Creating a Healthy and Productive Workforce

In today’s fast-paced and competitive business environment, creating a healthy and productive workforce has become a top priority for organizations worldwide. However, it is essential to understand that employee well-being is not just limited to physical health. Mental health is a vital component of overall well-being, and addressing it in the workplace is necessary. A healthy work environment considers the mental and emotional needs of its employees. It gives them the essential resources and support to manage stress, anxiety, and other mental health issues. Throughout this leading with purpose segment, Abby Grasso, Executive Director at NAMI, and Kelsey Haas, YESability Chair, will explore some strategies that organizations and individuals can adopt to create a healthy and productive workforce while emphasizing the importance of mental health in the workplace.


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Meet Our Guests

Abby Grasso

Abby has 25 years of clinical service experience. She has worked inpatient and has supported people struggling with suicidal and homicidal thoughts. Early in her career, Abby ran a shelter for adolescent runaways. She also supported people in outpatient roles within their homes. In her current position, NAMI allows her to help people in a more supportive way. She can support, educate, and advocate for anyone impacted by mental health.



Kelsey Haas

Kelsey is currently a Sales Consultant with Swoon Consulting. In addition, she is also the chair of Swoon’s DE&I committee, YESability. YESability aims to educate on how to best support individuals with various disabilities in the workplace. They want to eliminate the stigma surrounding disabilities of all types by providing ongoing education to ensure the Swoon and Swoon Consulting teams know what resources are available to them internally and externally for contractors, consultants, and clients.


Managing Bias and Making Room for Women to Thrive

Data and analytics is a rapidly growing industry that has revolutionized our lives and work. However, despite the increasing demand for tech talent, women remain significantly underrepresented in this industry. Studies show that women represent only a small fraction of the workforce in technology, with many facing obstacles and biases that prevent them from advancing in their careers. Gender bias in technology has been a topic of discussion for years, with organizations recognizing the need to create more inclusive workplaces. In this Leading with Purpose series by Swoon, Lori, Chief Data and Analytics Officer at BMO, and Quyen, Vice President at Swoon Consulting, explore the importance of managing bias and making room for women to thrive. They examine the various forms of bias and provide strategies for individuals and organizations to create a more inclusive environment for women.

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Meet Our Panelists

Lori Bieda

Lori is currently the Chief Data and Analytics Officer for BMO, the North American retail and business bank. She has been leading data and analytics teams for nearly 25 years across the world and is a huge advocate for women’s empowerment which is why she started, ElleExcel.

ElleExcel Women’s Circle is designed to empower women to excel in all aspects of their lives, offering inspiration, learning, and connection with other like-minded women – across four fundamental drivers: ME (self help and awareness), WE (community and connection), Wellness and Leadership.


Quyen Pham

Quyen is the Vice President of Swoon Consulting. Much like Lori, she is passionate about women in leadership and creating a space for women to thrive and advance within the workplace. She has done extensive research and has written articles with Forbes Business Council on the subject.


Megan Parrish

Megan is the Director of Marketing and Sales Operations at Swoon. She would not be where she is today without the strong women in her life, both personally and professionally. She believes it is so important to pass that forward –advocating for young women leaders by helping to amplify their voices and providing support in career development. 


Megan: What aspects of managing bias and making room for women to thrive are you most passionate about?

Lori: I have been seeing that bias has evolved, and we all have probably witnessed it. In a way, it is like a cell that mutates. Although it starts as one thing, it adapts and reconstructs itself. But bias is still here and around us at work and in our personal lives. It is also harder to detect, which is why I am so passionate about talking on this subject. It is something we all should educate ourselves on so we can help put an end to it. Most of the time, bias is subversive, leaving women questioning themselves. Was it me? Did that happen? Did they say that? Am I wrong in my interpretation of it? I think bias can be as blunt as more men being promoted or in a particular role than women or as subtle as being spoken over or having our thoughts reframed by someone else. Another example would be how, as women, we often get asked how we are available for this meeting or who is watching the kids, assuming that is your actual job and you are running or working at this company as a side job. So, I care about this topic because I care about equality.

