Quantcast
Subscribe!

 

Enter your email address:

Delivered by FeedBurner

 

E-mail Steve
This form does not yet contain any fields.
    Listen to internet radio with Steve Boese on Blog Talk Radio

    free counters

    Twitter Feed

    Entries in data (70)

    Wednesday
    Mar182015

    VIDEO: Fun with the quantified workplace

    The coolest thing you will see on the Internet today, (excepting for cats, bunnies, and 'which superhero would you be' quizzes), comes to us courtesy of the Sid Lee Agency in Paris who have Arduino-powered sensors hooked up throughout their office, and they brought the data together in a single dashboard.(click for a giant version of the dashboard)

    The result is a really interesting and clever view into the inner workings of the workplace in real-time.

    Check out the video below, (Email and RSS subscribers will need to click through), but better still, just head over to the live dashboard to see the real-time updates.

    Pretty neat, right?

    And I think the best HR/Talent play in the dashboard is on the lower right, where Sid Lee has a tile showing current number of job openings at the agency. Clicking that tile takes you to the firm's career site, (which, not for nothing, is woefully unappealing to look at compared to the activity dashboard. Come on HR/Recruiting, pay attention to UX would you?)

    I totally dig this, and I am not even sure why. It's just cool to look at I suppose. Like cats and bunnies and superhero quizzes.

    Have a great Wednesday.

    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.

    Monday
    Mar092015

    Team PowerPoint vs. Team Excel

    What would you say is the preferred tool or mechanism for creating, sharing, and socializing information in your organization that is used to generate discussion and ultimately, decisions?

    While many of us (sadly) would probably default to 'Email' as the technology of choice, even heavy email cultures rely on 'real' office productivity applications for work products and communicating information. Excel and PowerPoint, assuredly, are two of the most common applications in use across organizations of all types. But which one of these two applications tends to dominate how business information and data are documented and shared can reveal plenty about how decisions are made and what kind of organizational culture prevails.

    Check the below excerpt from a recent piece on Digitopoly, a review of research into how competing teams at NASA (Team PowerPoint and Team Excel), created and shared data and information on robot technology used for experiments on space projects:

    On Team Excel, the robot has a number of instruments but separate teams manage and have property rights over those instruments. The structure is hierarchical and the various assignments the instruments are given are mapped out in Excel. By contrast on Team PowerPoint, no one team owns an instrument. Instead, all decisions regarding, say, where to position the robot are made collectively in a meeting. The meetings are centered around PowerPoint presentations that focus on qualitative trade-offs from making one decision rather than another. Then decisions are taken using a consensus approach — literally checking if everyone is “happy.”

    What is fascinating about this is that the type of data collected by each team is very different. On Team Excel where each instrument is controlled and specialised to its task, the data from them is very complete and comprehensive on that specific thing — say, light readings, infrared etc. On Team PowerPoint, there are big data gaps for each instrument but there appear to be more comprehensive deep analyses of particular phenomenon where all of the instruments can oriented towards the measurement of a common thing. This is a classic trade-off between specialised knowledge and deep knowledge. What is extraordinary is that they bake the trade-off into their organisational structure and also decision-making tools — literally emphasizing different apps in Microsoft Office.

    We probably don't consciously think too much about how the technology and tools choices we make can effect how the organization actually functions, what particular approaches and skills tend to dominate, and even what gets recognized and rewarded. In the example from the Digitopoly piece, an argument is made that both of these approaches, Team Excel with its focus on individual accountability and control, and Team PowerPoint that relied much more on shared accountability and the 'big picture', are needed and have value.

    Where we get into trouble, I think, is when one type of technology, say PowerPoint, becomes dominant or the de facto method in an organization for communicating information and as a decision support tool. It is by its nature, shallow, and it assumes that viewers and readers understand the details and deeper contexts about the subject matter that is typically just about impossible to convey in a slide deck.

    Similar arguments can be made on cultures where 95% of communication is over email, or tied up in impossibly complex Excel workbooks. 

    We often choose the easy or expected technology solution out of habit, or out of a kind of cultural allegiance. It is fascinating how these technology choices can impact much more than we think.

    Team Excel. Team PowerPoint. That really shouldn't be the choice. Team 'Right tool for the job' is. Choose wisely.

    Have a great week!

