University who leads the College Republicans. He is a conservative on a campus that is overwhelmingly liberal and largely female. In conversations with classmates about politics, he treads lightly.

“My metric for deciding if I’m going to be friends with someone really does not come down to what their politics are,” he said. “It comes down to how tolerant they are.”

Meyer doesn’t know whether the men at GW skew more liberal or conservative than the women. But he has noticed a distinct trend among campus conservatives this fall.

“We’ve been doing a lot of recruiting of freshmen on campus,” he said. “And I would say, overwhelmingly, it has been male. The conservative females that do get involved, there’s fewer of them, but they tend to be way more passionate and way more involved.”

Several factors have liberalized the nation’s 20-something women. The most recent, and perhaps the most powerful, is #MeToo, an uprising against sexual assault, abuse and harassment that caught fire in 2017, empowering millions of women to come forward and seek justice.

The inauguration of Trump in the same year pushed more young women into the liberal column. The 45th president battled his own #MeToo allegations and proved uniquely unpopular among young, female voters. Polling in 2016 showed that only 25% of women ages 18 to 34 favored Trump, compared with 40% of same-aged men.

The rise of liberalism among young women has also marched apace with a dramatic increase in young people identifying as lesbian, gay, bisexual, transgender or queer. In a recent survey, 56% of young women reported exclusive attraction to men, while three-quarters of young men said they were solely attracted to women. Prior research suggests LGBTQ Americans of all ages trend toward liberalism.

Several longer-term trends have fed the liberalization of young women as well. One is marriage. The share of women ages 18-29 who are married has fallen by half in twenty years, from 31% in 2000 to 15% in 2021, according to the National Opinion Research Center.

The growing ranks of single, 20-something women feel a sense of “linked fate,” researchers say. They gravitate toward female friends in political views, whereas married women more often mirror the politics of their spouses.

“The correlation between women’s sense of linked fate and liberal political preferences suggests that the Democratic Party will benefit” from declining marriage rates among young women, Kretschmer and two co-authors wrote in a 2017 paper for the journal Political Research Quarterly. They noted that “women make up the majority of the population and vote at high rates.”

Women also outpace men in educational attainment, a trend that dates to the 1980s. The ratio of women to men in college enrollment now stands at roughly 60 to 40, and it continues to grow. Americans who complete college are more liberal than those who do not.

“Putting off marriage, going to college, entering the workforce, women are doing that at much higher rates than they used to,” said Marc Hetherington, a professor of political science at the University of North Carolina at Chapel Hill. “And all of those things are going to make conservatism and the Republicans significantly less attractive to women.”

In 1998, the first year of data collected by Gallup in its Social Series surveys, 28% of young men and 29% of young women identified as liberal. The gender gap in liberalism grew steadily wider in the 2000s, wider still in the 2010s. The 2021 poll yielded a 19-point spread between young men and young women, the largest on record.

“I do have some male friends that are moderate,” said Luci Paczkowski, 20, a California liberal. “And it annoys the hell out of me.”

What bothers Paczkowski about her nonliberal friends is not their centrism but her suspicion that they “do not have any clue why they are moderate. They just do not want to pick a side and, therefore, they are apathetic.”

[kp777]

By Julia Conley

Common Dreams

May 08, 2024

"The Democratic establishment is dysfunctionally out of touch with its voters on this issue," said one strategist.

A day after U.S. President Joe Biden commemorated the Holocaust, speaking about Americans' "obligation to learn the lessons of history" to ensure another mass slaughter of a religious or ethnic group never takes place, new polling showed the majority of U.S. voters whose support Biden is counting on in November believe Israel—with U.S. backing—is now committing genocide.

Journalist Mehdi Hasan's new media organization, Zeteo, partnered with progressive think tank Data for Progress to poll 1,265 U.S. voters from April 26-29, as Israel's ground invasion of Rafah loomed, threatening more than 1 million Palestinians in Gaza who have been forcibly displaced since October.

The poll released Wednesday found that 56% of Democratic voters believe Israel is committing a genocide against Palestinians in the enclave, where in addition to constant bombings and ground attacks, residents have faced Israel's blockade on nearly all humanitarian aid. The blockade has pushed northern Gaza into famine and is causing acute food insecurity among the entire population.

Nearly 40% of all voters believe Israel is committing a genocide, and 7 in 10 support a permanent cease-fire.

More than 50% of voters said Israel's full-scale assault on Gaza, where 2.3 million Palestinians live, has been ineffective at bringing the Israeli hostages kidnapped by Hamas on October 7 to safety.

Fifty-four percent said they support suspending all U.S. arms sales to Israel until it stops blocking American humanitarian aid from entering Gaza. Such a suspension would be in accordance with Section 620I of the Foreign Assistance Act of 1961.

Israel and the U.S. have repeatedly claimed that the Israel Defense Forces (IDF) is taking steps to protect the lives of civilians—even as the world has learned of mass graves found with the bodies of Palestinian women and children, some with their hands tied behind their backs. In April, Israeli journalist Yuval Abraham of +972 Magazine reported that military officials have permitted up to 100 civilian deaths for every Hamas member killed, and that the IDF has targeted Hamas fighters in their homes instead of at military outposts.

The Zeteo/Data for Progress poll was released more than four months after the International Court of Justice announced an interim ruling that Israel is "plausibly" committing genocide, which came after South Africa brought its case to the United Nations court.

South African attorney Tembeka Ngcukaitobi gave a 22-minute speech during the hearing, cataloging the numerous genocidal statements made by top Israeli officials since October, up to that point. Last week, Israeli Finance Minister Bezalel Smotrich called for the "total annihilation" of Gaza cities including Rafah.

The poll was also released as mass protests continued on college campuses across the U.S., with police aggressively cracking down at many schools as they ignore attacks on students by pro-Israel mobs, as in the case of University of California, Los Angeles last week.

A separate poll released Wednesday by USA Today and Suffolk University found that Democratic voters are split in their views of the movement. Thirty percent supported the protests, while 39% agreed with their demands but questioned some of their tactics. Two-thirds of respondents said they feared more violent confrontations would arise from the protests.

The Data for Progress survey is the latest sign that Biden, who signed a foreign aid package including $17 billion in additional military aid for Israel last month, faces widespread discontent among the coalition of voters that supported him in 2020. In January, The Economist and YouGov found that a full 50% of people who voted for him believed Israel was committing genocide.

More than 100,000 Democratic primary voters in Michigan—which Biden won by just 150,000 votes in 2020—voted for "uncommitted" on their ballots in February, hoping to send the message to the president that U.S. support for Israel must end. Similar results were seen in primaries in Wisconsin, Minnesota, and Washington state.

Strategist Nadia Rahman said the poll shows the Democratic establishment is "dysfunctionally out of touch with its voters on this issue."

"This is some of the clearest data yet that there's a massive disconnect between the media and what's happening on the ground," said journalist Ed Oswald. "And why yes, Biden's re-election is in big trouble."