The Human Side of Contingent Workforce Five-Star Data & Analytics

The data-driven transformation of contingent workforce management has started. In fact, a wave of new technology and different kinds of data, advanced analytics and AI have begun to pour into the contingent workforce management space. Successful organizations must learn to utilize that data to effectively navigate the complex non-employee workforce environment of the future.

Machines vs. Humans

While it may seem that the emerging digital, data-driven world will be dominated by machines and cognitive technology with humans less responsible for making decisions, the opposite is true.

The human element is indispensable and critical at different levels and in many aspects of delivering actual “data as value” to organizations. Accordingly, organizations approaching the next generation of contingent workforce data and analytics should give considerable weight to this human element and how a managed services provider may be the best path to successful adoption. (Related reading: “Using Data to Optimize Sourcing Strategy: The Recipe for Success.”)

The Human Side of Data and Analytics 

Let’s examine how people are essential and critical to achieving “data as value,” from sourcing and management of data, to its value-added application in business use cases.

Popularized coverage may have overemphasized the technical or uncertain aspects of the machine side of data and analytics at the expense of the human side. While the machine side of data and analytics does provide the workhorses for the heavy lifting and is necessary, the value of data and analytics is dependent on people’s unique human abilities in many impactful ways.

These can be broken down into four basic categories:

1. Architecture/design of data platform/ecosystem. Data and analytics solutions don’t just create themselves; they must be designed and implemented by people with the right expertise.
2. Supply and transformation of data. Data creation must be validated, augmented and processed by people in ways that machines are incapable of processing.
3. Use-case, context-based process execution. Data and advanced analytics outputs that enter into process execution must be judged by people in context.
4. Tactical/strategic consultative problem solving. Data and analytics can lead to solutions when applied by people with business expertise.

Understanding the human side of data and analytics can give managers a different perspective, as well as important insight on what to look for in their data and analytics solutions.

4 Critical Considerations for the Human Impact on Data

The human side of data and analytics can take many forms. Looking at it through the lens of the four categories can bring into focus how essential and valuable it is.

1. Architecture/design of data platform/ecosystem
   Data and analytics solutions must be architected and designed by people with appropriate technical expertise and an understanding of the solution domain. Although it requires a high degree of specialization in state-of-the-art technology and data science, it is not only a technical problem.
   
   Domain knowledge is necessary to understand what “data as value” means to business end-users and then design solutions that extend from the sourcing and collection of the necessary data to the presentation layer in a particular use case. This combination of technical and domain expertise is still rare in the contingent workforce space. Today it has been assembled together in very few places, such as at Magnit.
   
   
2. Supply and transformation of data
   Data and analytics solutions can be thought of as the endpoints where the data and analysis become valuable for business end-users in a specific use case. However, it is also a value-added production process in which people usually play a variety of roles. These can be a part of a crowdsourcing process in which individuals complete tasks to add, modify and/or verify information.
   
   These processes, sometimes referred to as human-in-the-loop, demonstrate just how embedded people actually are in the generation of valid data that can be used in different ways, from providing additional market insights (like rate intelligence from Magnit’s Skills Village), to developing data sets that can be used to train machine-learning algorithms.
   
   
3. Use-case, context-based process execution
   Organizations don’t want data or data outputs, they want “data as value” which is generated in particular use cases at specific points in processes, such as talent sourcing (check out our “Five-Star Data” white paper to learn more about leveraging data to capitalize on worker quality and savings). While machines can do the data processing heavy lifting, and data-driven algorithms can enable autonomous actions, achieving optimal actions and outcomes often requires “a human operator” to ensure data and intelligence are complemented by contextual information that has not been captured by an AI-based model.
   
   For example, while Magnit’s Direct Sourcing solution is enabled by advanced analytics to surface high-potential, matching candidates, PRO domain experts are very much involved in the process to curate the client’s talent pool and ensure alignment with customer-specific needs. 
   
   
4. Tactical/strategic consultative problem solving
   Data and analytics can be the basis of high-value solutions to intractable problems that are framed and attacked by domain experts at an executive and even a strategic level. At a tactical level, organizations may require support to select and analyze data sets and interpret them. This can be important when a standard data view or report raises questions that can only be answered with a deeper dive and additional analysis.
   
   At a strategic level, special data analysis projects and tailored prescriptive consultation — such as those conducted by Magnit’s Total Talent Intelligence strategic consulting team — can deliver significant value for organizations, such as benchmarking and optimizing or transforming/modernizing their contingent workforce management programs. (Learn more in our Contingent Labor Analysis fact sheet.)
   
   

The category of the human side of data and analytics can not only deliver high ROI to organizations, but provide a feedback loop to the architecture/design of data platforms/ecosystems, effectively creating a virtuous circle.

Organizations in the past have tended to look at their MSPs more as operational business process outsourcers that run and optimize standardized processes and less as providers of platforms for transformation (using the best processes, technology, intelligence and expertise to bring programs to higher levels of maturity and performance). The new discipline of contingent workforce data and analytics is changing that now.

Capitalizing on Contingent Workforce Data and Analytics

There are various ways organizations can try to capitalize on the emerging discipline of contingent workforce data and analytics, as described in the second part of our Five-Star Data blog series. However, few organizations will be able to pull together the considerable specialized expertise and other resources to get the most value out of it on their own. Given the complexity and inherent scale economies that are out of their reach, most organizations will need to rely on third parties.

Many factors are important in evaluating data and analytics providers and partners. For example, do they have the “technical plumbing” to deliver what they promise? Are their solutions really integrated? These are important questions to consider.

One critical dimension that may be neglected is the human side of the solution. Does the provider recognize the indispensability and importance of the human element in delivering not just data, but “data as value” to its clients? More importantly, does the provider have the full ensemble of competent, coordinated human resources, which ensures that data is not just being processed and presented by machines, but is also delivering real value in as many ways as possible?

There is a lot of new technology out there that can enable contingent workforce data and analytics, but the contingent workforce may not always be the main market focus for technological providers. Making it work and delivering business value in practice within contingent workforce management may be best accomplished by a technology- and data-savvy, modern workforce platform provider that has a deep understanding of the domain and views human enablers and experts as an integral part of a customer solution.

For more on the increasing importance of quality data within human capital management and how it can drive significant program benefits, check out our “Leveraging Five-Star Data” white paper.

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.