By now, if you follow this blog or any other web site that deals with contingent labor or other staffing-related topics, you’ve heard of the War for Talent (as an aside, it’s important to note that the use of the preposition “for” is quite important here as a War ON Talent is a different thing entirely and more appropriately discussed in another forum!). I also bet that you understand that, if you’re in a position responsible for ensuring that your organization gets “the right butts in the right seats at the right time, cost, and quality,” the War for Talent means that you need to employ a wide variety of techniques to achieve success and win the war.
Contingent Bill Rates are Steady Despite Record Temp Employment
Unemployment drops below 6%
The September BLS Employment Situation Release was welcomed with relief for its marked improvement over the weak August report. Posting 248,000 new jobs, the September results helped drive the unemployment rate to 5.9%, the first time that measure has been less than 6% since July 2008. The pronounced upward revision of new job estimates for July and August by 31,000 and 39,000, respectively was also encouraging.
However, the news wasn’t all good. The percentage of the US working age population participating in the workforce fell to 62.7%, a low level not seen since 1977. The unemployment rate fell both because net new jobs were created, but also because the number of people dropping out of the workforce continues to grow.
The average work week was up 0.1 hours to 34.6 hours, while private sector average wages dropped a penny to $24.53.
New Jobs Created in Diverse Industry Segments
Employment grew across many different industries, led by Professional & Business Services (which includes Temp Services). The return to school can be seen in the Education & Health growth. The Hospitality and Health segments remained strong, while Manufacturing was notably subdued. The public sector contributed 12,000 net new jobs, continuing a late recovery in the public sector. Continue reading
Big data represents a transformational change for creating new value from data in all sorts of settings, including workforce management. At IQN Labs, we’re building out a big data system that will drive better hiring processes based on the most advanced data management and analytic techniques. We call it Big Talent.
Complementing human insight with machine intelligence
The idea of big data has been criticized as mostly marketing hype, promoted by vendors hoping to sell you expensive tools your company doesn’t really need. Some data scientists consider it worse than hype, suggesting that big data is stupid data. The claim there is that insights will be found in right size data sets (often small), not big data sets.
But big data doesn’t replace insight-oriented small data projects; it complements them and often builds upon their findings and approaches. And big data isn’t about spending more money or buying fancy proprietary tools – it’s about building out systems that can (relatively) effortlessly scale out as more and more data becomes available without huge expenditures of money.
Spotting an epidemic
So you can understand how big data accelerates our ability to extract value from data, let’s leave the contingent workforce management space for now and consider a different domain altogether: that of infectious disease tracking. Healthmap, a big data system developed by researchers at Boston Children’s Hospital, recently made the news by raising the alert about a mystery hemorrhagic fever in southeastern Guinea nine days before the World Health Organization announced the Ebola outbreak in West Africa.
Map of Ebola outbreak as of September 23, 2014
Healthmap scrapes the web to find local and international news reports, government reports, physicians’ social networks, and other freely available sources that might have information about outbreaks. This unstructured text data is classified and analyzed then combined with geographic data to generate near-real-time tracking and alerts of disease outbreaks. This provides important information to public health officials, NGO staff, travelers, and anyone else with access to a web browser.
Healthmap doesn’t replace the careful and detailed studies that public health epidemiologists and statisticians carry out; it augments human insight generated by such studies with machine intelligence that can be delivered to a much wider audience than a CDC report, for example, might reach.
Human insight vs machine intelligence
Small data analyses designed to generate insight typically require highly trained data analysts to make sense of the data including carefully considering questions of correlation vs causation, understanding the data set in detail, and producing careful reports with detailed charts to communicate complex results. Such analyses can be supported by traditional business intelligence systems that offer ad hoc reporting and charting, such as that built into the IQNavigator application.
Big data systems take a different approach. Instead of offering ad hoc reporting and charting of carefully cleaned and structured transactional data, big data systems such as Healthmap typically do the following:
- Store and analyze very granular, event-oriented data
- Integrate data from multiple structured and unstructured sources
- Leverage machine learning algorithms to generate machine intelligence
- Rely upon free, open-source software to achieve cheap scalability
- Generate right-time alerts, predictions, and recommendations
- Provide those alerts, predictions, and recommendations to frontline decision makers, not just high level researchers or executives
The following table compares typical characteristics of small data approaches to the big data paradigm.
|Small Data||Big Data|
|Output||Insight in reports generated by humans||Alerts, predictions, suggestions, automated decisions generated by machine intelligence|
|Data set||Samples of structured data extracted and carefully cleaned by hand||Integrated from multiple structured and unstructured data sources|
|Data management||Data warehouses often implemented using relational SQL databases||Hadoop, NoSQL, other open source options|
|Analytical tools||Proprietary statistical tools such as SAS, SPSS, or Stata||Open source languages such as R, Python, or Java|
|Results||Provided to high-level decision makers||Provided to rank and file decision makers|
Big data applied to contingent workforce management
Our Big Talent system will integrate data from our vendor management system with additional outside data to enrich our analyses and machine learning capabilities. It will support the quick and agile exploration of data in small data insight-oriented projects such as the analysis of supplier submission limits we have in process while providing an infrastructure for big data style alerts, predictions, and recommendations.
Big Talent will eventually raise alerts, for example, around supplier performance or rate noncompliance, make predictions around time to fill or rate trends for different job titles, and generate recommendations such as suggested job titles to use for a given job description or resources that match a particular requisition.
Big Talent is a system for IQN Labs’ internal use but IQNavigator users will benefit as machine intelligence generated by Big Talent improves the VMS and thereby the hiring processes on an ongoing basis.
IQN Labs brings data-driven innovation to hiring
IQN Labs has the mission of driving game changing innovation in workforce management through collaborative projects with clients and partners. We think Big Talent will be a key part of delivering on this mission.
Stay tuned for more about how we’re innovating with data!
With globalization, shifts in workforce demographics and the shortage of critical skills, it is no surprise that organizations are rapidly embracing a more flexible workforce. In fact, nearly 27% of an average organization’s workforce is expected to be comprised of contingent labor by 2015.
Although a flexible workforce can help organizations reduce costs, close talent gaps, and navigate change, developing a strategy around the use of contingent labor has become increasingly complex. In order to maximize the value of contingent labor and plan for future workforce needs, organizations must adopt a more systematic approach to the way they manage and engage this critical talent pool. Organizations have a lot to consider and one issue that remains a key focus is SOW-based projects and services.
It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong. – Richard P. Feynman
We’re big fans of experimentation at IQN Labs, and not just because we consider ourselves scientists – data scientists, that is. Experiments are a great way to learn how the world works. We can use them to improve our contingent workforce management practices that, in turn, improve your business results!
Experimentation is not just for ivory tower academics. Web companies regularly undertake experiments (sometimes calling it A/B testing) to improve purchase rates or user engagement or process efficiency or customer satisfaction. They experiment because it generates good evidence about what works and what doesn’t. Experiments could also be a useful tool for figuring out what works to improve contractor hiring. They’ll allow us to move beyond best practices (which may just represent a common opinion) to evidence-based practices. Continue reading