Data-Driven Transformation of Contingent Workforce Management

In an environment of ongoing change and heightened uncertainty, organizations are looking to better manage their entire non-employee, external workforces as a path to higher levels of agility, efficiency, competitive advantage and financial performance. To accomplish this, organizations will have to leverage “data at scale” and “advanced analytics” in a vast number of ways.

But as noted in Part 1 of this series, accomplishing this is hardly a trivial undertaking, and it requires resources and capabilities well beyond the capacity of most organizations. Consequently, new approaches and solutions are needed. (Related reading: “Leveraging Five-Star Data.”)

The End of an Era

Organizations now need to and can leverage, consume and derive value from data like never before. But the decades of well-established, standardized practices and technology for contingent workforce sourcing and management have not set the stage for this to happen.

In particular, contingent workforce management (CWM) programs in the past have tended to rely almost entirely on VMS technology for their data and business intelligence. But this has meant mostly operational program data presented ex-post in the form of VMS-based reports, filtered views, near-real-time dashboards, and BI outputs. Only the truly advanced programs were ingesting global market data, evaluating behavioral analytics, or considering how data could help them analyze the implications of pay parity for full-time employees versus contingent workers.

VMS providers have begun to introduce context-based, decision-support information into some workflows, but even that is typically at an early stage. At the same time, some VMS providers have branded themselves as platforms for managing extended workforces. But pure-play technology VMS providers largely remain niche, process-centric software point solutions.

These VMS solutions still emphasize operational data, with other types of data looked at as an add-on. And operational data, even if it is extracted from multiple VMSs, does not solve the whole problem (which is structural). The real problem for organizations today is finding a way to integrate multiple sources of operational program and market data and advanced analytics capabilities (i.e. AI, ML, etc.) to deliver use-case-targeted solutions for client organizations, suppliers and others.

The solution requires a different approach.

The Data-Driven Transformation of Contingent Workforce Management (CWM) Has Started 

After many years of gradual evolution and lots of incremental changes in how organizations manage their narrowly defined contingent workforce or broadly defined extended workforce, a more transformational change seems to be occurring. The force behind this is the maturation and growing business applications of data-at-scale and advanced data analytics in many industries; and the CWM space is primed to follow.

An increasing number of organizations and CWM industry intermediaries now appear to be aligned to embrace a different future. That is a transition from siloed, process-centric, single-system contingent workforce management to data-centric, integration-platform-based enablement of externally sourced work/services.

This emerging approach:

• Remains focused on the financial performance and risk mitigation objectives of the contingent workforce management model, but it must address those requirements within a broader context of more worker types, new sourcing channels, services/outcome management, et al.
• Leverages built-for-purpose digital technology, data and analytics, and integrations that are not overlays or after-the-fact inserts to automated business processes, but rather their foundation.
• Provides additional up-to-date market-driven capabilities (e.g., sourcing becomes more like data- and analytics-driven talent acquisition and engagement, similarly to the state-of-the-art in recruiting and engaging permanent hires.
• Continues to integrate domain and technical expert-based services to ensure organizations have superior client support, program management and workforce management consulting.

Organizations must now come to grips with how they will move beyond CWM into this data-driven future.

Benefits of a Data-Centric Integration Platform

Today, to make the leap to the data-centric, organizations have three options:

• Take a “Do-it-Yourself” Approach: Organization develops its own, in-house, proprietary data and analytics solution based on its own expertise and/or consultants, existing and required new technology, new relationships with workforce data suppliers and analytics solution providers.
• Outsource to Multiple Solution Providers: Organization relies on a number of providers (VMS, MSP, rate benchmarking, etc.) to partner, cooperate and somehow source and integrate data and other capabilities across its systems and processes to deliver a comprehensive data and analytics solution.
• Partner with Holistic, Data-centric, Integrated Platform Provider: Organization engages with a platform provider that leverages its own digital ecosystem of data suppliers and analytics capabilities providers, its purpose-built technology foundation and its own employed expertise to develop and deliver data-driven customer solutions.