Quyen: Lori, I loved how you mentioned microaggressions. Those little things that are not so overt and obvious can sometimes be the worst because we are not acknowledging them. I think there are two main reasons why this topic is critical, especially for a woman in leadership or one looking to join the data field:

1.     In the world of data, women help drive effective solutions because we come with different perspectives and innovative ideas that lead to better decision-making. We are 50% of the population, so we represent a lot of spending power. If you think about the perspective we bring to the table, we need to delve into and take a look at that.

2.     Gender diversity in a company can help achieve better financial outcomes. It is something that creates and produces financial outcomes for companies. Looking at this from an economic standpoint and how we promote women within our field is essential. 21% are more likely to have above-average profitability if the company has women in leadership (McKinsey study).

Megan: Studies and books have been written on data bias, specifically, the biased data that excludes women. What are your thoughts on data bias as women in leadership and data?

Lori: While we are 50% of the population, we are less than 1% of the C-suite across all industries, but it is amplified for women in STEM. I believe bias, at least in the workplace, has three entry points:

·       Bias during the acquisition phase. For example, men will apply for a job with 50-60% of the required skills, whereas women, by their admission, will have 100% of the skills needed before applying for that job. The bias starts internally within ourselves to say, “am I worthy of being here?” or “am I worthy of this job?”.

·       Bias that happens during the interview process. Hiring managers or recruiters are sorting through in a traditional way and judging fit for a job. Commonly, an interview approach tends to have a different complexion. It is more about telling me what you have done and strutting your stuff, which women are often uncomfortable doing.

·       Bias that comes into play once you get into the workforce. This bias shows up in different ways, such as micro or macro, but it is also pervasive throughout the other processes.

Organizations need to be very deliberate in being able to recognize those biases and be able to create conditions where the biases will not thrive.

Quyen: Bias is ubiquitous. It is everywhere and inside all of us. It is about having awareness around that and knowing it is also in our data. There was a book written in 2019 by Caroline Criado-Perez called Invisible Women: Data Bias in a World Designed for Men. This book dives into how most of our data is gathered from men. They talk about the day-to-day things like how farm equipment is measured to the body of a man or how snowplowing in Sweden is done around the men’s schedules.

Click here to read our full blog that dives into unconscious biases, creating a space for women to thrive, the importance of a support system, and much more! 

Outsourcing Data & Analytics: Consider Nearshore

For many businesses, outsourcing data and analytics projects can be a daunting and overwhelming task. However, it can come with significant cost savings, among other benefits. The nearshore model of outsourcing data and analytics offers a unique solution that combines cost savings, access to top talent in STEM fields like data science and analytics, and North American cross-border agreement benefits.

By leveraging available talent pools in Mexico, companies can benefit from proximity to North America. This can provide an excellent advantage for many reasons:

  • Similar time zones for seamless communication and measurably faster action
  • Collaboration between teams on either side of the border
  • Cost savings

The cost-saving benefits achieved using nearshoring services are hard to ignore in today’s competitive marketplace. Outsourcing can save a company 40% or more on information technology needs. There is a reason 70% of American businesses are eyeing nearshore outsourcing

This article will discuss how nearshoring data analytics helps businesses by assessing the CAGE model, USMCA (United States-Mexico-Canada Agreement), while tapping into Mexico’s talented pool of STEM professionals.

The problems with outsourcing data analytics offshore

There are various aspects to consider outside of the availability of talent. First, look at the fragile domestic policies within Eastern European countries where offshoring has been the norm. Political instability is common in countries with a significant hub for remote work, including technology jobs. Offshoring outsourced data analytics has become even more challenging with additional political constraints. 

Next, the distance between the countries can make communication and collaboration difficult. This can significantly slow workflow, making it difficult to troubleshoot any issues that arise, especially when dealing with large or complex projects.

Finally, the time zone difference, while not as prominent a factor as the language barrier, can still create complications. Team meetings, reviews, and discussions may need to take place outside traditional business hours to accommodate both teams. 

Why nearshore outsourcing in Mexico

When working with an outsourcing partner, you want to ensure it is done right, on time, and within budget. To do this, nearshore outsourcing provides several advantages.

When outsourcing, it is important to consider the CAGE (Cultural, Administrative, Geographical, and Economic) model to assess offshore and nearshore opportunities. This can help companies identify a nearshore destination that best suits their data and analytics needs. For example, Mexico is an ideal nearshore country for American companies. This model looks at four things: Culture, Administrative rules, Geography, and Economics. 

