WASHINGTON--(BUSINESS WIRE)--Data Society, the leading provider of custom Data Science Training and cutting edge AI Solutions to Government Agencies and Fortune 500 companies, announced today that CEO and co-founder Merav Yuravlivker had been named an Ernst & Young LLP (EY US) Entrepreneur Of The Year 2021 Mid-Atlantic Award finalist. The program honors unstoppable business leaders whose ambition, ingenuity, and courage in the face of adversity help catapult from the now to the next and beyond.

Being recognized as an EY Entrepreneur of the Year finalist is a true testament to the talent, commitment, and passion of the Data Society team, said Merav Yuravlivker, CEO and co-founder, Data Society. Our goal is to transform how organizations and Government Agencies operate, solve critical challenges, and value their workforce through custom training academies.

A panel of independent judges selected Merav Yuravlivker. Award winners will be announced during a special virtual celebration on August 3rd and will become lifetime members of an esteemed community of Entrepreneur Of The Year alumni from around the world.

A leader in Data Science Training for the past seven years, Data Society, has worked with organizations to build their data capacity and open up new opportunities for expansion by developing customized Data Academies. These programs have led to the up-skilling of thousands of professionals, saving their employers hundreds of millions of dollars. Data Societys response to COVID-19 included critical support to Government initiatives and has doubled its team during this time.

Regional award winners are eligible for consideration for the Entrepreneur Of The Year National Awards, announced in November at the Strategic Growth Forum, one of the nations most prestigious gatherings of high-growth, market-leading companies. The Entrepreneur Of The Year National Overall Award winner will then compete for the EY World Entrepreneur Of The Year Award in June 2022.

Sponsors

Founded and produced by Ernst & Young LLP, the Entrepreneur Of The Year Awards are nationally sponsored by SAP America and the Kauffman Foundation.

About Data Society

Data Society specializes in providing customized signature training programs and AI/ML Solutions to executives, analysts, scientists and engineers on a variety of programming, data literacy, data analysis, data visualization, data science, machine learning and artificial intelligence topics. Since inception in 2014, Data Society has demonstrated success in training government teams across federal, state and local agencies, as well as Fortune 500 clients. datasociety.com

In Mid-Atlantic, sponsors also include

PNC, DLA Piper LLP, the Washington Business Journal, the Baltimore Business Journal and Cooley LLP.

About Entrepreneur Of The Year

Entrepreneur Of The Year is the worlds most prestigious business awards program for unstoppable entrepreneurs. These visionary leaders deliver innovation, growth and prosperity that transform our world. The program engages entrepreneurs with insights and experiences that foster growth. It connects them with their peers to strengthen entrepreneurship around the world. Entrepreneur Of The Year is the first and only truly global awards program of its kind. It celebrates entrepreneurs through regional and national awards programs in more than 145 cities in over 60 countries. National Overall winners go on to compete for the EY World Entrepreneur Of The Year title. ey.com/us/eoy

About EY

EY exists to build a better working world, helping create long-term value for clients, people and society and build trust in the capital markets.Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. Information about how EY collects and uses personal data and a description of the rights individuals have under data protection legislation are available via ey.com/privacy. EY member firms do not practice law where prohibited by local laws. For more information about our organization, please visit ey.com.

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EY Announces Merav Yuravlivker of Data Society as an Entrepreneur Of The Year 2021 Mid-Atlantic Award Finalist - Business Wire

Medical student James E.K. Hildreth was on his first clinical rotation when he saw the patient, a Black woman in her early 20s who had just given birth. It was the early 1980s and AIDS was spreading, with no treatment for the virus. Both the mother and her baby did not make it.

There was nothing we could do except treat their symptoms and watch them die, Dr. Hildreth says quietly. The experience so affected him he changed his specialty from training to be a transplant surgeon to an HIV investigator. He became one of the worlds top HIV/AIDS researchers, with much of his work focusing on blocking HIV infection by learning how it gets into cells.

Now, as president and CEO of Meharry Medical College in Nashville, one of the nations oldest and largest historically Black academic health science centers, he sees similarities between the AIDS era and the COVID-19 pandemic responses initial reluctance by some government officials to acknowledge the gravity of the virus and its impact on people of color and says they must not be repeated.

Theres a lot that we learned, Dr. Hildreth says. But one thing we learned for sure is that we need to do a better job of diversifying our health care work force. We need to spend more money on public health and preventive medicine to prevent this from happening again. And it also illustrates the importance of improving the basic health status of all of us, so that the next time this happens, we wont be having the same conversation again. This is not the last pandemic for sure.

