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Women comprise 56% of the US workforce, but hold only 26% of technology jobs. The percentage of female STEM, or science, technology, engineering, and math graduates, is about 19%. According to the National Center for Women and Information Technology (NCWIT), this number is continuously decreasing.

Research shows that gender-diverse teams bring in more business and improve creative outcomes when working in diversity-rich environments. With organizations digitizing their operations since the pandemic, there is a greater need for technical qualifications for all employees, regardless of gender.

A significant challenge facing women in the tech industry is the lack of role models. Because of the low representation of women in leadership positions, it is often difficult for them to advance their careers and achieve their goals.

In fact, unfair treatment is a primary reason why women leave their technical jobs at a 45% higher rate than men. Plus, according to the Kapoor Center’s Tech Leavers study, women of color face unfair treatment at even higher rates.

US Army veteran Tiffany Pilgrim, a Barbadian American who founded tech public relations firm Coralini PR, wants to change that imbalanced equation. Pilgrim is also a user experience designer and researcher who is adamant about the importance of women becoming a more disruptive force in the tech sector to help fuel design and innovation.

“We are seeing trends about women in entrepreneurship and technology right now. Entrepreneurship has been booming since the pandemic, and the trend towards women in tech has been very slow,” Pilgrim told TechNewsWorld.

She predicts that women will become a more noticeable disruptor in the tech sector over the next few years.

Diversification opportunities started early

After moving with her parents to New York when she was 16 years old from the Caribbean island where she was born, Pilgrim served as a motor transport operator in the US Army at the age of 18.

When his army service ended, he focused on diversifying his knowledge and skill set. First, as a classically trained actress, she then trained in fine arts and design. Then she perfected her communication talent at a global social media agency based in London.

Pilgrim manages top tier billion-dollar brands such as T-Mobile and DAZN, an international sports streaming platform. Prior to founding Coralini PR, she went into the TV and entertainment business as a producer and communications specialist, working with celebrities and Fortune 500 companies, such as Showtime (CBS) and Paramount (formerly ViacomCBS).

Tiffany Pilgrim Coralini Pr.  is the founder and CEO of

US Army veteran Tiffany Pilgrim is the founder and CEO of Coralini PR.


“I began to realize that I wanted to start my own technical PR firm. After all, technology and public relations were my backgrounds,” he said of his long stint working for a marketing agency.

Having accumulated more than 10 years of communication experience, Pilgrim puts together its skill set to help startup companies get to know their products better. She helped them with their marketing and brand image.

As a technology evangelist running her own public relations firm, she works with top leaders and startups to build their brands and social media outreach.

This was one of the main pain points for new companies. She explained that when they launched their products, they did not know how to convey the features and benefits of the product to the consumers.

“My firm merges technology and PR to solve entrepreneurs’ pain points,” she offered.

tying the elephant in the room

Pilgrims dedicates herself to helping other women start their technical careers. They are often approached by women entering the tech sector as a startup or doing a technical job for another company. Pilgrims are generous with their time, promoting their interests and answering their questions.

“I always attend Zoom sessions with another woman who needs to pick my brains about technology,” she said.

Some of that advice is needed to keep women in their tech jobs rather than avoid the partisan treatment they often face. Pilgrims do not hesitate to discuss the gender barriers they faced while starting.

The Pilgrims admit to dealing with a lot of pushback in the military and beyond. For example, her first duty assignment after basic training was at a military base in Colorado, where she was the only female soldier in the motor pool.

“It was a shock to me and the men as well. I had to prove my worth as a woman in a men’s motor pool. It was something I dealt with a lot. It was a challenge when I was there,” He gave advice.

“Yeah, of course, that’s what happens in the tech industry,” she said. “Of course, I’ve faced obstacles. You can’t avoid it when you’re a multicultural woman.”

Addressing the lack of role models

Pilgrim helps tech entrepreneurs make a name for new and emerging technologies. But his career has an additional goal that involves a broader outlook for those new to the technical line of business.

She continues to mentor and encourage women to succeed in their tech careers. To that end, Pilgrim helps fellow veterans enter the tech industry, just as it did.

