How a Dynamic Data Ecosystem will Foster the New Age of AI and Machine Learning | Webinar Recap
January 12, 2023
Table Of Contents
We recently hostedwith Amazon Web Services spotlighting the possibilities of data-driven AI/ML solutions. Below are some highlights and insights from the webinar.
If you like what you read here, we encourage you to.
AI and Machine Learning (AI/ML) is a major part of the next frontier of business, and has created massive buzz within virtually every industry to automate away menial tasks and work smarter with data and intelligent technology.
, 48% percent of businesses surveyed report already deploying (or near plans to deploy) AI/ML technologies in their business. However, many still struggle with making the technology a part of their standard operations. Gartner also predicts some turbulence as we reach the “stabilization stage” of these technologies sometime near year 2025.
Also, the economic downturn will call for smarter business strategy utilizing AI/ML technologies.
We believe that the success of AI solutions is dependent on highly-efficient data solutions like People Data Labs, and the maturation of AI can’t exist without robust data environments such as AWS’ Data Exchange.
AI/ML Technologies Are Still in the Discovery Phase, but Have Huge Potential
We asked our attendees to detail what goal(s) they wanted to address from attending the webinar. The responses were intriguing, and kicked of our first points of discussion:
Discover next-generation use cases for AI/ML using third-party data: 46%
Create data-driven AI/ML applications to sell as a B2B or B2C offering: 37%
Implement an internal AI/ML solution using PDL on the AWS Data Exchange: 37%
Form a better understanding of accessing PDL on AWS’ Data Exchange so I can integrate with other tools on the AWS Data Exchange: 29%
These results lightly indicate that innovation with AI/ML is still in the discovery phase, as people do not yet know all of the things that are possible with these technologies. People and businesses are looking for ideas for next steps, use cases, and swift implementation using external tools and third-party data. Implementing a full AI/ML solution within a business requires a vast amount of data and technology resources, so companies are looking to outsource work where possible vs. creating and managing everything internally.
Data is Now Eating the World
In 2011, Marc Andreeson famously wrote, “software is eating the world.” Since then, we’ve seen industries transform and companies fold in response to companies like Amazon, Netflix, Airbnb, and more. While Marc’s statement is still very much true, we argue the extremely high demand for data that fuels software and technologies like AI/ML has shifted this to “data is eating the world.” The exponential accumulation of data to enable innovative solutions has created a beast entirely of its own. Now, data is everywhere, and it is the most valuable commodity. We’ve seen feedback where people state, “both are eating the world”, to which we ask, “what is modern software without quality data”?
The most successful companies on Earth have set their sight on improving their data process, powering their business through massive data streams and AI/ML technologies. One great, recent example is the rise of innovations like GPT-3 that are pushing the boundaries of what machines are capable of. We're living through a data-powered revolution.
How People Data Labs Powers AI/ML Innovation
At PDL, we offer more than 3 billion person profiles, 22 million company profiles, 150 fields (including resumes, contact information, social channels, demographics, and more. All of this is delivered to our users via APIs and flat files that are tailored to specific use cases.
Machine learning models are only as good as the data that they consume.is used across multiple verticals, including talent acquisition, sales and marketing, investment insights, fraud prevention, and many more. This makes our products extremely robust and flexible for any use case where an AI/ML solution needs data.
In our webinar, we go into detail on this with examples in HR Tech and investment research. But, here’s some points we discussed where our data helps in various industries:
With our resume data set (which includes >700M person profiles) you can use ML algorithms to find common patterns in employees’ skill sets and broaden your search.
Our time series data (e.g., growth rates via company insights) provides fantastic model fuel.
Our data helps companies pinpoint employees coming from previously successful businesses (top next and top former fields), demonstrated periods of rapid growth (even at a young age), and employee retention (low rates of churn).
How Using People Data Labs on the AWS Data Exchange Improves AI/ML Innovation
AWS recognized that industry leaders across the world's most influential companies are using unprecedented volumes of data available today to innovate and grow their organizations. As a result, People Data Labs and AWS formed a partnership to lead innovation with data via a robust ecosystem of data and solution providers.
addresses some tough hurdles:
Forming one place to find data and integrate it with other third-party data providers or combine it with existing first-party data
Managing various subscriptions while building upon a current system
Delivering solutions to customers in an easy format
Lowering cost and becoming more agile to innovate faster
PDL and AWS hold a similar belief that exchanging data should be streamlined through simplification and democratization. Using data on the cloud should be as easy as adding items to a shopping cart when online shopping.
There are 2 main avenues where we are witnessing the AWS Data Exchange improving the data flow for providers like PDL:
Ease of native integration with existing databases, data warehouses, machine learning, and tools such as SageMaker. Data is always encrypted at rest and in transit and is integrated with identity and access management.
Creation of a singular place to exchange public and/or private data. Users can migrate existing data they have already purchased at non additional cost. This consolidates all of the contracts and billing into one bill.
Therefore, with this setup, users now have one place to find and discover any type of third-party data. That data can then be analyzed quickly without wasting time on integration, cleaning, and quality assurance. As soon as new data is available, users have automatic access.
In our webinar, we dive into some company examples where we see this happening today, as well as take you through a step-by-step process of subscribing to and interacting with our dataset. If you were intrigued by what you read here,to get more insight and information, or if you would like a free consultation.