Meharry is among the academic institutions and nonprofit organizations that are grantees of Microsoft AI for Health, which uses artificial intelligence (AI) and Azure high-performance computing to help improve the health of people and communities worldwide. AI for Health was launched a few months before COVID-19, and once the pandemic struck, more than 180 AI for Health grants went to those on the front lines of COVID-19 research, data and insights.

John Kahan, Microsoft vice president, Chief Data Analytics Officer and global lead for the AI for Health program, says when the pandemic started, so little information was known, and there was a massive race for data and insights.

I think that our learning is in better shape now, he says. The science is in better shape. But it is still unclear that the governments of the world have gotten together on a common set of standards around exactly what data must be gathered immediately after a pandemic has been declared.

Other AI for Health grantees, including Brown University School of Public Health, the Institute for Health Metrics and Morehouse School of Medicine, agree about the work that remains to be done.

Dr. Ashish Jha, dean of the Brown University School of Public Health, is a pandemic expert who now is also a familiar face to many television viewers in the U.S. for his perspectives. Using Azure and Power BI, Brown and Microsoft AI for Health developed a comprehensive COVID-19 dashboard that includes whether states in the U.S. are meeting COVID-19 testing target numbers, risk levels for each county in the country, and vaccine distribution and administration data. The dashboard also includes worldwide figures, and has become a helpful tool for the public, as well as for policymakers and leaders, to gauge the progress of the vaccine rollout.

Whats important about it and other COVID-19-related dashboards that have since been created is that they represent the first time such important tools have become widely available.

Fundamentally the public health systems data infrastructure (during COVID-19) sort of worked a little, but not nearly enough, Dr. Jha says.

Every public health department in the U.S. has its own data infrastructure for collecting information on infections, tests, hospitalizations and deaths and theyre really, really old, clunky systems, Dr. Jha says. What that means is that there are places across the country when somebody has a positive COVID-19 test, they might send that information to their local health department by literally printing out the test result, and faxing it to the local health department, who will then hand-enter it into a computer system. This is how data is still largely being collected.

It hindered the COVID-19 response in the U.S., Dr. Jha says. At a national level, until very recently, we had no government-driven data on infections and cases and deaths. In fact, national data was being aggregated by a group of journalists who were pulling together data and cleaning it up across every state and putting it together. Even the previous White House (administration) was largely using this data as opposed to using federal data.

Dr. Jha says a collection of anonymized, non-health related data such as restaurant reservations made through an app also are incredibly helpful in providing information about peoples behaviors during the pandemic, such as their willingness to go out for dinner. Its really a way of measuring peoples sense of safety in their community, he says. For example, we saw reservation numbers fall as infection numbers began to rise, well before any policy was made on shutting down restaurants.

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Before the next pandemic: Lessons learned, and those still to be absorbed - Stories - Microsoft

As a data science practitioner, think about all the data you have at your disposal. Then think about the latest data science methods you know about. Now, try answering these two questions: Why do your users adopt or reject your products? And how do they use these products?

Both may sound simple, but theyre trick questions. You can much more easily figure out which features of the products are being adopted, when and where theyre adopted, and perhaps even by whom.

Answering why and how questions with analytics, however, is much more complicated because you need to better understand your users, the contexts in which they operate, their considerations, and their motivations. Though answers to these questions are critical, data science teams cannot always rely only on assumptions, models, and numbers to understand the choices users make and the decisions that lead them to use a product in the ways they do.

The purpose of this little thought exercise is to suggest that, while analytics has many advantages, it also has its limits. Recognizing these limits will help you broaden your insight and become more innovative.

But to achieve this, youll need to engage in exploration where parameters and variables are less known, assumptions are mostly absent, and curiosity abounds. Youll need to think in a way that diverges from how you were trained, and youll need to use data and research methods fundamentally different from those you usually rely on.

In short, think about incorporating qualitative research into your analytics process.

Typical analyses involve getting data from users devices, their logged activities, or through user experiments such as A/B testing. But to answer why and how, youll need to learn the perspectives, meanings, and considerations of users from them directly.

Instead of top-down analytics, gather data by getting out into the field the contexts in which your users operate. Rather than rely on known hypotheses and existing variables to carry out deductive work (by far the more common form of analysis), immerse yourself in an inductive process of qualitative research.