    Wednesday
    Feb252015

    CHART OF THE DAY: There's Just 5 Million Open Jobs in the USA

    Here's your latest Chart of the Day, courtesy of my two favorite online data sources, the Bureau of Labor Statistics, (specifically the Job Openings and Labor Turnover Summary, or JOLTS report), and the FRED data analysis and visualization tool.

    First, the chart, then some FREE commentary from your humble scribe:

    1. First, the actual numbers - there were 5.028 million job openings in the US on the last business day of December 2014, the highest number since December 2001.

    2. The chart shows a pretty much straight up and to the right climb in job openings since early 2009, meaning talk of the recession and the labor market disruptions it caused are really seeming far, far behind us

    3. This increase in openings is driving organizations like Walmart to raise wages for many of its workers - for a wide range of industries, and geographies, (including previously 'low worker power' ones like retail), the balance of that power is shifting. 

    4. Average weekly earnings for Production and Non-farm employees are climbing as well, not as fast as jop openings, but certainly on the same trajectory.

    So what does this mean for you, Mr. or Ms. HR pro?

    Probably nothing new, or at least nothing you have not been hearing about and likely experiencing in the last 18 months or so. 

    Lots more noise in the system to get your company and your opportunities noticed in a much more crowded market of available jobs.

    Many fewer un- and under-employed individuals around that might not always been qualified for your openings, but at least were a source of steady candidate flow. At the depths of the recession, there were about 7 unemployed workers for every job opening. Today that ratio is less than 2/1.

    You, having a harder time coming up with explanations/excuses to your leadership and hiring managers who (traditionally) are much slower to accept these changes in the labor market and the ensuing power shifts. I recommend forwarding to them the Walmart story above, with a subject line that says 'See, even Walmart is having a hard time finding and keeping people'.

    Long story short, we entering year 6 of an extended recovery/tightening of the labor market. Talent is in shorter supply, opportunities are everywhere, the Dow and the S&P 500 are at record highs, and the people you need to find, attract, and retain are well, harder to find, attract, and retain.

    Have fun, it's a jungle out there.

    Tuesday
    Feb242015

    On trusting algorithms, even when they make mistakes

    Some really interesting research from the University of Pennsylvania on our (people's) tendency to lose faith and trust in data forecasting algorithms (or more generally, advanced forms of smart automation), more quickly than we lose faith in other human's capabilities (and our own capabilities), after observing even small errors from the algorithm, and even when seeing evidence that relative to human forecasters, the algorithms are still superior.

    From the abstract of Algorithm Aversion: People Erroneously Avoid Algorithms After Seeting Them Err:

    Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet, when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human.

    Let's unpack that some. In the research conducted at Penn, the authors showed that even when given evidence of a statistical algorithm's overall superior performance at predicting a specific outcome (in the paper it was the likelihood of success of MBA program applicants that the humans and the algorithm attempted to predict), most people lost faith and trust in the algorithm, and reverted to their prior, inferior predictive abilities. And in the study, the participants were incentivized to pick the 'best' method of prediction: They were rewarded with a monetary bonus for making the right choice. 

    But still, and consistently, the human participants more quickly lost and faith and trust in the algorithm, even when logic suggested they should have selected it over their (and other people's) predictive abilities.

    Why is this a problem, this algorithm aversion?

    Because while algorithms are proving to be superior at prediction across a wide range of use cases and domains, people can be slow to adopt them. Essentially, whenever prediction errors are likely—as they are in virtually all forecasting tasks—people will be biased against algorithms, because people are more likely to abandon an algorithm than a human judge for making the same mistake.

    What might this mean for you in HR/Talent?

    As more HR and related processes, functions, and decisions become 'data-driven', it is likely that sometimes, the algorithms we adopt to help make decisions will make mistakes. 

    That 'pre-hire' assessment tool will tell you to hire someone who doesn't actually end up beign a good employee.

    The 'flight risk' formula will fail to flag an important executive as a risk before they suddenly quit, and head to a competitor.

    The statistical model will tell you to raise wages for a subset of workers but after you do, you won't see a corresponding rise in output.

    That kind of thing. And once these 'errors' become known, you and your leaders will likely want to stop trusting the data and the algorithms.

    What the Penn researchers are saying is that we have much less tolerance for the algorithm's mistakes than we do for our own mistakes. And maintaining that attitude in a world where the algorithms are only getting better, is, well, a mistake in itself.

    The study is here, and it is pretty interesting, I recommend it if you are interested in making your organization more data-driven.

    Happy Tuesday.