  • Culture looks at the language people in the area speak and how they act. 
  • Administrative rules cover laws that may affect how you do business there. 
  • Geography means looking at how near or far away the place is from where you live or work and what potential impact the distance creates. 
  • Economics means considering how much it will cost to outsource your data and analytics project there. 

Protecting intellectual property

It is important to note that the USMCA (United States-Mexico-Canada Agreement), benefits the nearshore model through tight intellectual property protection laws and is inclusive of the digital economy, especially as it relates to the free movement of data across all countries. The USMCA is the renegotiation of NAFTA and regulates trade between the United States, Mexico, and Canada, including updated intellectual property protections that aligns with the United States IP laws. 

Streamlined visa procedures

The relationship between the US and Mexico bolsters the discussion of security and migration, making Mexico more appealing compared to Asia, Eastern Europe (due to the current fragile political environment), and other offshore options because of its preferential access. Work visas can be applied for and granted throughout the year, whereas H1B visas are strictly monitored and processed and only issued in October. The NAFTA Professional (TN) visa is a temporary, three-year work permit for citizens of Canada, Mexico, and the US. It can be renewed indefinitely for qualified workers from each country to reside in the US with their spouses and children. This means team collaboration can be seamless as professionals can be mobile and, if necessary, stay for extended periods based on the project’s needs. 

The bottom line

When it comes to outsourcing, nearshoring data and analytics work in Mexico poses tangible benefits. From near-identical time zones, language skills, ease of work visa and travel, cost savings, and IP protection to US standards to a large pool of skilled professions, nearshore data and analytics solutions can yield positive outcomes. Read more about these solutions here.

Generative AI and the Future of Knowledge Work

Brainstorming ideas for your next new product. Learning what to consider when designing a user interface. Summarizing the steps needed to start working with a new tool. Writing a fairytale or a poem about modern life in the style of a historical bard. These are some mundane—or wildly creative —tasks that can easily be done with generative AI.

Throughout the last year, new offerings have highlighted the potential for generative AI to change how we approach content creation drastically. Generative AI has the potential to enable knowledge workers to spend less time doing research and completing routine tasks and spend more time thinking creatively and strategically. But are we ready to let generative AI tools into our daily work? Will it carry the same problems with bias, lack of context, and concerns about inclusivity that came with the last generation of AI solutions?

In this article, we will summarize what generative AI can do and its impact on knowledge work. We’ll discuss the rollout of chatbots in search tools, highlighting some of the drawbacks and biases of generative AI. Finally, we’ll touch on its impacts on inclusion and where generative AI can go from here.

What is generative AI?

Generative artificial intelligence (AI) is a collection of technologies that create new content, including text, code, audio, images, video, and simulated data. It is an application of artificial intelligence that has been accelerated in the last few years due to investments by big tech leaders, notably by a company called OpenAI that Elon Musk, AWS, and Microsoft have funded.

Generative AI is based on a machine-learning model called the transformer. A transformer is a neural network architecture designed to understand context and sequential patterns—such as how to capture the subject of a picture, blend it naturally with its background, and style the whole thing. When combined with a language model, which predicts the words and phrases likely to follow given the previous ones, we get a chatbot. A generative model is trained on lots of data, like any other deep learning model, but then tasked to use the transformer to create outputs given prompts it has never seen before. OpenAI, Google, and other tech companies have pioneered research in this field over the last decade. In the past few years, they have applied huge amounts of computing power, training their models based on data from the internet.

Impact on knowledge work

Many with careers in information technology and analytics can be considered “knowledge workers.” This includes not only engineers, data analysts, and programmers but also web designers and systems architects. Knowledge workers spend their time researching and solving complex problems. Therefore, they need to have not only domain expertise but excellent communication skills. Generative AI has the potential to make these jobs more efficient and enable everyone to be more creative.

Instead of searching the web endlessly to understand a new idea, you can have a conversation with a chatbot to hone in on the details relevant to your task— or sate your curiosity.

Rather than scrolling through hundreds of stock images that don’t quite capture the excitement you want to convey, you can use an AI art generator to create a photorealistic scene of your users in a way that resonates with them.