The data collected in qualitative research is different from what youre used to in data science. This is how it works:

Note that, unlike traditional data used in data science, qualitative data are multilayered and complex. Field observations are tied to notes, the notes are then connected to interviews, both are then connected to the documents.

Its not a linear process, and by going back-and-forth between these interconnected layers of data, patterns emerge, research questions get refined, new behaviors and characteristics identified, and insights gained. And because the data is intentionally collected mostly in an unstructured fashion (meaning not answers to specific, close-ended questions) the input reflects different perspectives.

All of this can lead to refined questions and hypotheses to further pursue using data science tools.

Read More From Our Expert ContributorsIs a Data Lake Right for Your Company?

In business contexts, qualitative research is mainly reserved for studies of user experience (UX), product and UX design, and innovation. This intensive research work is largely disconnected from the work done by data science teams.

However, if your data context involves people, you should consider bridging this disconnect.

Take, for instance, an engineering team at Indeed that realized they needed to create a new measure for lead quality but didnt have enough background about leads and how to assess their properties. So they spent some time observing, interviewing, and analyzing documents from their account executives. By analyzing these data, they identified features that theyd not considered before and developed the measure they were after.

Being able to collect data on new features and designing machine learning models added significant value. But they realized that as the market, user needs, and their platform continued to evolve, it was important to return to collecting qualitative data from time to time to further inform their analytics pipeline. This ongoing integration of qualitative data and big data resulted in millions of dollars in added revenue.

Or consider the results of having qualitative research and data science teams work together at Spotify. Despite having a wealth of user data at the online streaming service, the company still needed to make sense of users behavior when receiving ads. The data science team followed the standard approach and performed an A/B test (with the intervention being skippable ads and the control being the standard ad experience). The results led the data science team to identify distinct behavior profiles.

Interestingly, the company also asked qualitative researchers to directly study users. Their findings were fundamentally different. For instance, they found that some of these profiles had nothing to do with inherent choices but were actually more an outcome of confusion about features and presented information.

Learning from this experience, the company started embracing a mixed methods approach (where qualitative data is integrated with more structured big data) to leverage the benefits of both approaches. They established a common research query, devised a process where researchers continuously communicated, and then triangulated their insight with both qualitative and quantitative data.

The result was more comprehensive research data, where business and design decisions, such as notifying users explicitly about ad skip limits, were based upon insight gained from users and data about users.

There are several ways for a data science team to engage with qualitative data:

You should also familiarize yourself with the three data collections methods (observations, interviews, and document analysis) that are at the core of qualitative research:

Finally, connect with researchers who have experience with immersive research, inside and outside your company. They can help you as you are thinking about ways to collect and analyze qualitative data. And perhaps you can offer to help them with parts of the tedious analysis of the more quantitative bit of their data too.

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Improve Your Insight by Mixing Qualitative Research With Data Science - Built In

To do really important research in environmental policy, said Francesca Dominici, the first thing we need is data.

Dominici, a professor of biostatistics at the Harvard T.H. Chan School of Public Health and co-director of the Harvard Data Science Initiative, recently presented the Henry W. Kendall Memorial Lecture at MIT. She described how, by leveraging massive amounts of data, Dominici and a consortium of her colleagues across the nation are revealing, on a grand scale, the effects air pollution levels have on human health in the United States. Their efforts are critical for providing a data-driven foundation on which to build environmental regulations and human health policy. When we use data and evidence to inform policy, we can get very excellent results, Dominici said.

Overall, air pollution has dropped dramatically nationwide in the past 20 years, thanks to regulations dating back to the Clean Air Act of 1970. On average, we are all breathing cleaner air, said Dominici. But the research efforts of Dominici and her colleagues show that even relatively low air pollution levels, like those currently present in much of the country, can fall well within national regulations and still be harmful to health. Moreover, recent patterns of decreasing air pollution have left certain geographic areas worse off than others, and exacerbated environmental injustice in the process. We are not cleaning the air equally for all of the racial groups, Dominici said.

Speaking over Zoom to audience members tuning in from around the world, Dominici shared these findings and discussed the underlying methodologies at the 18th Henry W. Kendall Memorial Lecture on April 21. This annual lecture series, which is co-sponsored by the MIT Center for Global Change Science (CGCS) and MITs Department of Earth, Atmospheric and Planetary Sciences (EAPS), honors the memory of the late MIT professor of physics Henry W. Kendall. Kendall was instrumental in bringing awareness of global environmental threats to the world stage through the World Scientists' Warning to Humanity in 1992 and the Call for Action at the Kyoto Climate Summit in 1997. The Kendall Lecture spotlights leading global change science by outstanding researchers, according to Ron Prinn, TEPCO Professor of Atmospheric Science in EAPS and director of CGCS.

