Image of a person working on their laptop representing the extended workforce.

Flextrack and Brightfield – Real-Time Market Data and the Extended Workforce

Enterprise people strategies are increasingly reliant on the extended workforce to ensure access to talent in a challenging market. According to recent estimates, the non-employee workforce accounts for approximately 35% to 47% of the average company’s total workforce in America.[1][2] In addition, about 90% of business leaders realize that freelance, crowdsourcing, on-demand and external platforms play a significant role when it comes to ensuring long-term competitive advantages.[3]


Flextrack’s Platform-as-a-Service architecture enables customers to take full advantage of the growing ecosystem of extended workforce solutions and tools to design a custom program that suits their unique needs. One of the ecosystem partners Flextrack brings to customers is Brightfield, a leading workforce intelligence platform. Through our partnership with Brightfield, Flextrack customers can access real-time market data directly in their workflows to ensure the most precise rates for in-demand skills as well as the availability of those skills in their markets. Flextrack customers can now stay ahead of a fast-moving market for skills and more precisely plan to access the talent with those skills as needed.


Real-time Market Data and Workforce Planning at the Skills Level


A fast-moving business world requires organizations to rely on the most up-to-date workforce data, rather than annual salary surveys or backwards-looking analytics. The increasing reliance on independent contractors, gig workers and temporary employees to meet the need for sought-after skills makes real-time market data even more important for hiring, project and team leaders managing budgets and timelines. In this type of environment, annual salary surveys quickly become out of date and cannot capture fluctuations in rates – especially when considering the market rates and availability of specific skills rather than broader job titles.


“Why wait to know that something was going to take three weeks, when you can have that intelligence ahead of time and better set your expectations around the right talent?”  — Jason Ezratty, Co-founder, Chairman and Chief Data Scientist at Brightfield


This increased granularity of data, including real transactional market data for specific skills, provides talent and team leaders with more actionable decision intelligence to access precisely the skills they need without overpaying or waiting too long due to a price mismatch. For example, an organization may have dozens or even hundreds of senior software developers with diverse skills, while annual salary surveys might aggregate and average tens of thousands of developers at a specific level of experience based on title and broad role descriptions. By looking at the cost and availability of specific skills through transactional market data, rather than at titles in a salary survey, these leaders can make more accurate and timely decisions regarding their talent as well as staff their projects more effectively.


Machine Learning and Granular Data


The granular data available to Flextrack customers through our partnership with Brightfield is made possible through machine learning. At its core machine learning uses algorithms – logical processes for solving problems – to process massive quantities of data to identify patterns, relationships and anomalies to inform decisions by human users. The value of machine learning is in its ability to process almost limitless dimensions of data and the speed at which it can process them.


In the staffing context, machine learning can classify unstructured, granular data about the work and the workforce to help with decision intelligence. For example, machine learning can process large numbers of job descriptions, again let’s say with a software developer title, to classify the type of software developer and the specific skills within those role descriptions given the increasing complexity and specialization of the role as it evolves. Machine learning can also determine the classification of a worker, such as an SOW worker, an FTE, or an independent contractor.


Machine learning can also process a tremendous number of variables associated with that data to determine what is normal, or expected, given those variables and to predict how combinations of those variables might lead to specific relationships or outcomes between them. Using the software developer example, machine learning might help a talent or hiring manager predict the market rate for a specific collection of skills in a specific market or geography, along with the availability of those skills in the market.


Asking the Right Questions of the Data to Solve Business Problems


One of the beauties of machine learning and of the real-time market data it can provide through platforms like Brightfield is the relative ease of application for the day-to-day consumer of this data, such as a workforce program leader or project hiring manager. Rather than becoming a data scientist or an expert on people analytics, day-to-day consumers can focus their attention on refining and clarifying the questions they ask of the data. In other words, hiring and project managers should work to ensure that their questions directly address the problems they are trying to solve in the business before going to the data.


A direct application of this in workforce planning is in scenario planning. Hiring managers might ask themselves, and the data, “Given the specific skills I need for a project, would it be better to meet these needs through an extended workforce staff augmentation effort or should try to hire FTE’s?” By combining and analyzing real transactional market data from Brightfield around the contingent workforce with data from an applicant tracking system or ATS, possible through Flextrack’s PaaS solution, customers can inform their decisions around rates, availability, predicted time to fill and even retention for both scenarios.


Towards a Unified, Total Workforce Strategy


This is just one example of how real transactional market data can help promote a more unified, total workforce when combined with other sources of data and the right business questions. So, in addition to delivering data and insights for “in the flow” decisioning during the requisition process, Flextrack’s extended workforce solution can help talent and project leaders make the best decisions for their teams during the planning process as well.


If you are interested in knowing more about how Flextrack and real-time market data from Brightfield can help you make faster, more accurate decisions around needed skills and help unify your extended and FTE workforce strategies, contact us directly.


[1] The US Gig Economy, 2021 Edition, Tony Gregoire and Timothy Landhuis, Staffing Industry Analysts, 2021.

[2] “The Extended Workforce Is a Cornerstone of The Future of Work,” Christopher Dwyer, The Future of Work Exchange, January 26, 2022.

[3]Building the on-demand workforce. Fuller, J., Raman, M., Bailey A., Vaduganathan N., et al. Harvard Business School and BCG, 2020. 


Jeff Mike

Jeff Mike

Head of Research, Flextrack

Jeff Mike works closely with HR, Procurement and IT leaders to design extended workforce ecosystems that fuel and future-proof enterprise talent strategies. Jeff brings over 15 years of experience leading HR functions, along with five years leading global HR- and workforce-related research, to combine the best thought leadership, business practices, and platform technology into purpose-built solutions.

Jeff Mike