As an alternative to passing marketing copy back and forth, you can ask a chatbot to edit your work. For example, it may offer ideas to expand upon, summarize a long description, check grammar, and translate your message.

Rather than creating the same code patterns over and over (called “boilerplate” code), you can use generative AI to automate even this task. You can prompt GitHub Copilot by adding code comments describing the desired logic. It suggests code to implement the solution, enabling a developer or data scientist to focus on the big picture.

We should think of generative AI as a productivity tool and a superpower that accelerates the creative side of knowledge work. As this technology gives us more power to complete nuanced tasks, we can spend more time on bigger ideas—that is, thinking strategically and innovating.

Rollout and impact on search

The compelling part about generative AI is the ease of use. You go to a web page, type in a question or task, and the technology will instantly respond. A chatbot will remember your past inputs and responses, creating context and enabling your experience to feel like a real conversation.

As a major investor in OpenAI, Microsoft has begun incorporating a ChatGPT interface into Bing. The initial response has been remarkable: the new search engine is, in many ways, a “marked improvement” over Google. Google, which has sponsored much of the foundational research in generative AI, has responded by releasing its own search chatbot, Bard. However, there is genuine concern about whether the technology is ready for the general public. Chatbots sometimes give factually incorrect answers. They seem to have their own personality and feelings. They can even be subversive, revealing dark, strange desires.

Thankfully, the perception of “personality” is something that can, with enough engineering, be tweaked. Remember that, at its core, a language model predicts the next word, phrase, or idea. So, adding the right amounts of randomness to the model allows engineers to experiment, with the results running from monotonous, to interesting and conversational, to gibberish. Microsoft has also begun limiting conversations with ChatGPT to a few interactions to avoid going down the stranger paths that have taken many people’s attention.

As for inaccuracies in chatbot answers, tech companies are also working on this front. For example, Google’s AI research lab DeepMind is working on a chatbot called Sparrow, which will cite its sources. Between these two giants’ ”arms race” on search, generative AI looks to impact how we all get information in the near future.

How does generative AI reflect diversity?

Like most technologies, generative AI is neither “good” nor “bad,” but its reputation will be formed based on its usage. One immediate benefit of generative AI is access to powerful, real-time language transcription and translation. For example, OpenAI’s Whisper listens to human speech and transcribes it in real-time. It recognizes 98 spoken languages and is robust to accents, background noise, and technical language. This means that it can be used to translate stories and wisdom from other cultures, enabling diverse voices to be heard with ease.

There are, however, legitimate concerns about how generative AI models can produce biased results, perpetuating racial and gender stereotypes. For example, in the summer of 2022, as OpenAI was preparing to launch its art generator DALL-E 2, researchers determined that it sexualized women and produced mainly black men when given negative connotations (prompted by, e.g., “man sitting in a prison cell”), and defaulted to white men in many situations.

Clearly, this is due to a need for more representation in the data used to train the models. The responsibility to make the results less biased lies with the organizations that create AI technology. This can be done by openly testing for bias and iterating as OpenAI has done. It can also be done by building teams with diversity. This is not done by achieving a “diversity score” but by finding people who bring requisite skills regardless of their backgrounds, being mindful of privilege, and fostering interest in technology roles early in a person’s career.

Where to go from here?

The current offerings of art generators, AI code tools, and chatbots are only the beginning of a new wave of AI. As these technologies mature, many other organizations will find more use cases that enable them to take advantage of generative AI in new and exciting ways. These products and services can enable more diverse audiences. Like all data products, it is incumbent on the data professionals who create them to ensure they recognize biases in training data and take responsibility for model results.

As responsible users of generative AI, we should pay attention to the prompts we input and look for implicit bias in the results these tools deliver. We should fact-check results from chatbots and not plagiarize the content they create. As programmers, we must ensure that we inspect and unit test automatically generated code before committing it. As creators, we should ensure that photos, videos, and audio produced with AI is inclusive, diverse, and representative of a broad range of cultures.

Generative AI is here today, with many tools existing and coming soon to make our work more efficient and our lives more creative.


Michael Rice, Sr. Consultant – Data Science, Swoon Consulting


Gartner’s 2022 Top Data & Analytics Trends

Finding the talented labor resources to solve the increased scarcity problem in data and analytics (D&A) is becoming mission-critical for companies around the world, according to technology research and consulting firm Gartner, as revealed in its 2022 list of the top D&A trends.

