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    Entries in work (104)

    Thursday
    Mar122015

    CHART OF THE DAY: The decline of employer provided training

    Today's installment of the wildly popular CHART OF THE DAY series offers a selection from some light reading that you can perhaps spend some time with this coming weekend, the 300+ page long 2015 Economic Report of the President

    Nestled on page 147 of this tome, is the below chart - a look at trends in Employer-provided training and on-the-job training opportunities for the US labor force from the period 1996 - 2008 (the latest year this data was available). As always, take a look at the chart, then some witty, wry, and as always FREE commentary from me.

    The Chart:

    As you can see from the data, both employer paid for and on-the-job training activity, as reported by workers, were both on the decline from 1996 to 2008. And even with 'old' data from 2008, it seems pretty defensible to argue the ensuing few years, the tail end of the recession and the ensuing years of halting economic recovery, that trends and declines in employer paid for training would not have reversed themselves.

    So, what do we make if this data? Here goes....

    1. No one has time or much tolerance for onboarding new people who have to be 'taught' very much, at least taught more general, and transferable from one employer to another type skills. Every job ad you see for say an Accounting Manager just about demands that the person actually already be an Accounting Manager to be considered to get hired as an Accounting Manager.

    2. When employers perceive workers to have fewer attractive options outside the organization, the pressure or impetus to invest in upskilling and employee career development tails off. While this is a pretty obvious conclusion, it does not diminish its significance. By 2008 firms, often by financial necessity, had backed way off training and development. That is a short term strategy and decision that can have much greater than expected consequences once times start to improve.

    3. Employee training continues to be 'someone else's problem' for many employers. It still is really easy for organizations to demand fully trained and capable candidates for any role prior to hiring, as the fears of costs of training become sunk if and when the employee leaves present a high burden for proponents of more employer provided training to overcome.

    4. As an employee, you remain, invariably, on your own. Keep yourself ready, keep current, be willing to pay for it yourself, since fewer and fewer employers are willing to invest in you.

    Ack, that was kind of cynical. Sorry.

    Happy Thursday.

    Thursday
    Feb192015

    A feature that Email should steal from the DMV

    In New York State, and I suspect in other places as well, when you visit a Department of Motor Vehicle (DMV) office to get a new license, register your sailing vessel, or try to convince the nice bureaucrats that you did in fact pay those old parking ticket fines, there is generally a two-step process for obtaining services.

    You first enter the office and wait in line to be triaged by a DMV rep, and once he/she determines the nature of your inquiry, you receive a little paper ticket by which you are assigned a customer number, and an estimated waiting time until you will be called by the next DMV agent. You then commence waiting until your number is announced and you can complete your business. 

    That little bit of information, the estimated wait time, is the aspect of the DMV experience that I think has tons of potential for in other areas, most notably in Email communications. The DMV estimates your wait time, (I imagine), in a really simplistic manner. It is a function of the number of customers waiting ahead of you, the number of DMV agents available, and the average transaction time for each customer to be served. Simple math, and probably is pretty accurate most of the time.

    The Email version of the 'Estimated Wait Time' function would be used to auto-reply to every (or selected) incoming email messages with a 'Estimated Response Time' that would provide the emailer with information about how long they should expect to wait before receiving a reply. 

    How would this work, i.e., what would the 'Estimated Response Time' algorithm need to take into account? Probably, and at least the following data points.

    1. The relationship between the sender and the recipient - how frequently emails are exchanged, how recent was the last exchange, and what has been the typical response time to this sender in the past

    2. The volume of email needing action/reply in the recipient's inbox at the time the email is received, and how that volume level has impacted response times in the past

    3. The recipient's calendar appointments (most email and calendar services are shared/linked), for the next 1, 3, 12, 24, etc. hours. Is the recipient in meetings all day? Going on vacation tomorrow? About to get on a cross-country flight in two hours?

    4. The subject matter of the email, (parsed for keywords, topics mentioned in the message, attachments, etc.)

    5. Whether the recipient is in the 'To' field or in the 'CC' field, whether there are other people in the 'To' and 'CC' fields, and the relationship of the recipient to anyone else receiving the email

    And probably a few more data points I am not smart enough to think of in the 20 minutes or so I have been writing this.

    The point?

