Talent Management that Taps the Semantic Cloud
By: John Rossheim, Monster Senior Contributing Writer
Large employers with complex and rapidly evolving labor needs require an integrated talent management system that will support long-term success.
Unfortunately, such a solution has been easy to imagine but nearly impossible to realize.
To that end, HR executives look to data mining and data analytics to boost the power of their workforce planning, allowing them to anticipate specific needs for learning and development programs that can fill potential talent shortages.
Knowing that recruiters can miss opportunities to fill openings internally, HR leaders also covet semantic search to identify talent -- in whatever terms it is described and wherever in the enterprise it resides -- from employee resumes to job descriptions or profiles on SharePoint sites.
“Organizations are used to looking at their own workforce data and at external data,” says Mollie Lombardi, research director of Aberdeen Group’s human capital management practice in Boston.
"Now they want to look at internal and external at the same time, and it’s a big challenge, because there’s lots of data in different formats. It gets complicated.”
This complex convergence of data is a challenge for older database searches, which categorize people into pre-determined occupations and classifications. Yet people don't fit neatly into standardized categories. In contrast, semantic search evaluates the multifaceted skills and experiences within a resume and provides instant candidate matching and analysis against the job's requirements.
Until recently, incorporating semantic search into a talent management system has been prohibitively expense, given the massive computational resources it requires.
Enter talent management -- based on semantic cloud computing.
An Integrated View of the Workforce and Labor Pool
Semantic cloud talent management is beginning to fulfill the promise of integrated workforce planning by combining rich workforce data from multiple internal and external sources, while linking with ATS systems and APIs for third-party applications.
“The world doesn’t need yet another talent-management system,” says Javid Muhammedali, senior product director for Monster's SeeMoreTM, Monster’s July 2011 entry in the nascent cloud-talent management space. “What the world needs is a cloud-based platform to take employers’ existing systems, integrate them and make them semantic-aware.”
“SeeMore helps identify talent gaps and oversupplies,” says Muhammedali, enabling employers to slice and dice a comprehensive database of their internal workforce, together with available workers in the relevant geographic area.
“It enables executives to recognize possible talent shortfalls, such as a defense contractor with a lot of engineers retiring in 5 or 10 years.”
The predictive potential of cloud-based talent management allows companies to grow their own talent to meet near-future labor needs, while boosting employee engagement. “You can look at undersupplied talents, like customer service representatives with negotiation skills, then address the shortfall with learning and development,” says Muhammedali.
Talent management that taps the cloud may be able to boost retention of key employees, and even tell you “which categories of employees and which specific employees are flight risks,” according to a Deloitte Consulting report on HR in the Cloud.
The State of Ohio AdoptsSemantic Talent Management
Multinational companies are not alone in seeking to attract and retain top talent. The State of Ohio has partnered with Monster's SeeMoreTM to improve employers’ visibility into the workforce, and vice versa.
“If we’re going to train welders, we want to be sure there are jobs for them,” says Mark Birnbrich, project director for Ohio Means Jobs, a state program. “We wanted data mining to show what jobs are actually out there in Ohio.”
Ohio’s workforce data-mining and job-search system brings together jobs posted directly to Ohio Means Jobs; it also pools resumes targeted at individuals who live or want to work in Ohio. Employers can then query the state’s workforce database in detail; job seekers can see where their skills are needed.
“We receive and fulfill requests from economic development people with needs from employers,” says Birnbrich. “I had a request from a city that wanted to know how many fork-lift operators Ohio has. I searched our database of 2.1 million resumes and within 5 minutes I sent them an email saying that there are 2,700 people with ‘fork-lift operator’ as their job title, and that 270 have Microsoft Excel experience and 420 have done inventory management.”
The state of Ohio hopes that this sort of actionable information will boost employers’ confidence that they’ll be able to field the workforce required to build their businesses -- and deliver qualified candidates -- when the time comes.
Fast, Cost-Effective Implementations
Enabled by nimble programming techniques, cloud-based talent management also has the ability to tie into more cumbersome legacy systems to make them semantic-aware. The result promises a rapid return on investment that organizations, both public and private, now typically require.
“Many SaaS and cloud computing options cost less and are faster to implement than large enterprise systems,” says the Deloitte report.
In uncertain times, this new talent-management paradigm offers valuable workforce intelligence to point the way forward.
“Cloud talent management lets you answer the question, ‘Where is the talent for where my business is going,’ taking into account skills, competencies, geographies, and so on,” says Lombardi. “You can think about how to get your workforce ready for what’s coming in 6 to 12 months. Sometimes you can’t find people fast enough, so you have to grow them internally.”