The pandemic initiated unseen challenges for businesses all over the world and accelerated the digital transformation.
These changes brought a tide of demands for more sophisticated, powerful tools and security measures like AI-powered solutions and robust antivirus systems. But the exponentially growing needs are now significantly outpacing supply, as the pool of developers is drastically drying out.
Our guest emphasizes that scarcity of talent is today's biggest obstacle to AI innovation. Therefore, to discuss what challenges AI solutions are yet to overcome and how this field might evolve in the near future, we invited Israel Niezen, CEO of Factored – a company specializing in machine learning, data engineering & data analytics.
Would you like to share what the journey has been like for Factored?
We solve a very widespread and important problem in a growing industry, which is the sourcing, vetting, and upskilling of specialized talent in data science and AI. This technology is set to change how we live and conduct business, so having highly skilled and efficient talent to help businesses achieve their goals is crucial. We help address the acute shortage of AI and data science talent globally. We help ambitious companies build world-class AI, machine learning, data analytics, and data engineering teams faster and more cost-effectively.
Factored’s journey has been incredibly arduous and rewarding. We have seen tremendous growth in a short space of time but we are conscious of how we are achieving our growth to make sure it’s truly sustainable. We want to be around for a while and have never lost track of what is truly important, and that is our people. The pandemic was obviously a scary moment for many businesses but we have seen Factored succeed so far thanks to the remarkable passion and talent of our teammates.
What is the biggest obstacle to AI innovation and adoption today in your opinion and what are ways to solve these challenges?
There is no doubt that the exponential growth of data from multiple sources and our much-improved capacity to process such massive volumes of data at very high speeds and reduced costs have led to great interest in AI from businesses and other institutions.
The promise is clear: by deploying AI, machine learning, and data science applications, businesses can glean important learnings to improve products, optimize marketing, automate operations, improve customer experience, reduce churn, and much more. AI and machine learning truly have the power to help businesses reimagine processes and provide revolutionary new solutions and outcomes by tapping into newly discovered opportunities and efficiencies for the business.
While the potential for AI is very promising, we believe the biggest obstacle to AI innovation and adoption today is undoubtedly the scarcity of talent. Demand from companies looking to build their data science teams is growing very quickly and is significantly outpacing supply. This isn’t just a problem in the U.S., it is a problem globally. Without experienced data scientists to convert great ideas into real-world solutions, innovation is stunted.
In today’s market, companies need to accept that AI is already fueling their competition. To keep pace, organizations need to look beyond traditional ways of attracting talent. The good news is that the pandemic has also revolutionized the way we work and has democratized access to talent. Remote work has allowed businesses to look beyond their immediate geographic areas and hire anywhere great talent resides. This is where Factored can help by augmenting teams with brilliant, quality, highly vetted, and tested data scientists and machine learning engineers in your time zone. Some of the biggest tech companies in Silicon Valley have already discovered the benefits of distributed teams.
In your opinion, which industries would greatly benefit from deploying machine learning?
Most industries that can produce and track data can benefit enormously from deploying machine learning techniques and data-driven solutions. At Factored, we have most commonly deployed machine learning projects in industries such as financial services, healthcare, manufacturing, telecom, logistics, and retail.
There are also benefits that span all industries such as marketing and advertising insights (such as how someone might engage with or react to a particular piece of content, customer acquisition optimization, LTV analysis), and customer service optimization (better categorizing and routing customer queries to ensure they’re resolved more quickly, customer churn prediction, etc) which can be automated at scale using machine learning.
What are some of the most common challenges businesses run into when it comes to realizing successful AI projects?
There are three main challenges when executing successful AI projects: Availability of Data, Deployment, and Interpretability.
AI projects are still relatively new in most organizations. When undertaking such investments leaders have to consider infrastructure, data, risk, and governance. It is also important to note that to properly execute AI projects, they have to be efficiently integrated into the business at large. Consider process infrastructure and ensure that supporting processes are automated wherever possible. Otherwise, your initiative will fall short of the expected outcome.
