Talent Data Exchange: A Game Changer in Contingent Labor and Beyond

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We recently had the chance to speak with Jason Ezratty, the president and co-founder of Brightfield Strategies. Brightfield, already known as one of the top analytics-based consulting and advisory firms in the contingent workforce management space, has created a unique data aggregation and analytics platform, the Talent Data Exchange (TDX).

TDX appears to be a new distinct branch of the Brightfield business. Development of the platform capabilities and the platform’s commercial rollout is ongoing and has recently attracted private investment from staffing entrepreneur Gary Nelson. The TDX business model is consortium-based: Data are sourced from the consortium member organizations, which allows TDX to provide information and analytics products and services to members and other clients and partners.

In this article, we peel the onion back a little on TDX and set the stage for our subsequent slice-and-dice drill-downs (for our PRO members) on what a data platform solution like TDX can mean for workforce and services procurement.

The Importance and Growth of Workforce Analytics

According to one market research firm, “Workforce analytics is a combination of software and methodology that implements statistical models to worker-related data, allowing company leaders to develop human resource management (HRM). Workforce analytics helps to analyze the efficiency of the employees/workers in an organization. It has many applications such as identification of need for new departments and positions, physical risks to employees in specific positions, job satisfaction of the employee etc.”

According to another market research firm, “the workforce analytics market size is estimated to grow from $422.5 million in 2015 to $860.4 million by 2020, at a compound annual growth rate (CAGR) of 15.3% from 2015 to 2020,” with growth being driven by competition for talent and skills and the capability to procure and consume them with efficiency and agility.

Based on the above, the increasing importance of workforce analytics for workforce and services procurement is clear. But identification, selection and adoption of the fast-growing assortment of analytics solutions may be challenging. Workforce analytic solutions can focus strictly on employee versus contingent workforce; solutions can range from data sets and dashboards to powerful information outputs based on advanced data analytics methods and techniques.

The scope of TDX, which does provide information outputs based on advanced data analytics methods and techniques, comprehends both employee and non-employee workforce. From our standpoint, its significance lies not only in what it does, but what it suggests is possible for services procurement down the road.

A Quick Dissection of the TDX Platform

We’ve developed this visualization to describe the makeup of the TDX platform in three layers:

  • Bottom: effectively, data management
  • Middle: data attribution and structuring
  • Top: analytical modeling and insight/decisioning tools

As noted by the upward arrow on the right, end-user consumable, higher value “information artifacts” (i.e., outputs, products, etc.) are created and variously exposed for consumption at the mid and top levels.








Click to enlarge

At the bottom layer, data is collected from consortium members’ vendor management system (VMS), application tracking system (ATS) or talent management system (TMS) solutions. Ezratty told us that stringent rules and practices apply at this level — the data become the foundation for consumables in the mid and top levels. To ensure quality, for example, a prospective member’s data can be turned down if it does not meet TDX standards.

The middle layer is where advanced technologies, like natural language/semantic processing and artificial intelligence/machine learning, are applied to create data relationships and structures that can be used to support classifications, taxonomies and different analytical models and tools. One of the most important applications pertains to structuring job, work and talent categories. To date, the state-of-the-art has been position titles and position descriptions, which is kind of like using an ax instead of a scalpel to perform heart surgery.

In TDX, the approach is really to classify work-to-be-done in relation to specific skill and capabilities attributes. This allows end-users to get beyond the vagaries of position titles and establish normalized taxonomies that can support more specific engagements of talent, as well as various kinds of analysis of costs. Ezratty indicated that the middle layer of TDX was perhaps the platform kernel and differentiator that sets TDX apart from other workforce analytics solutions. Here TDX creates data structures that can be consumed and used by clients and partners to establish their own taxonomies and develop their own analytical applications.

“What’s more important than specific taxonomies is the mechanism that generates the taxonomy...an ongoing, dynamic, evolving taxonomy,” Ezratty told us.

Finally, the top layer of TDX, supported by the two layers beneath, is where analytical models and insight/decisioning tools can be created by members, partners (VMS, ATS, TMS), clients or by TDX-Brightfield for clients. (Ezratty emphasized that this is not the core business model of TDX, which is really based in the unique value created in the mid and bottom layers.) This multi-level architecture is what really helps differentiate TDX as more of a true platform versus just a hosted business intelligence toolset and related set of information services.

One consumable of TDX is a rate benchmarking service called Labor Basket Analyzer, which allows blinded rate comparisons across the companies participating in the TDX Contingent Workforce Analytics platform consortium. More importantly, it allows a participant to drill into some of the causal factors surrounding what’s driving the rate, and to not only seek shorter-term opportunities but also explore how they model skills, competencies and job descriptions in the first place.

One insight/decisioning tool that TDX offers as a consumable called Job Variances, a tool that allows an end-user to start with a rather broad job definition (say software developer), then drill down into common, more specific variants of that role that have been identified algorithmically. In this way, a more specific configuration of work attributes can be used for hiring purposes or for more effective analysis.

Is the Game Changing? We Think Yes

Here’s the big thing to consider: This is not just some cool little AI plugin that a consulting firm is bolting onto a benchmarking service it’s trying to establish in the market. This is much more profound. What we’re talking about here is a new battleground for insights and intelligence based on big data rather than a feature/function war for VMS tools and tactical MSP services wrapped around them. We’re talking about diving much more deeply into what is truly driving supply markets (in this case labor markets) based on much richer spend/supply analytics. We are training “machines” (software) to perform analytics on our behalf to spot opportunities that we don’t have the time or budget to find.

In a recent paper we published (which is free and downloadable here), we said:

“Intelligent supply analytics are those that are predictive and increasingly rely on either sophisticated machine learning techniques or expert refined data models built on verified and validated rules to uncover deeper data patterns and relationships that can yield intelligence and insight.”

A provider that can build an ecosystem of partners around its intelligence platform will create huge value for the market and for itself. And it can be disruptive. Such is the potential of a TDX. But its emergence in the workforce management space brings up important questions for businesses in that space to consider:

  • How does this solution compare to benchmarking services available from large VMS and MSP providers?
  • Should more VMS and MSP providers partner with Brightfield, or should those providers similarly invest in this level of platform, and does it make sense for the industry?
  • What types of players in or outside workforce industry may be positioned and resourced to also create such a platform? Should some of the pieces of the platform be open source, and if so, which?
  • How should buyer organizations make best use of this platform and service to deliver short-term measurable financial benefits and longer-term strategic benefits?
  • Does this intelligent technology support broader spend analysis and management beyond just contingent labor (answer: yes), and in which areas? And how big of a deal is it really?

We will address some of these and other related questions as part of our ongoing coverage of analytics and intelligence in the procurement, talent management and supply chain realms, so as always, stay tuned!

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