How Much Evidence Do You Need? 18th Henry W. Kendall Lecture

In the various studies Dominici discussed, she and her colleagues honed in on a specific kind of harmful air pollution called fine particulate matter, or PM2.5. These tiny particles, less than 2.5 microns in width, come from a variety of sources including vehicle emissions and industrial facilities that burn fossil fuel. Particulate matter can penetrate very deep into the lungs [and] it can get into our blood, said Dominici, noting that this can lead to systemic inflammation, cardiovascular disease, and a compromised immune system.

To analyze how much of a risk PM2.5poses to human health, Dominici and her colleagues turned to the data specifically, to large datasets about people and the environment they experience. One dataset provided fine-grained information on the more than 60 million Americans enrolled in Medicare, including not only their health history, but also factors like socioeconomic status and Zip code. Meanwhile, a team led by Joel Schwartz, a professor of environmental epidemiology at the Harvard T.H. Chan School of Public Health, amassed satellite data on air pollution, weather, land use, and other variables, combined it with air quality data from the EPAs national network, and created a model that provides daily levels of PM2.5for every square kilometer in the continental United States over the last 20 years. In this way we could assign, to every single person enrolled in the Medicare system, their daily exposure to PM2.5, said Dominici.

Combining and analyzing these datasets provided a holistic look at how PM2.5affects the population enrolled in Medicare, and yielded several important findings. Based on the current national ambient air quality standards (NAAQS) for PM2.5, levels below 12 micrograms per cubic meter are considered safe. However, Dominicis team pointed out that even levels below that standard are associated with a higher risk of death. They further showed that making air quality regulations more stringent by lowering the standard to 10 micrograms per cubic meter would save an estimated 140,000 lives over the course of a decade.

The scope of the datasets enabled Dominici and her colleagues to use not only traditional statistical approaches, but also a method called matching. They compared pairs of individuals who had the same occupations, health conditions, and racial and socioeconomic profiles, but who differed in terms of PM2.5exposure. In this way, the researchers could eliminate potential confounding factors and lend further support to their findings.

Their research also illuminated issues of environmental injustice. We started to see some drastic environmental differences in risk across socioeconomic and racial groups, said Dominici. Black Americans have a risk of death from exposure to PM2.5that is three times higher than the national average. Asian and Hispanic populations, as well as people with low socioeconomic status, are also more at risk than the national population as a whole.

One factor behind these discrepancies is that air pollution has been decreasing at different rates in different parts of the country over the past 20 years. In 2000, nearly the entire eastern half of the United States had relatively high levels of PM2.5at 8 micrograms per cubic meter or higher. In 2016, those pollution levels had dropped dramatically across much of the map, but remained high in areas with the highest proportions of Black residents. Racial inequalities in air pollution exposure are actually increasing over time, said Dominici. She noted that one thing to consider is whether future regulations can tackle such inequities while also lowering air pollution for the entire nation on average.

Issues of both air pollution and environmental injustice have been thrown into stark relief during the Covid-19 pandemic. An early study on Covid-19 and air pollution led by Dominici showed that long-term exposure to higher levels of air pollution increased the risk of dying from Covid-19, and that areas with more Black Americans are even more at risk. Additional research showed that during last years wildfire season in California, up to 50 percent of Covid-19 deaths in some areas were attributable to the spikes in PM2.5that result from wildfires.

Due to a lack of data on individual Covid-19 patients, some of these analyses were based on county-level data, which Dominici noted was a major limitation. Fortunately, in some geographical areas, weve started getting access to individual-level records, said Dominici. Access to more and better data has sparked additional research around the world on the link between air pollution and Covid-19. Dominici was also part of an international collaboration that estimated, for example, that 13 percent of Covid-19 deaths in Europe were attributable to fossil-fuel related emissions.

For Dominici, a data scientist at heart, findings like these highlight the role of data science in influencing critical environmental policy decisions. Our all being devastated by this pandemic could provide an additional source of evidence of the importance of controlling fossil-fuel related emission.

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How data science gives new insight into air pollution in the US - MIT News