To do this, she uses organizational skills learned in the military as well as strategies derived from her communication and design mastery. When she returned to New York about 10 years ago, Pilgrim began her career in communication with social media clients.

“I had no formal training in communication. I just fell into it because my first job was previously with an agency in London. I taught myself User Experience Design. I worked for a while in Hollywood with clients and their media image,” she said.

According to Pilgrim, women are creating a good stir right now when it comes to corporate leadership and technology.

“Many of these women are paving the way for those who follow them. These situations are real for many women, I must say, “she observed. “I believe that women are designing a new world right now.”:

Advice for Female Veterinarians Interested in Tech

Over the years, Pilgrim has been actively involved with a non-profit organization, Veterans in Media and Entertainment. Membership totals approximately 5,000 veterans across the US focused on advancing media and entertainment. According to Pilgrim, he wants to pursue a career in media or be on a film set.

“I actually had a lot of mentors, and I still give advice to veterans who want to get into the media,” she said. I feel now that I am being heard, this is a great platform to attract women who need mentorship.”

Pilgrim recommends that any female veteran interested in starting a tech career needs to do some research about what types of jobs would be a good fit. Also, they need to assess their skill set. What are they able to do or learn? If that person can’t learn from self-study, go to an immersive tech boot camp.

Other options include taking out a loan, obtaining a payment plan or education grant, or investigating technical training programs that offer veteran discounts.

The career training path that Pilgrim followed was to obtain technical certifications for self-study programs. For example, he is certified by Adobe in Photoshop, Illustrator and InDesign. This qualified him to become a visual design specialist after completing all three programs and learning to use software tools.

The cost of cleaning up data is often beyond the comfort zone of businesses full of potentially dirty data. This paves the way for reliable and compliant corporate data flows.

According to Kyle Kirwan, co-founder and CEO of data observability platform BigEye, few companies have the resources needed to develop tools for challenges such as large-scale data observability. As a result, many companies are essentially going blind, reacting when something goes wrong instead of continually addressing data quality.

A data trust provides a legal framework for the management of shared data. It promotes cooperation through common rules for data protection, confidentiality and confidentiality; and enables organizations to securely connect their data sources to a shared repository of data.

Bigeye brings together data engineers, analysts, scientists and stakeholders to build trust in data. Its platform helps companies create SLAs for monitoring and anomaly detection and ensuring data quality and reliable pipelines.

With full API access, a user-friendly interface, and automated yet flexible customization, data teams can monitor quality, consistently detect and resolve issues, and ensure that each be able to rely on user data.

uber data experience

Two early members of the data team at Uber — Kirvan and bigeye co-founder and CTO Egor Gryznov — set out to use what they learned to build Uber’s scale to build easy-to-deploy SaaS tools for data engineers. prepared for.

Kiran was one of Uber’s first data scientists and the first metadata product manager. Gryaznov was a staff-level engineer who managed Uber’s Vertica data warehouse and developed a number of internal data engineering tools and frameworks.

He realized that his team was building tools to manage Uber’s vast data lake and the thousands of internal data users available to most data engineering teams.

Automatically monitoring and detecting reliability issues within thousands of tables in a data warehouse is no easy task. Companies like Instacart, Udacity, Docker, and Clubhouse use Bigeye to make their analysis and machine learning work consistently.

a growing area

Founding Bigeye in 2019, he recognized the growing problem of enterprises deploying data in operations workflows, machine learning-powered products and services, and high-ROI use cases such as strategic analysis and business intelligence-driven decision-making.

The data observability space saw several entrants in 2021. Bigeye differentiates itself from that pack by giving users the ability to automatically assess customer data quality with over 70 unique data quality metrics.

These metrics are trained with thousands of different anomaly detection models to ensure data quality problems – even the most difficult to detect – are ahead of data engineers ever. Do not increase

Last year, data observability burst onto the scene, with at least ten data observability startups announcing significant funding rounds.

Kirwan predicted that this year, data observation will become a priority for data teams as they seek to balance the demand for managing complex platforms with the need to ensure data quality and pipeline reliability.

solution rundown

Bigeye’s data platform is no longer in beta. Some enterprise-grade features are still on the roadmap, such as full role-based access control. But others, such as SSO and in-VPC deployment, are available today.

