From Data to Intelligence: The New Frontier for Workforce and Services Procurement Technology Solutions and Practitioners

cognitive computing agsandrew/Adobe Stock

Most large enterprise workforce and services procurement programs rely on a vendor management system (VMS) as their primary technology tool or solution. One of the many valuable things that VMS has done for these organizations is to provide a window into data — that is, provide “visibility.”

Whether in traditional reports, operational dashboards or data sets that can queried, manipulated and visualized, the model has been to transform structured data into information that businesses use as a basis of judgment or action. Such uses would include gaining insight, making decisions and executing tactically or strategically. In effect, technology provides managers with the information they need to manage — for example, to monitor operations, to analyze performance or to plan.

Over the past several decades, the long-standing paradigm, in which data is transformed into actionable information for business users, has in the aggregate produced inestimable value for organizations and people. But over the past 10 years, new kinds of technologies (hard and soft) have given rise to an extraordinary new paradigm in which software acts upon data and performs many “cognitive” activities without the involvement of human users or complements/enhances human activities and frees humans to apply their unique cognitive capacities.  Based on the experience to date of adopting businesses, this second paradigm looks like the next enormous wave of data and analytics value for businesses.

While many other business areas have already begun to adopt the technologies and applications of the new paradigm, for the most part, contingent workforce management and procurement are only at the threshold of adoption.

From Data to Intelligence

Over several decades, data analytics — and its application in business — has evolved through a number of steps, as shown below.

Analytics EvolutionClick to enlarge


The first three steps in the evolution of data analytics are likely well-known to most business managers.

Data Management is foundational — the storing and organization of data. Data management has continued its evolution from hierarchical databases to today’s world of big data, unstructured data and data lakes; new data management platforms, such as NoSQL and Hadoop; and data connectivity through middleware and PaaS and iPaaS APIs.

Descriptive Analytics is most often tactically focused on monitoring, assessing and making decisions about ongoing operations. Descriptive analytics came to be decades ago as recurring, printed reports from mainframes (“canned reports” with short shelf lives) and eventually evolved into the sophisticated, user-configurable real-time dashboards of today.

Exploratory Analytics allows a person to “interrogate” data to find relevant insights. Exploratory analytics started with queries of relational databases and evolved to include tools (like OLAP) and more recently suites of advanced tools (like Tableau) for interrogating almost any type of source data and representing the resulting information in different ways, including thought-provoking visualizations.

Within the first three steps above, we see the arc from data management to what is commonly referred to as business intelligence (BI). But BI, as such, is really just information — actionable information — something similar to the “intelligence” that the CIA collects and acts upon. Data is transformed into coherent, contextually relevant information that allows business managers and other users to understand what is happening in their world and formulate appropriate actions for the present or the future.

But after these first three steps, the paradigm shifts based on a different set of technologies (mainly software) that include artificial intelligence (AI), natural language processing (NLP), neural nets and machine learning. These technologies and their applications, which have been increasingly adopted into some sectors over the past 10–15 years, have taken two main forms:

  • Predictive Analytics: If a,b,c, then x will happen
  • Prescriptive Analytics: If x,y,z, then do n

There are at least two distinguishing features of this new paradigm that go beyond BI (i.e., actionable information) to “active intelligence”:

  • Rather than data being transformed simply into information that humans can choose to act on, in the new paradigm, software acquires structured and unstructured data to identify patterns, select information, make inferences and judgments, learn and even trigger actions. As such, active intelligence can substitute in places where humans are not the best agents and can make possible the valued-added application of uniquely human cognitive capacities that always outperform machine-driven ones (e.g., judgments and decisions that must be based on qualitative inputs or where there is a rich, dynamic context that must be assessed)
  • Can be woven into other software processes to either reduce and replace human involvement or, perhaps more importantly, allow humans to now add value in ways they are uniquely suited to.

Returning to the contingent workforce management context, one example of the above would be a case where a “learning machine” (machine learning software) is processing massive amounts of data, including job roles, bill rates, geographical locations and any other kind of data to predict or forecast spend on particular labor categories six months from now. Similar processing of the software may tell a manager that the bill rate should be adjusted (or the software may simply change the rate itself and perhaps initiate some other action). In either case, the manager is freed to perform other higher level activities.

While data management, descriptive analytics and exploratory analytics have created inestimable value for organizations and people, the new paradigm of data analytics (now being looked at as a part of the emerging paradigm of “cognitive computing”) promises to do the same — and on a much greater scale.

From Data to Intelligence in Contingent Workforce Procurement

As mentioned at the outset: While many other business segments have already begun to adopt the technologies and applications of the new paradigm, for the most part, contingent workforce management is only at the threshold of adoption. However, adoption is not only possible or likely — it is inevitable. The questions are how will adoption occur and how fast? In whatever way that happens, it is clearly on the horizon, as indicated by The Hackett Group chart published in "The CPO Agenda: 2016 Procurement Key Issues."

Adoption by contingent workforce programs will be mainly driven by technology providers bringing the new technology and “intelligent” applications into the mix. There are a number of possibilities for this happening, but it is likely that the most beneficial scenario for businesses is one where VMS providers begin to deliver the technologies and “intelligent” applications on top of the core technology platform that contingent workforce programs rely on. But not all VMS providers will have the capability or make the investment to pull this off.

How fast adoption will happen will be based on two factors: (1) how fast organizations see the value and begin to find the ways to adopt (something which will separate leaders from laggards) and (2) how fast contingent workforce technology providers — effectively VMS — make these technologies available for adoption. Where there is a coincidence of the two, adoption will quickly follow.

It will therefore be necessary for contingent workforce procurement programs to assess what VMS providers can “deliver” — in short, which ones are already down the path of making these technologies and “intelligent” applications available to clients. The information contained in this article could be the basis for starting a screening process for evaluating providers.

The benefits of adoption are many, ranging from performance improvement in cost savings to mitigating the problem of skill gaps that will become more prevalent over time, both across entire organizations and specifically in procurement. Future leaders will start to take the steps leading from data to information tools and finally to intelligence, while laggards will be left behind at a lower rung.

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