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    Entries in Big Data (18)


    Making sense of all that data

    Quick shot, or rather a question for a snowy Wednesday which is this:

    Just how are HR and talent leaders at organizations going to make sense of what is already the dramatic increase in workforce data from all the new and disparate sources that are now or will become available?

    If you think the answer is the deployment of more software tools for creating charts, dashboards, graphics, or better visualizations of that data you might be right. Or at least partly right.

    But it could be that you have already spent time and resources on these kinds of analytics tools and still find that there is a gap between the raw data and the insights you need to derive from that data. Maybe more charts and graphs are not the answer after all. Maybe charts and graphs are not enough.

    But a new company called Narrative Science offers a hint about what the next step might be in data analysis technology with a solution they call Quill.

    Quill is designed to examine raw data, apply complex artificial intelligence algorithms to the data, extract and organize key facts and insights from the data, and finally present that analyses of the data in a narrative, natural language format to the end user.

    So instead of looking at another bar chart with a trend line or a scatter plot that leaves your mind sort of scattered, the Quill system presents a key set of interpretations, conclusions and even talking points for the users (and communicators) of the data.

    Take a look at the video below from Narrative Science to see Quill in action, in the context of an investor's portfolio analysis, and think about how it seems reasonable or possible that a similar data analysis and narrative overlay could be done on all manner of HR, talent, and workforce data (Email and RSS subscribers will need to click through)

    Pretty cool, right? And likely not that terribly complex once some underlying assumptions are put down.

    The financial advisor gets the 'right' talking points and conclusions based on the data and the investor's profile and goals, then he/she can spend more time talking about their go-forward strategy and less time just trying to figure out what the data means. And the advisor can handle more clients too, which is certainly good for the investment firm's bottom line. Surely this has a parallel to the front-line supervisor in any field that has a dozen or more direct reports to keep on track on a daily, weekly, monthly basis.

    But this kind of narrative analysis cuts out one of the chief problems of trying to implement a more data-driven decision making environment, which is answering, simply, the question of 'Just what is all this data actually telling us?'

    I am not sure whether or not Narrative Science has HR or HCM data analysis capability on the product roadmap for Quill, but I bet even if they don't, we will see this kind of capability in the HCM space sooner or later.

    Or maybe some enterprising HCM solution provider is already doing this, and if so, I hope they submit their solution to the Awesome New Technologies for HR process for HR Tech in October!


    Big Data - on the basketball court today, tomorrow in your office?

    Super piece over at Grantland the other day titled The Data Flow Continues: NBA D-League Will Monitor Player Heart Rate, Speed, Distance Traveled, and More, about some of the steps that the NBA, (and its affiliated minor league the D-League), are taking that leverage wearable tracking devices to monitor player movements, player vital signs, and evaluate things like player fatigue levels and stress during the course of play.

    These new devices, ones that go beyond the already in-place sophisticated video technology that records player actions like direction of movement, speed, acceleration and deceleration, and move into more precise measurements of a player's biological and physical status and condition, seem to offer NBA teams a rich and copious set of information that can inform in-game strategy, (Is LeBron really tired, or does he just look tired?), and off season training and conditioning plans.

    But of course the potential backlash for the NBA and its teams is that no one, not even highly compensated NBA players, will be terribly excited about not only having their actions tracked, but also their physical reactions tracked as well.

    But if we move off of thinking about this kind of physical tracking as something that is limited to jobs or activities like playing basketball we could easily see how this kind of technology and data collection and mining approach could have applications in other domains.

    Wouldn't you like to know, Mr. or Ms. HR/Talent pro, how a given manager's team members physically react when they are in a performance coaching session, or getting any kind of feedback on their work? Do the team member's hearts start racing when their boss enters the room or begins one of his soliloquies? Do certain team members react and respond differently to the same managerial techniques? And wouldn't that information be valuable to feed back to the manager so that he or she could better tailor their style and approach to fit the individuals on their team?

    I know what you are saying, no way are employees going to agree to be wired up like subjects in some kind of weird biology experiment. Too intrusive. Too much potential for the data to be lost. Too many chances for the data to be held against them.

    The NBA players are probably going to make similar arguments, but eventually they will succumb.

    I will leave with a direct pull quote from the Grantland piece, and as you read it, think about how naturally you could substitute 'organizations' for 'NBA teams'.

    Bottom line: None of this stuff is going away. Data of all kinds are already piling up at a rate that is overwhelming NBA teams, and the pace and variety of data available will only increase. Teams are going to have to change hiring patterns, and likely hire additional staff, to mine anything useful out of all this information. And the holy grail, to me, remains what these tracking devices can tell us about health — about preventing injuries, predicting them, monitoring players’ training loads, and keeping them healthy.

    Have a great weekend!


    Listen to this: Data and Analytics for Hiring and HR

    From afar, I have to admit for some time I have been a fan of Evolv, an HR technology company that has for the last few years been been doing some really clever and interesting things in the assessment, screening and analytics space.

    Essentially, Evolv helps big organizations, like Xerox for one, understand the characteristics, experiences, and skills that tend to make people successful (and not successful) in a given job, and then helps organizations test for and hopefully hire, the kinds of people that meet (or come close to) those characteristics and therefore are most likely to be successful if they are hired.