If you think of AI as a machine, then data is the oil that enables the machine to run. Without data, AI and machine learning would be difficult to integrate with most business scenarios. And data is only truly useful when it is properly collected, managed, and organized. Even now, when we generate more data than ever before and we are drowning in a sea of information, there is still a shortage of clear, usable data to solve several problems using AI. Such examples would be defect detection in manufacturing, anomaly detection in machinery, or fraud detection in the world of finance to mention a few.
Some machine learning models, especially deep learning models, are powerful but extremely difficult to interpret. They work mostly as black boxes that do a certain job but the interpretability of these models can be challenging. Interpretability is an important issue in certain sectors, such as fintech, retail, and marketing.
How did the recent global events affect your field of work? Were there any new challenges you had to adapt to?
Pre-covid, some companies still believed that data scientists and data engineers should work on location, as this was thought to be the best way to keep data safe. Covid accelerated the acceptance of remote work and the adoption of remote engineering teams. After a few months of uncertainty when the pandemic first broke, our business was able to work with companies who wanted to move fast in taking advantage of the great benefits of nearshore talent in their same time zone.
The current geopolitical situation, like the unfortunate war in Ukraine, is igniting another wave of changes, particularly as it relates to the increased focus on Latin American talent given its time zone proximity to the U.S., and its relative stability economically and politically as of recent decades. Many companies who used to source talent mostly in India and Eastern Europe are now looking at their neighbors down south for talent.
What are some of the worst mistakes companies make when handling large amounts of data?
Not organizing or curating their data properly and not having a data strategy are two of the more fatal mistakes we have seen companies make repeatedly. That is, not including considerations like data engineering best practices to make the most out of their datasets.
It’s never a good idea to invest a huge amount of time and effort in AI solutions without having the former problems solved. In other words, you absolutely need to have your data in order before you go about creating solutions that use that data if you want any meaningful business outcome.
What predictions do you have for the future of AI technology?
I could answer this question by painting a colorful picture of all the cool solutions that will rest on AI technology in various industrial applications. But I won’t go that far. Instead, let’s focus on some of the fundamentals first that are essential to the success of AI.
The future of AI rests not on huge quantities of data but rather on the quality of the data we use. In other words, AI technology is really only as effective as the quality of data that it runs on. In addition to quality, speed is also a factor for efficacy. Effectively storing, managing, and querying data requires automated processes, as well as a fully integrated, harmonious solution between technology infrastructure and the business at large.
Therefore, data-centric AI is going to be a major focus in the coming years. It is already rising to the forefront. We are seeing this, especially in the increased demand for data engineers and analytics engineers in the market. People are recognizing that data scientists with such skill sets play a crucial role in preparing and structuring data. The rise and demand for data quality and data observability tools is proof of this trend.
I’m predicting that the AI industry will continue to place greater emphasis on the quality of data used to create models. Solving for quality and speed should become a prerequisite. This echoes the thoughts of our Founding Adviser, Andrew Ng, who believes that the AI industry is great at building models but now needs to focus more on data and key aspects of data management such as data labeling, data quality, observability, and error analysis.
Would you like to share what’s next for Factored?
Factored will continue our relentless focus on building very high-caliber data science, machine learning, data engineering, and data analytics teams for the world’s most technologically ambitious companies.
We will also continue to leverage advanced AI and machine learning in our own business operations, to learn as much as we can about the data science talent market, and to better match the best-fit engineers to the right opportunities. We will also expand into building products that make building and deploying data science and data engineering solutions much easier and more cost-effective, leveraging key learnings from the many projects and workflows we facilitate every year on behalf of many companies.
Finally, we hope to do all of the above while growing our team sustainably, with a view to accelerating their careers and making a significant impact in the U.S., Latin America, and other communities where our business might expand. Even though our business is AI, we know that humans are still paramount, and we take our responsibilities as humans, and for humans, very seriously.