66
Projected age of retirement
for current workers.


"facticious"
Data that is false or fabricated.

gold
The best data scientists turn
distilled information into pure gold.

butter
Too much churn and
companies lose the cream.

guatemala
Guatemala has the largest CW
compared to population in Americas.

1 in 3
# of working Americans in
the contingent workforce.


"jobsolete"
As some jobs become out of date,
others emerge.

rope
In a conformity string, we call attributes
that impact cost and availability of
qualified job candidates "pieces of work".

2%
Projected growth office/clerical
staffing 2013.

44%
Companies implementing proper
measures during offboarding.

singapore
Singapore was world's top CW
productivity market 2014.

male
Data Scientist: the most wanted
job by employers on LinkedIn
in 2014.

belgium
Belgium has the highest tax burden in EU.

247:10k
Ratio of robots to employees in Korea,
highest level in the world.

36%
Employers who find paying
freelancers cumbersome.

stars
The big star in our universe is Data Centauri.

40
% of American workforce projected
to be freelance by 2020.

crystalball
Predictive analysis is only as
insightful as the analysts.

sugar2
Data should never be sugar coded.

bow
A good strategy stretches without
changing its basic shape.

19.6wks
Average length of unemployment
of managerial candidates.

17m
# of workers with tenuous
ties to employers.

37
% of senior HR officers identifying
talent management as top HR issue.

questions


To find answers, we formulate questions.
Then question the questions.

< 20
% of private sector workers receiving
employer sponsored health insurance
by 2025.

16%
CW population at average
large company.

france
France has the highest
tax burden in EMEA.

70
% of Fortune 100 who’ve
implemented a VMS.

-1.5m
Shortage of US managers able to
analyze big data and make decisions
based on findings.

£2.6b
Amount NHS spends on
temp staffing.

shamrock
Independent contractors can
be reclassified by Irish courts.

CWS 3.0: October 16, 2013

By Kay Colson

Big data is everywhere. Innovative technologists are introducing new solutions and tools daily, enticing us to “cut through the big data clutter” and “access timely and accurate insights.”

It all sounds exciting and important … the business of planning, recruiting and hiring the right workforce being driven by meaningful data!

Recently Pinstripe, a recruitment process outsourcing provider, suggested “The true sign that analytics have arrived in the HR software space is the presence of visual reporting tools, customizable data relationships and cross-platform data integrations in nearly every product. Software vendors clearly understand how important analytics are to HR, and they are building analytics functions into their products. It remains to be seen how the execution holds up, but understanding the need for tools is an important first step.” No doubt analytics have arrived, but what does that mean to the average recruiting manager? For starters, it means that you need to define what business intelligence you need and the data that drives it.

There are four types of business intelligence that can aid recruiting. All are powerful and should be considered as we put big data to work to improve recruiting. To be successful, your external or internal recruiting team must be actively pursuing all four.

Performance. Most commonly used for service-level agreements and performance plans, this category includes time-to-fill, candidate and slate quality, hire retention, service delivery quality, and many more.

Retrospective. We don’t use these as well as we could, but they are very important as key performance indicators, which help us diagnose delivery failure before it happens. Think about best sources as well as ratios of hires to candidates presented, interviews, offers, etc. Effective workforce planning brings knowledge and forward planning to the recruiting process and is highly dependent on good historical data. We need more work on tracking where best candidates come from, i.e., schools, competitors, geographical regions, etc.; how many candidates it ideally takes to make a fill; and why qualified people decide to join. We would also benefit by looking back on successful hires and the characteristics that contributed to their success.

Trends. With global requirements increasing, program managers need to understand current trends in each market where they hire; the market changes quickly, so tracking must be ongoing. Compensation trends alone require constant diligence to ensure you are always considering market rates. Following and understanding trends in open, held and cancelled requisitions, as well as fills, is the best way to know what is ahead in workload.

Predictive. Now here’s the meat — and something we have not yet mastered. We should be exploring what indicators show that a candidate is more, or less, likely to succeed in your organization or move upward a step on the food chain, and what characteristics are likely to contribute to exemplary performance. Does this sound like data we can only gather in the interview and screening process? With big data, that’s changing.

A final word to the wise: Be sure you are comparing apples to apples … location, skill sets and much more can all affect your business intelligence. Data integrity is critical. Thoughtful interpretation turns data into business intelligence.

View on the Staffing Industry Analysts website