    That a smart algorithm, even a 'dumb' one like at the DMV, could go a long way to help manage communications, workflow, and to properly set expectations. When you send someone an email you (usually) have no idea how many other emails they just received that hour, what their schedule looks like, the looming deadlines they might be facing, and the 12,458 other things that might influence when/if they can respond to your message. But with enough data, and the ability to learn over time, the 'Expected Response Time' algorithm would let you know as a sender what you really need to know: whether and when you might hear back.

    Let's just hope once the algorithm is in place, we all don't get too many "Expected Response Time = NEVER" replies.

    Now please Google, or Microsoft, or IBM get to work on this.

    Friday
    Feb062015

    Notes from the road #14 - Things seen and (over)heard

    The better part of the last two weeks on the road as always provides a rich source of amusement. 

    Herewith, presented in no particular order, are 5 random observations from the road...

    1. Overheard in the Delta Sky Club - 'Tell him to take his head and pull it out of his ass.  If he can't do that, then fire him. I SAID FIRE HIM!'

    2. Also overheard in the Delta Sky Club - 'The last four guys who quit have told HR in their exit interviews that the demands of the job are unreasonable. That it total BS. No, I can't meet with you tomorrow. I have a meeting with HR.'

    3. Also overheard in the Delta Sky Club - 'No I have not hired anyone for Japan yet. They keep sending me crap candidates. The last one didn't know that Osaka is not the same as Okinawa.'

    4. If there is a major spill of liquids or such in an airport corridor, the sheer number of folks that get involved is staggering. Retail workers, airport staff, private security, Metro Police, cleaners, other kinds of maintenance people, etc. I saw a pretty large spill of water in the Cleveland airport, (one of those 5-gallon water jugs blew out), and no less than 9 different people had some involvement in the reporting, cleanup, and assigning blame processes. 

    5. If I ever do another 'Ignite' style presentation (20 slides, 15 seconds per slide), I will absolutely not try to tackle as big a subject as Humanity's relationship with technology. I did think the 5 minute talk went well, but as is my typical fashion, I could have gone on for another 45.  But DisruptHR Cleveland was a blast.

    Have a great weekend!

    Tuesday
    Feb032015

    Two important engagement questions that employers never ask

    Caught this fascinating piece over the weekend from the Bureau of Labor Statistics Monthly Labor Review publication titled Worker's expectations about losing and replacing their jobs: 35 years of change. The piece describes changes over time in American worker's feelings about job security, confidence, and (I would argue), their ability to focus wholly on doing their actual job well, and not just trying not to lose that job.

    This data about worker's expectations comes from analysis of data from the General Social Survey which has been administered each year since 1972. The results of the General Social Survey are representative of the adult population of the United States, as the respondents are in line with population characteristics drawn from the population in surveys of the U.S. Census Bureau.

    In the piece, author Charles Weaver notes that:

    "Workers were less secure about retaining their jobs in 2010 and 2012 than in 1977 and 1978; they also were less secure about the ease with which they would find a comparable job if they were separated. As might be expected, the two measures of job security track unemployment, although other factors certainly play a role as well"

    This conclusion is drawn from the responses to the following two specific survey questions (repeated every year in the survey)

    1. Thinking about the next 12 months, how likely do you think it is that you will lose your job or be laid off—very likely, fairly likely, not too likely, or not at all likely?

    2. About how easy would it be for you to find a job with another employer with approximately the same income and fringe benefits you have now? Would you say it would be very easy, somewhat easy, or not easy at all?

    As Mr. Weaver reports, over time the number of workers who felt it was very likely or fairly likely to lose a job or be laid off rose to 11.2% from 7.7%, while only 48.3% felt it would be very easy or somewhat easy to find a comparable job, down from 59.2% in the late 1970s.

    So in the period from about 1977 to 2012 job security on the macro level had declined, while confidence in one's ability to find a comparable job had also declined. There are potentially thousands of reasons for these declines, and while important, and interesting, are not why I wanted to post about these findings today.

    What I thought about was the two questions themselves, and how an individual worker feels about them might relate to their job satisfaction, performance, and potentially their engagement.

    Are you in fear of losing your job or getting laid off? If you were laid off, how easy/hard could it be to find a comparable job?