    Evolv can then evaluate these data points, (and they have millions of them), compare the answers given by prospective candidates to the profile of characteristics of existing employees that indicate eventual performance success, and assess the 'match' of the candiate's answers to the characteristics of the people that time and history have proven to actually be successful on the job.

    Recently the folks at NPR's Planet Money took a look at the online predictive assessment process that companies like Evolv are developing and their experience and observations make for a really interesting listen. Check out the NPR podcast here, or using the embedded player below, (email and RSS subscribers may need to click through)

    Really fascinating discussion, and mostly because it gives a glimpse into what 'regular' folks, i.e. people not into HR tech or HR or tech, and rather the typical job seeker, think about this approach to employment assessment and HR technology.

    The bottom line seems to be that while there might be some (initial) reservations about the relevance, accuracy, and applicability of these kinds of screening tools for employment, that the 'old' system of resumes, cover letters, interviewing polish, and 'Do you have a friend on the inside?' cronyism that are the hallmarks of the traditional job search are no better than casing your lot with an online assessment.

    For what it is worth, I think the approach Evolv is taking represents the future of screening, assessment, and hiring. Wordsmithing resumes, spending hours and hours on cover letters than no one reads, and trying to decide if you should wait 24 or 48 hours to follow up with a recruiter after an interview seem incredibly nonsensical, add little value to the process, and ultimately have nothing at all inherent in them that can predict eventual success on the job.

    So take a listen to the podcast (about 18 mnutes or so), and get an idea where the future of assessment and recruiting is heading. Let me know what you think.

    Happy Geico Day!


    REPRISE: Happiness and HR Data - Coming to a Delivery Truck Near You

    Note: The blog is taking some well-deserved rest for the next two weeks (that is code for I am pretty much out of decent ideas, and I doubt most folks are spending their holidays reading blogs anyway), and will be re-running some of best, or at least most interesting posts from 2013. Maybe you missed these the first time around or maybe you didn't really miss them, but either way they are presented for your consideration. Thanks to everyone who stopped by in 2013!

    If 2013 was the year of Robots and Automation, then the first runner up for topic of the year would probably have been Data and Analytics. The below post was my personal favorite example of the topic and what the future (the near future I bet) will hold for how data about people will be combined with data about machines and mashed up with process design in order to drive business outcomes. The piece originally ran in August 2013.


    Happiness and HR Data - Coming to a Delivery Truck Near You

    Sometimes in all the conversation in the HR/talent space about the increased use of data, Big Data, and workforce analytics by HR leaders and organizations that practical, innovative (and possibly somewhat creepy), examples of how all this data coupled with better tools to understand it all are sometimes hard to find. Or hard to understand. Or not really specific enough that they resonate with many HR and Talent pros.

    Lots of the articles and analysis about data and analytics for HR end up reading more like, 'This is going to be important', or 'This is going to be extremely important and you are not ready for it', or even 'This is going to be extremely important, you are not ready for it, but I (or my company) is ready to help you sort it out.'

    Fortunately for you, this is not one of those kind of articles.

    Over the weekend I read a long-ish piece called Unhappy Truckers and Other Algorithmic Problems on the Nautilus site, that provides one of the most interesting and practical examples of how a better understanding of HR data, (among other things), is helping transportation companies plan routes, assign work, and execute managerial interventions, often before they are even needed.

    At the core of most transportation and delivery problems is essentially a logistics challenge as the 'Traveling Salesman' problem.  Given a fixed time period, say a day or an 8-Hour shift, and set number of destinations to visit to make sales calls, how then should the traveling salesman plan his route for the maximum efficiency. 

    For a salesperson making four or five stops in a day the problem is usually not that hard to solve, but for say a UPS or FedEx delivery truck driver who may have as many as 150 stops in a day - well that problem of math and logistics gets much, much more complex.  And, as the piece from Nautilus describes, the Traveling Salesman problem is not only incredibly important for transportation companies to try and solve, it becomes even more complex when we factor in the the delivery drivers are actual human beings, and not just parts of an equation on a whiteboard.

    Check out this excerpt from the piece to see how one (unnamed) delivery company is taking HR and workforce data, couples with the realization that indeed, people are a key element,  and baking it in to the classic math problem of the Traveling Salesman:

    People are also emotional, and it turns out an unhappy truck driver can be trouble. Modern routing models incorporate whether a truck driver is happy or not—something he may not know about himself. For example, one major trucking company that declined to be named does “predictive analysis” on when drivers are at greater risk of being involved in a crash. Not only does the company have information on how the truck is being driven—speeding, hard-braking events, rapid lane changes—but on the life of the driver. “We actually have built into the model a number of indicators that could be surrogates for dissatisfaction,” said one employee familiar with the program.

    This could be a change in a driver’s take-home pay, a life event like a death in the family or divorce, or something as subtle as a driver whose morning start time has been suddenly changed. The analysis takes into account everything the company’s engineers can think of, and then teases out which factors seem correlated to accident risk. Drivers who appear to be at highest risk are flagged. Then there are programs in place to ensure the driver’s manager will talk to a flagged driver.