The app is closed source, and hence proprietary models are used for anomaly detection. Bigeye is a big fan of open-source alternatives, but decided to develop one on its own to achieve internally set performance goals.

Machine learning is used in a few key places to bring a unique mix of metrics to each table in a customer’s connected data sources. Anomaly detection models are trained on each of those metrics to detect abnormal behavior.

Built-in three features in late 2021 automatically detect and alert data quality issues and enable data quality SLAs.

The first, deltas, makes it easy to compare and validate multiple versions of any dataset.

Issues, second, brings together multiple alerts at the same time with valuable context about related issues. This makes it easier to document past improvements and speed up proposals.

Third, the dashboard provides a holistic view of the health of the data, helps identify data quality hotspots, close gaps in monitoring coverage, and measures a team’s improvement in reliability.

eyeball data warehouse

TechNewsWorld spoke with Kirwan to uncover some of the complexities of his company’s data sniffing platform, which provides data scientists.

TechNewsWorld: What makes Bigeye’s approach innovative or cutting edge?

Kyle Kiran Bigey Co-Founder and CEO
Kyle Kiran, BigEye Co-Founder and CEO

Kyle Kiran: Data observation requires a consistent and thorough knowledge of what is happening inside all the tables and pipelines in your data stack. It is similar to SRE [site reliability engineering] And DevOps teams use applications and infrastructure to work round the clock. But it has been repurposed for the world of data engineering and data science.

While data quality and data reliability have been an issue for decades, data applications are now important in how many major businesses run; Because any loss of data, outage, or degradation can quickly result in loss of revenue and customers.

Without data observability, data dealers must continually react to data quality issues and entanglements as they go about using the data. A better solution is to proactively identify the problems and fix the root causes.

How does trust affect data?

Ray: Often, problems are discovered by stakeholders such as executives who do not trust their often broken dashboards. Or users get confusing results from in-product machine learning models. Data engineers can better get ahead of problems and prevent business impact if they are alerted enough.

How does this concept differ from similar sounding technologies like Integrated Data Management?

Ray: Data observability is a core function within data operations (think: data management). Many customers look for best-of-breed solutions for each task within data operations. This is why technologies like Snowflake, FiveTran, Airflow and DBT are exploding in popularity. Each is considered an important part of the “modern data stack” rather than a one-size-fits-none solution.

Data Overview, Data SLA, ETL [extract, transform, load] Code version control, data pipeline testing, and other techniques must be used to keep modern data pipelines working smoothly. Just like how high-performance software engineers and DevOps teams use their collaborative technologies.

What role do data pipelines and dataops play with data visibility?

Ray: Data Observability is closely related to the emerging practice of DataOps and Data Reliability Engineering. DataOps refers to the broad set of operational challenges that data platform owners will face. Data Reliability Engineering is a part, but only part, of Data Ops, just as Site Reliability Engineering is related but does not include all DevOps.

Data security can benefit from data observation, as it can be used to identify unexpected changes in query volume on different tables or changes in the behavior of ETL pipelines. However, data observation by itself will not be a complete data protection solution.

What challenges does this technology face?

Ray: These challenges include issues such as data discovery and governance, cost tracking and management, and access control. It also includes how to handle queries, dashboards, and the growing number of ML features and models.

Reliability and uptime are certainly challenges many DevOps teams are responsible for. But they are also often charged for other aspects such as developer velocity and security reasons. Within these two areas, data overview enables data teams to know whether their data and data pipeline are error free.

What are the challenges of implementing and maintaining data observability technology?

Ray: Effective data observability systems must be integrated into the workflows of the data team. This enables them to continuously respond to data issues and focus on growing their data platform rather than putting out data fires. However, poorly tuned data observability systems can result in a flood of false positives.

An effective data system should perform more maintenance than just testing for data quality issues by automatically adapting to changes in the business. A poorly optimized data observation system, however, may not be accurate for changes in business or more accurate for changes in business that require manual tuning, which can be time-consuming.

Data observability can also be taxing on a data warehouse if not optimized properly. Bigeye teams have experience in optimizing large-scale data observation capability to ensure that the platform does not impact data warehouse performance.