Over the years, the VMS technology marketplace has made important strides in meeting the growth in contingent workforce program management requirements and supporting new program services being offered. In numerous cases these contingent workforce management technologies have led the industry in its actual evolution. Because the competition in this marketplace remains fierce, innovative enhancements are arriving every day, which support the high rate of engagement of these VMSs.
But VMS technologies are not the only systems used in the comprehensive management of contingent workforce engagements. Independent contractor compliance and freelancer management systems have emerged strongly in the ecosystem, with statement-of-work management applications and HRIS/ERP systems having fundamental roles as well. Accordingly, although the VMS is appropriately the system of operating record for most CW management programs, a lot of data are now scattered across multiple corporate systems related to a comprehensive CW program management service portfolio. Some one- and two-way data integrations have enabled some seamless data-sharing between systems, but the integrations themselves are not always simple or inexpensive to implement or manage.
So, program managers are beginning to look at business intelligence and analytic platforms to provide more comprehensive visibility of CW program service data scattered across multiple corporate systems. Incidentally, this is not restricted to CW programs; many other corporate management service programs are challenged to manage data that is distributed across multiple corporate function systems. Everyone has their own discrete management system that is designed to automate and enable one’s required business process, but now, integrated sharing of data across some of these discrete systems is becoming a competitive operating requirement.
When looking to engage business intelligence and analytic platforms, there are points to consider:
- Your organization is most probably deeply involved with these types of business intelligence applications and might already be trying to create standard practices and use of these technologies. Potentially something to leverage with the internal IT function addressing the enterprisewide need to “speed to insight” reporting and overall data governance.
- BI applications come with numerous, growing capabilities with strengths and weaknesses. Some are strong in data analysis and automation, while others have stronger capabilities in the graphical display and dashboard distribution of the data.
- BI applications are getting easier to use, allowing “citizen data scientists” — such as CW program managers — to access and operate them rather than companies needing actual, degreed data scientists or IT experts. These tools are also engaging artificial intelligence and natural-language generation capabilities to drive deeper adoption of the applications. Industry research indicates that the number of citizen data scientists will exceed degreed data scientists in the next three to five years — a potential outgrowth of ease of use capabilities in business intelligence and analytic platforms.
Similar to any management-enabling technology consideration for a CW program, a lot of discovery is required for creating a business case for the use of a BI application. In the past, the concentration of data in a VMS system for a staff augmentation service did not necessarily call for the use of an ancillary BI application and the cost of implementing and using these systems was very high. CW programs’ service portfolios have expanded substantially, and although more and more program data are captured effectively in VMS systems, new CW program initiatives such as total talent acquisition, IC compliance management and use of freelance workers sourced through the human cloud are causing data discovery and analysis integration capabilities beyond the VMS system of operating record.
Note: Gartner defines a citizen data scientist as a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.