The company pinned down three key areas that need addressing overall:

  • Institutionalize Trust
  • Augment People and Decisions
  • Activate Dynamism and Diversity

The Lack of Data Literacy
Technology is again outpacing the workforce’s ability to understand and master its use. The chief deficiency comes from a lack of needed data literacy in most companies’ workforces. Employees across every industry lack the basic knowledge and practical understanding of how data is sourced and constructed, how to apply techniques and analytics to it, and how to implement AI-powered analysis into use cases, applications, and future business decisions. With Machine Learning (ML) and Artificial Intelligence (AI) already becoming significant components of most companies – 86% of CEOs report it’s already considered mainstream technology in their offices – this is no passing fad, but the definitive way competitive business functions now and in the future.

The Gap Between Technology and People to Understand the Value of Data

Gartner predicts the lack of sufficiently trained professionals to fully employ data and analysis will continue for the majority of Chief Data Officer (CDO) organizations through 2025. The belief is that companies that have become hyper-focused on technology are betting on the wrong horse. No matter how good AI is at recognizing patterns and making recommendations, companies still need talented, trained human employees to understand the value of the data to make the best decisions on how it will create ROI for the company in the future.

Gartner promotes digital learning for companies to handle the widening gap between what they are technologically capable of and what their employees are capable of. The gap is widening rapidly chiefly because of the digital aspect of installing new systems without needing new hardware. Twenty years ago, implementing a new ERP system or switching from one workflow system to another was a long slog of upgrading hardware, buying licenses, and new hardware. These steps to implement new systems and processes would allow time for employees to start training on the new workflow. With tech now deliverable instantly by the cloud, that lag time has virtually vanished, and employees are falling behind as technology moves faster and faster.

A New Working Culture and A Matter of Security, Risk, and Trust

With a lack of employees who understand the process of data analysis powered by AI also comes the risk of companies losing their customers’ trust and their ability to remain transparent in their processes. This lack of cohesion can be amplified in the ever-popular remote working model. Allowing employees to work from home was deemed mandatory during the COVID-19 pandemic, but as most countries move past those uncertain times, the push to enable workers to occupy whatever space they wish during business hours is now seeing repercussions. With employees logging in to virtual servers and cloud environments from their homes, coffee shops, airports, hotels, and every place in between, connected governance goes out the window. The hard and fast rules of how data is viewed, shared, manipulated, and utilized on-site can fall by the wayside when employees venture off the premises, making data security a primary risk.

When AI models misrepresent, particularly without adequate staff to recognize the shortcomings, the risks mushroom into bad business decisions, including those that risk lives in hospitals, government facilities, power grids, and the like. The push to achieve value from substantial investments in D&A and AI is leading some companies to cut corners on best practices in developing connected governance throughout edge environments and distributed systems.

It’s another area where a lack of fundamental knowledge of data and its usage is causing employees to make decisions without knowing what they are risking. Upskilling the workforce and obtaining highly skilled expertise that understands data protection, sharing, and usage is necessary to keep companies from risking legal trouble, violating privacy regulations, and destroying the company’s reputation, not to mention limitations on return on investment.

Contact Swoon Consulting to learn how our Nearshore Data & Analytics solutions can help solve talent scarcity and upskilling gaps.

Making an Impact, Advice from Diverse Female Leaders

Women occupy a much smaller portion of leadership within the workforce. Throughout this interview, Vidya Peters, Board Member and former COO at Marqeta, and Quyen Pham, VP at Swoon Consulting, discuss making an impact, being diverse women in leadership, and advice for others.

Fostering Gender Equality and Diversity in Big Data

Join Morgan Templar, Board Member at Swoon Consulting, and Quyen Pham, VP at Swoon Consulting, as they discuss hiring based on potential, putting hiring managers through unconscious bias training, increasing flexible working options to boost gender diversity in technology, and more.

Leading in a Hybrid Workplace

While some team members love being in the office, others work better in remote environments. That is why Quyen Pham, VP at Swoon Consulting, sat down with Anna Griffin, Chief Market Officer at Commvault, to discuss how to transition your team to hybrid work successfully.