Thứ Tư, 27 tháng 7, 2016
Top 10 Ways to Find Your Career Path
If you're not sure which direction your career should go in, you're thinking about making a career change, or you just want to feel more fulfilled in your career, these ten tips might help.
Ideally, everyone would know their true calling early in life and find happiness in their work, but it often doesn't work that way. One survey (of New York professionals) found that they expected to change careers three times in their lifetimes; lifelong careers may not be the norm any more.
That said, we know there are better ways to choose a career than just following your parents' footsteps or choosing randomly. Here are some ideas.
10. Think About What Excites and Energizes You
This one's the first obvious step—we all want to enjoy and actually like our careers. (Perhaps the biggest sign you're on the wrong path is if you dread talking about your job.) While passion isn't the only requirement for being content in your career, many would say it's still essential, if only because passion is what keeps you going even through the tough times. Is there a job you would do job for free?
9. But Also Keep in Mind What You're Good At
Maybe you don't feel that passionate about any specific career—or you love multiple areas and can't decide on just one. Then it's time to think about your personality and focus on the skills you have. "Don't do what you love. Do what you are."
8. Take a Test
Well, you say, what if you don't know what you're good at or even what you're interested in? Career assessment tests in college or even high school help narrow down a field (perhaps with the Myers-Briggs personality index), but if it's been a while since you took those tests, there are other kinds of assessment tests you can take. This one from Rasmussen College matches your self-reported skills and interests with potential jobs. (And they also have a salary and job growth interactive chart.) For potential programmers, Switch recommends a coding career based on your preferences. About.Com's Job Search site has a collection of other career tests.
You can also find a career that fits your motivational focus with this assessment test.
7. Try an Internship
If you have flexibility when it comes to salary, an internship could be a great way to test out an industry or type of career—and eventually get a full-time job (especially if you have no prior experience). Even if it doesn't turn into a job or you find out it's the wrong career for you, an internship can help build your network—from which you can get career and job advice. (Not all internships are just about picking up coffee. For example, Google internships, while hard to come by, put you to real work.)
6. Find a Mentor
A mentor could help you take your career to the next level and give you the insider insight to help you make sure you're on the right path. Here's how to ask someone to be your mentor.
If there's a career you're interested in, you might also check to see if any companies or people in that line of work would let you shadow them for a few days to see what it's really like.
5. Explore Unconventional Careers
We all know the popular careers available to us—doctor, lawyer, teacher, computer engineer, police officer, store owner, etc. If you feel uninspired by the typical choices, know that there are thousands of unusual jobs you might not have heard about, hidden, perhaps, in the Bureau of Labor Statistics' Occupational Handbook. Mashable has a list of six dream jobs that pay well (panda caretaker! Chocolate inspector!), Thought Catalog highlights 10 more (sex toy testers?!), and Chron lists a couple of others (along with related articles like "Unusual careers with animals" and "unusual accounting careers").
4. Ask Other People
Perhaps the best way to discover a new career is to ask other people about theirs—assuming you come into contact with people who don't all work in the same field. Your LinkedIn network (or other social media sites, but especially LinkedIn) might be a good place to start mining for information. Also, don't forget your local library's reference librarian can point you to career resources.
3. Use the G+P+V Formula
The perfect career for you would most likely fit the G+P+V formula, which stands for Gifts + Passions + Values. Consider your strengths and passions, as we've noted above, and your values—what's nonnegotiable about the way you work?
2. Make a Career Plan
As with most things, your career will benefit if you have goals and a plan for it. Maybe you think you want to be a writer, but the next step after that, is editing. (Do you really want to do that?) Or maybe you want to transition from being an editor to a restaurant owner. (How are you going to get there?) Map out where you want to go, with concrete milestones, as if it were a four-phase project.
1. See Your Career as a Set of Stepping Stones, Not a Linear Path
Of course, all these plans and ideas are never set in stone. Your career is a marathon, not a sprint and it can turn out to be a very winding road indeed, knitted together from all of your experiences into, hopefully, a career worth having.
Photos from VoodooDot (Shutterstock), OpenClips (Pixabay), Mopic (Shutterstock), sacks08, auremar (Shutterstock), Little Birth, bobsfever.
Teaching Machines to Think About HR
For all its promise for HR, big data and its “machine-learning” component still only give us facts about, and factual relationships within, our workforces; not conclusions based on the statistical analyses HR has always needed—and always will—to make meaningful predictions.
By Peter Cappelli
Workforce analytics. Big data. Machine learning.
The above terms—or “buzzwords” if you don’t like them—are popping up in many discussions of human resources, mainly involving vendors with solutions that make use of new data aimed at answering traditional workforce questions. Is there anything really new in these approaches, and, if there is, should we be paying attention to it?
