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    Entries in Big Data (25)

    Thursday
    Sep222016

    PODCAST - #HRHappyHour 259 - Big Data and Innovation at ADP

    HR Happy Hour 259 - Big Data and Innovation in HR Tech at ADP

    Hosts: Steve BoeseTrish McFarlane

    Guest Host: Mollie Lombardi

    Guest: Don Weinstein, Chief Strategy Officer, ADP

    Listen to the show HERE

    This week on the show Steve and guest host Mollie Lombardi were joined by Don Weinstein, Chief Strategy Officer at ADP for a conversation on how the power of huge data sets can help HR leaders and organizational leaders be more informed about their own businesses and make better decisions about people and operations. ADP's Data Cloud and Benchmarking tools are leveraging the aggregated insights from hundreds of thousands of customers and millions of employee records to provide information and insight to HR and business leaders in real-time. 

    Additionally, Don provided an update on the one of 2015's HR Tech Products of the Year, the ADP Marketplace, and the continued importance of technology to enable better, faster, and less costly integration of multiple HR and Talent systems.

    Finally, Don shared some thoughts on where HR technology and innovation may be heading in the coming years, and how your voice may become the next User Interface technology.

    You can listen to the show on the show page here, or by using the widget player below, (email and RSS subscribers will need to click through)

    This was a fun and interesting show, and we hope you check it out. 

    Many thanks to Don and the entire team from ADP for hosting the HR Happy Hour Show. 

    Be sure to subscribe to the HR Happy Hour Show on iTunes, Stitcher Radio, or your favorite podcast app - just search for 'HR Happy Hour' to subscribe and never miss a show.

    Friday
    Sep092016

    CHART OF THE DAY: There's almost no one left to fill your open jobs

    I am an absolute mark for big picture labor market data. And the best, most interesting regular look at labor market data os the Bureau of Labor Statistics monthly Job Openings and Labor Turnover Survey report, better known as the JOLTS report.

    Federal Reserve Chair Janet Yellen has stated that the JOLTS report is one of the most important data sets she relies on when pondering the Fed's decisions on monetary policy, and if the JOLTS is good enough for J-Yell then you had better believe the rest of us should be paying attention to it as well.

    For today's Chart of the Day, take a look at what's happening with the ratio of unemployed persons to current job openings - a fixture of the JOLTS data. First the chart, then some comments from me after the data.

    Some quick thoughts on the data:

    1. When the most recent recession began (December 2007), the number of unemployed persons per job opening was 1.9. The ratio peaked at 6.6 unemployed persons per job opening in July 2009 and has trended downward since. The ratio at the end of July was 1.3 unemployed persons per job opening. This represents the all-time low in the ratio since it has been calculated by the BLS.

    2. In addition, the very same JOLTS report shows that the denominator of the ratio, the number of current job openings in the US is also at a record level, hitting 5.9 million at the end of July. 

    3. This data reminds us that it is both a great and terrible time to be in recruiting/talent acquisition. Let's start with the terrible part. For lots of jobs and locations there simply are not enough (qualified for sure), candidates to form an adequate pipeline for the roles you need to fill. There are fewer unemployed persons overall, workforce participation rates remain really low by historical standards, (a subject to its own), and lots of people with desirable skills are coming to terms with their power and negotiating leverage in the market. When you have to pry someone away from the job they already have, that gives a little bit of power to the person that in worse economic times they would not enjoy.

    The good news is that the same JOLTS report that shows the ratio of unemployed persons per job opening is at an all-time low, also shows that the 'Quits' rate, i.e., the percentage of workers who are voluntarily leaving their jobs continues to trend upward - hitting 2.0% in July, which equates to about 2 million quits. In other words, workers continue to express confidence in the labor market and willingness, (almost at a pre-recession rate), to quit the job they have now, to (in theory), take the job you are trying to fill. If you can make a compelling offer, chances are at least decent you can pry someone out of where they are now to take it. And you may have to as the unemployed/jobs ratio continues to fall, and nothing seems to be significantly moving the needle to entice more people back into the workforce who are currently on the sidelines.

    There is plenty more in the report, but I think you get the idea and I will leave it to you to dig in more. The JOLTS report should be your monthly must-read if you are interested at all in what is happening at a macro-level in the US labor market. Bookmark this page and thank me later.

    Have a great weekend!

    Wednesday
    Sep092015

    A reminder that even the world's most admired company has hiring challenges

    Lots of words are spilled in the HR/Talent/Recruiting space that more or less read something like this - 'Oh sure, that (insert HR/Recruiting/Benefits program of choice here), might work for Google or Apple, but there is no way that applies to us, we don't have a sexy, well-known brand.'

    Said differently, it is more or less commonly accepted that companies like Google, Apple, Nike, Goldman Sachs, etc., have incredible advantages in competition for talent by virtue of their brand equity, vast resources, employer brand reputation, and the like. If you are repping one of these companies from Fortune's World's Most Admired Companies list, you would think you pretty much could dial up anyone you need and they would be sold on the opportunity. And that is at least partially, if not mostly true.

    But even the World's Most Admired Company for 2015, Apple, faces the occasional recruiting challenge. Yep, I know, hard to believe.  But apparently in the global fight for scarce data science talent, even Apple has some issues attracting talent. From a recent piece on The Stack titled Apple's privacy policies repel the data scientists it needs to create 'predictive' smart phones:

    Just for once, it seems that Apple ‘can’t get the staff’. According to a Reuters exclusive, the Cupertino-based global device giant is falling behind in the race to create ‘predictive’ services for smartphones because its privacy policies are too protective of the end-user.

    The report has crunched numbers on Apple job openings and talked to various industry insiders, many of whom agree that Apple lacks the best conditions to attract the very limited supply of data scientists necessary to leverage cloud-based services and anticipate the most minute demands of smartphone users.

    The reason for the company’s difficulty in challenging the likes of Google, Facebook and Amazon for the brightest and the best new minds in data science and analysis seems to lie with its commitment to protect the privacy of its users. The report notes that data retention policies on user-centric information gathered into its Siri ‘personal assistant’ product is a reasonably generous six months, whilst information retained from the user’s exploration of Apple Maps expires after only 15 minutes

    So it looks like the world's best talent in the field of data science doesn't like the fact that Apple keeps comparatively less data around upon which to practice their science. Companies like Google and Facebook in comparison, seem to offer these scientists more of a playground for them to challenge themselves with.

    A couple really interesting points I think worth noting in this story, that are probably true for both the World's Most Admired Companies and for your shop as well.

    1. The work, then challenge, and the opportunity to be your personal best in your field still trumps the 'Brand' or the reputation of the company in general. Apple might be the #1 company in the world to work for, but for this group of highly scarce and talented folks it is the work that matters more.

    2. Often the factors that influence a candidate's decision about joining an organization sit well out of reach of the org's HR/Recruiting leadership. No matter how much influence the HR and Talent organization has at Apple, they are never going to impact Apple's customer data storage policies and practices.

    3. For a big company like Apple with lots of resources, acquisition might be the best (and only) way to get the talent that they require. The related Reuters study notes that Apple's 'acquisitions of startups such as podcasting app Swell, social media analytics firm Topsy and personal assistant app Cue have also expanded Apple’s pool of experts in the field.'

    Interesting times out there when even the most well-known, most valuable and most admired companies is facing recruiting issues. I guess that sort of makes the rest of us feel good, maybe a little anyway.

    Have a great Wednesday!

    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.

    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.