These options are compared and evaluated as follows:

1. “Do-it-yourself” is an approach that is only feasible for a very limited number of advanced, well-resourced organizations.
2. Outsourcing to multiple Solution Providers may seem to offer an easy transition from where an organization is today to leveraging additional data and analytics and services. But this approach has a number of problems:
   • VMS not designed as a full-blown data platform, so a provider would have much to build.
   • Core data sets, processes, solutions, services are not integrated/managed on a single orchestrating platform.
   • Working across multiple providers/partners to deliver a customer solution to an organization means potential for:
     • Operational and customer-support/experience misalignment, gaps.
     •  Incompatible processes, technology frameworks and systems.
     •  Sub-optimal integration of technology and data across providers.
     •  Slow consensus-based solution design decisions and outcomes.
   • Future business planning and investments must also be based on consensus among entities and can hamper market responsiveness.
3. A holistic, data-centric, integrated platform provider may represent an unfamiliar alternative for some but is the best option for organizations committed to a complete, long-range data-centric approach to optimize the performance of their external work/services:
   • The main point of a platform model is the integration and management of core assets and capabilities to build and provide access to value-added, flexible and economic solutions for platform users/customers, promoted by:
     •   Single business strategy/operationalization
     •   Purpose-built technology/data architecture
     •   Solution and digital ecosystem extensibility
     •   Superior economies of scale and scope
     •   Unified, omnipoint customer experience
   • A platform model gives rise to and passes other significant benefits to customer organizations and end-users, including but not limited to:
     •  Five-star data (including single source of truth; holistic design-driven curation and management of data sources).
     •  Unified design, development and integration of domain-specific, tech-enabled, data-centric solutions (leaving room for open innovation with digital ecosystem members).
     •  No conflicting priorities, as well as a controlled environment (as opposed to trying to introduce multiple technology/service partners that need to figure out how to work together).
     •  High degree of flexibility in configuring and bundling/unbundling solution customer solutions.
   • A platform-based solution stack provides integration of a human service layer with underlying technology, data, analytics, enabling:
     •  Unification of all client-facing products and services, including customer support (efficient problem solving, consistent feel and UI of products).
     •  “Human in the loop” program management and domain expertise across customer-specific use-cases and processes (e.g., talent curators in contingent direct sourcing).
     •  Broad range of different data-based management consulting services, ranging from tactical to strategic (e.g., implementing dynamic, data-driven rate cards; developing an AI-driven skills taxonomy; program performance and maturity, external integrated workforce planning/optimization).

A platform-based approach offers organizations many of the benefits of a traditional single-vendor solution approach (e.g. in terms of solution implementation, training, support). But it offers much more. Engaging a provider of a holistic, data-centric integration platform is the best option for organizations to arrive at the data-centric capabilities needed to optimize the performance of their external work/services. What to Look for in a Data-Centric Integration Platform Provider

The Shift to a Data-Centric Integration Platform Strategy 

The industry shift from siloed, process-centric, single-system contingent workforce management to integrated, data-centric, platform-based enablement of external work/services is underway. Organizations must find and engage the best partner with the best approach that can support them through their own transformations to the data-driven, post-CWM world of external, contracted work and services.

Such a provider must be materially committed to a data-centric integration platform strategy that is currently being executed by:

1. Establishing the requisite at-scale data assets, data management and data science analytics capabilities.
2. Deploying a technology foundation that already enables integration with a curated set of data sources and ecosystem partner capabilities.
3. Developing a range of domain-specific data/analytics-driven solutions for client organizations and other consumers (such as suppliers, et al).
4. Engaging the talent, expertise and know-how across requisite disciplines (e.g., technology, data science, customer support, domain specialist, solution strategy, et al.).
5. Orienting to offer organizations a world-class, omnipoint customer experience and one-stop-shop for bundled and unbundled, proprietary and third-party solutions.

In this time of transformation and transition, the stakes are high. If an organization cannot check all these boxes, it will know it should continue looking for the platform provider that fits the bill.

For more on the benefits of a holistic platform approach, download “8 Advantages of an Integrated Workforce Management Platform.”

If you’re interested in learning more about how Magnit is helping organizations implement winning contingent workforce programs globally, please contact a Magnit representative at info@magnitglobal.com.

Disclaimer: The content in this blog post is for informational purposes only and cannot be construed as specific legal advice or as a substitute for legal advice. The blog post reflects the opinion of Magnit and is not to be construed as legal solutions and positions. Contact an attorney for specific advice and guidance for specific issues or questions.