The answers are yes and yes.
Let’s start with the term workforce analytics. In some ways, this term is to traditional measures of outcomes as human resources was to personnel. It is about addressing traditional, evergreen questions in different, more sophisticated ways.
Workforce analytics describes an effort to use data and sophisticated analyses to address HR problems. The most topical ones at the moment are: “Which candidates will make the best hires?” and “Which employees are most likely to leave?”
There is nothing new about those questions, and there isn’t much new about how they are being approached. The novelty comes, in part, from the fact that, after the early 1980s, big corporations gave up trying to address these questions in a sophisticated manner, so most people in business aren’t aware that similar approaches were tried a generation or so ago.
But there are some differences. One is a greater interest in analyses pertaining to financial outcomes: e.G., It will save us $5,000 per employee by reducing turnover.
The second difference involves the type of data available. The “Manplan” program in the 1960s required HR staff to read information about an employee from one file, mechanically punch it onto a card, then get different information for that same employee from another file, punch it onto a different card, then do that for every employee they wanted to study. Only when those steps were completed could they start looking to see what factors predicted turnover. It cost a fortune to look at even a small set of employees.
Virtually all HR data now is kept electronically, and, in most companies, the information on every applicant who tried to get a job with them is sitting somewhere in a dataset. It’s much easier and cheaper to look at huge numbers of observations, which makes it much easier to find potentially useful results. Being able to capture every “hit” on your employee-benefits website, for example, can tell us almost instantly what kinds of employees are worried about what types of issues.
But here’s the brake to the Big Data bandwagon: Not all HR data is big. The key piece of information needed to make workforce analytics valuable is a measure of job performance. We can’t say anything about which of our 1 million applicants will do well without being able to identify who among our employees is a good performer. In most companies, that information is no better than it was in the 1950s, and, in many companies, it is actually worse, as we’ve gone from assessment-center scores to a supervisor’s guess about potential. The phrase “garbage in/garbage out” is highly relevant here.
That takes us to the last and most obtuse of the buzzwords: machine learning.
It is a different way to think about data than most of us have previously seen, one that came from people whose expertise was rooted in computers rather than statistics per se. The “learning” idea comes from the fact that computer programs (i.E., The “machine”) can be designed to look at data and find patterns that allow them to make predictions.
How exactly machine learning differs from statistics is a topic of endless fascination to people in those two fields, but for the rest of us, here’s what matters: The traditional, statistical approach to analyzing HR data begins with hypotheses that come from prior research. It includes careful statements about assumptions in the study, or studies, and whether those assumptions are true. Traditional machine learning, in contrast, is theory-free and assumption-free. It just looks for patterns in the data, and it uses different techniques from what had commonly been used in statistics to find the clearest patterns.
A statistical examination of whether a given employment test predicts good hires concludes with either “yes” or “no,” where "no" means we can’t rule out that the relationships were due to chance.
In the case of a machine-learning examination, an employee might instead conclude that, while there is no overall relationship, there is a very powerful relationship for this subset of employees, nothing much for that subset, and, for a third subset, a strong relationship that was different than that of the first group.
The power of machine learning comes from the fact that it might well find important predictors that we never thought of before because prior theory didn’t include them—e.G., The distance an applicant’s home is from the work location predicts turnover—and wasn’t particularly adept at “mining” through lots of seemingly unrelated data to find predictors.
All that sounds really promising for machine learning, but there are a bunch of things about this approach that we’d better consider pretty carefully before diving in.
The first of these is a reminder that machine learning produces facts, rather than conclusions. It tells us “X is related to Y,” but not why they are related. Without hypothesis testing and clear statements of assumptions, we don’t learn much about what a given relationship means or why it exists. Perhaps most important, machine learning can’t tell us much about the likelihood that a relationship observed in this dataset will be useful in another context.
This matters in HR because most of the frameworks that support modern employment—especially legal frameworks—relied on the scientific method and traditional statistical tests for their foundation.
Consider, for example, the legal norm that selection tests should not discriminate and the “Uniform Guidelines of Employee Selection Procedures” put together by psychologists over the past generation to ensure that hiring practices are both valid and don’t discriminate against protected groups. Machine learning, as traditionally practiced, would surely uncover relationships that, if applied to hiring, would violate the law.
Machine learning applied to big data will certainly turn up a lot of interesting facts for workforce analytics to ponder. Transferring those facts to practice, however, is still a big leap. On their own, these “buzzword” approaches won’t get us there.
Peter Cappelli is the George W. Taylor Professor of Management and director of the Center for Human Resources at The Wharton School of the University of Pennsylvania in Philadelphia. His latest book is Why Good People Can't Get Jobs: The Skills Gap and What Companies Can Do About It.