    In other words, the traveling salesman problem grows considerably more complex when you actually have to think about the happiness of the salesman. And, not only do you have to know when he’s unhappy, you have to know if your model might make him unhappy. Warren Powell, director of the Castle Laboratory at Princeton University’s Department of Operations Research and Financial Engineering, has optimized transportation companies from Netjets to Burlington Northern. He recalls how, at Yellow Freight company, “we were doing things with drivers—they said, you just can’t do that.” There were union rules, there was industry practice. Tractors can be stored anywhere, humans like to go home at night. “I said we’re going to need a file with 2,000 rules. Trucks are simple; drivers are complicated."

    Did you catch all the HR/talent/workforce data baked into the model described above?

    Payroll, time and attendance, life events that likely would show up in the benefits admin system, scheduling are all mentioned, and I bet digging deeper into the model we'd find even more 'talent' elements like supervisor or location changes, time since a driver's last compensation increase, and maybe even 'softer' items like participation in company events or number of unread emails in their inbox.

    The specifics of what bits of talent data aere being incorporated into the process matter less than the fact that in the example the HR data is being mashed up so to speak with the 'hard' data from the truck itself (which is another interesting story as well), and analyzed against past driver experiences to alert managers as to when and where an accident is more likely to occur.

    There is even more to the problem than the technical observations from the truck itself, and the alogorithms' assessment of the HR/Talent data - things like Union rules and contracts factor into the equation as well. 

    But for me, this example of taking HR data and using it not just to try and 'predict' HR events like involuntary turnover or a better or worse performance review score, and apply it to real business outcomes, (the likelihood of accidents) represents a great example of where 'Big Data for HR' is heading.

    I definitely recommend taking a few minutes this week to read the entire piece on the Nautilus site, and then think about some the next time the FedEx driver turns up with a package.


    REPRISE: If Yahoo doesn't kill remote working, then Big Data will

    Note: The blog is taking some well-deserved rest for the next two weeks (that is code for I am pretty much out of decent ideas, and I doubt most folks are spending their holidays reading blogs anyway), and will be re-running some of best, or at least most interesting posts from 2013. Maybe you missed these the first time around or maybe you didn't really miss them, but either way they are presented for your consideration. Thanks to everyone who stopped by in 2013!

    The below post hits on a couple of topics that were beaten to death in the HR blogosphere in 2013 - the talent management decisions at Yahoo! and what technology and Big Data will mean for work and workplaces. The piece originally ran in March 2013.


    If Yahoo doesn't kill remote working, then Big Data will

    A little bit lost in the continuing fallout from the decisions by Yahoo to end remote working arrangements for their staff, and Best Buy's move to end ROWE (Results Only Work Environment), at its corporate headquarters was this much more interesting, (and potentially more important), report in the Wall Street Journal, 'Tracking Sensors Invade the Workplace', that hints at a data-powered future workplace where 'being physically together' is not just mandated, but is tracked, recorded, and interpreted by algorithms and leveraged by management.

    How exactly does Big Data, (which usually sounds kind of benign, or at least non-threatening), play a role in the future of telework?  Take a look at this excerpt from the WSJ piece:

    As Big Data becomes a fixture of office life, companies are turning to tracking devices to gather real-time information on how teams of employees work and interact. Sensors, worn on lanyards or placed on office furniture, record how often staffers get up from their desks, consult other teams and hold meetings.

    Businesses say the data offer otherwise hard-to-glean insights about how workers do their jobs, and are using the information to make changes large and small, ranging from the timing of coffee breaks to how work groups are composed, to spur collaboration and productivity.

    "Surveys measure a point in time—what's happening right now with my emotions. [Sensors] measure actual behavior in an objective way,"

    The next step in figuring out how people work, communicate, and interact in the workplace and with their colleagues involves wearing an always-on tracking device, (bathroom breaks optional), and harnessing all the data the device collects about who a worker talks to and for how long, how often they get up, when they hit the coffee room and vending machine, how long they stand waiting outside a conference room because the prior meeting ran long - all of this and more.  Mash up that 'experience' data with other electronic data trails (email, IM, internal collaboration tools, etc.), and boom - the data will be able to prescribe optimal amounts of employee interaction, recommend the timing and duration of breaks, send push notifications alerting you that the guy you need to connect with about the Penske account is two stalls away from you, and crucially - keep your managers informed about just what the heck you are up to all day.

    But it seems really likely to me that if these workplace tracking sensors gain more well, traction, that organizations will quickly realize that the only way to really exploit them, and the data they collect to its fullest potential, will be in a traditional workplace environment - with all employees together in a physical location and 'on-duty' at the same time. Let's face it, for a remote worker wearing a tracking sensor probably won't produce much valuable data - unless its to try to 'prove' to a suspicious manager that a remote worker is slacking off.

    The tracking sensors, if they catch on, will change the anti-telework argument from 'We need you to come in to the office so we can keep an eye on you' to 'We need you to come in to the office so we can track everything you do, say, touch, and feel all day.'

    It's a brave new world out there my friends...