    The answers to these questions can tell you plenty about workers. They speak to uncertainty, fear, anxiety, etc. about work and their livelihoods. The less optimistic one feels about job security, the more likely they are to approach work as something to fear, a place not to screw up, and I think these kinds of fears might improve short-term performance, (and other things not directly related to performance, like showing up on time, following rules, etc.)., but a anxious worker is not going to be a happy worker, (or an engaged worker) very long.

    For those reasons, (and probably more), employers would probably like to know how their employees would respond to the two questions above. Wouldn't you like to know if your workers are tiptoeing around, hoping the other shoe isn't about to drop?

    Sure. But here is another sure thing. You will never find either of those two questions on any internal employee survey. 

    Tuesday
    Jan132015

    What Will Happen if we Move the Company: The Limits of Data

    Some years back in a prior career (and life) I was running HR technology for a mid-size organization that at the time had maybe 5,000 employees scattered across the country with the largest number located on site at the suburban HQ campus (where I was also located). The HQ was typical of thousands of similar corporate office parks - in an upscale area, close to plenty of shops and services, about one mile from the expressway, and nearby to many desirable towns in which most of the employees lived. In short, it was a perfectly fine place to work close to many perfectly fine places to live.

    But since in modern business things can never stay in place for very long, a new wrinkle was introduced to the organization and its people - the looming likelihood of a corporate relocation from the suburban, grassy office park to a new corporate HQ to be constructed downtown, in the center of the city. The proposed new HQ building would be about 15 miles from the existing HQ, consolidate several locations in the area into one, and come with some amount of state/local tax incentives making the investment seem attractive to company leaders. Additionally, the building would be owned vs. leased, allowing the company to purpose-design the facility according to our specific needs, which, (in theory), would increase overall efficiency and improve productivity. So a win-win all around, right?

    Well as could be expected once news of the potential corporate HQ relocation made the rounds across the employee population, complaints, criticism, and even open discussions of 'time to start looking for a different job' conversations began. Many employees were not at all happy about the possible increase in their commuting time, the need to drive into the 'scary' center city location each day, the lack of easy shopping and other service options nearby, and overall, the change that was being foisted upon them.

    So while we in HR knew (or at least we thought we knew), there would be some HR/talent repercussions if indeed the corporate HQ was relocated, we were kind of at a loss to quantify or predict what these repercussions would be. The best we were able to do, (beyond conversations with some managers about what their teams were saying), was to generate some data about the net change in commuting distance for employees, using a simple and open-source Google maps based tool.

    With that data we were able to show that (as expected), some employees would be adversely impacted in terms of commuting distance and some would actually benefit from the HQ move. But that was about as far as we got with our 'data'.

    What we didn't really dive into (and we could have even with our crude set of technology), was break down these impacts by organization, by function, by 'top' performer level, by 'who is going to be impossible to replace if they leave' criteria.

    What we couldn't do with this data was estimate just how much attrition was indeed likely to occur if the move was executed. We really needed to have an idea, (beyond casual conversations and rumor), who and from what areas we might find ourselves under real pressure due to possible resignations. 

    And finally, we had no real idea what remedial actions we might consider to try and stave off the voluntary and regrettable separations (the level of which we didn't really know).

    We basically looked at our extremely limited data set and said, 'That's interesting. What do we do with it?'

    Why re-tell this old story? Because someone recently asked me what was the difference between data, analytics, and 2015's hot topic, predictive analytics. And when I was trying to come up with a clever answer, (and I never really did), I thought of this story of the corporate relocation.

    We had lots of data - the locations of the current campus and the proposed new HQ. We also had the addresses of all the employees. We had all of their 'HR' data - titles, tenure, salary, department, performance rating, etc.

    We kind of took a stab at some analytics - which groups would be impacted the most, what that might mean for certain important areas, etc. But we didn't really produce much insight from the data.

    But we had nothing in terms of predictive analytics - we really had no idea what was actually going to happen with attrition and performance if the HQ was moved, and we definitely had no ideas or insights as to what to do about any of that. And really that was always going to be really hard to get at - how could we truly predict individual's decisions based on a set of data and an external influence that had never happened before in our company, and consequently any 'predictions' we made could not have been vetted at all against experience or history?

    So that's my story about data, analytics, and predictive analytics and is just one simple example from the field on why this stuff is going to be hard to implement, at least for a little while longer.