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    Entries in Big Data (20)

    Friday
    Jun272014

    TOP HR DATA PLAY: Kill the FTE

    I had a fun time riding shotgun to Kris Dunn yesterday on the Fistful of Talent Webinar titledHR Moneyball:  The FOT Bootstrapper Guide To Getting Started With Big Data, in which KD and I took a look at some the ways that HR/Talent pros can use Big Data and Business Intelligence approaches to raise their games and drive the adoption of so-called 'Data-driven HR' in their organizations.

    Of the five 'Big Data' plays in the FOT playbook, I think the one that I dig the most was #3, an idea called 'Salary Cap Utilization'. The basic idea is this - take a play from the world of sports leagues like the NBA and NFL that force teams to operate under a set of rules that govern maximum total player compensation, (the 'Cap'), and apply it inside your organization.

    I know what you are saying, that we already do that, it's called the Annual Salary Budget. We've been managing compensation that way forever. Each budget holding group or manager is allotted 'X' amount of dollars he/she can 'spend' on total comp for the year and they (probably subject to a dozen other HR rules around increase percentages, salary bands, etc.), have to sort out how that salary budget is allocated among their staffs.

    But chances are you are placing an additional, and probably unnecessary constraint on your managers as well - something called the full-time equivalent (FTE) budget.

    The FTE budget tells managers that in addition to the maximum amount of $$ you can spend on comp (The Salary Cap), there is some (kind of arbitrary) maximum number of headcount that you can spend your Salary Cap on, i.e., the FTE budget.

    When I first moved into an HR role, managing the HR systems at a mid-sized company, and first encountered the acronym FTE, I had to ask someone to explain it to me, as I had never seen it before. It seemed like a made-up kind of a construct, especially when you have to spend time breaking down and trying to convert worker schedules into their 'full-time' equivalents. And what, really, is 'full-time' anyway? That too, is kind of an arbitrary measurement to some degree.

    But $$ are not arbitrary and are not subject to interpretation or manipulation. Everyone understands what a dollar-based budget means.

    What are the advantages of dropping the FTE budget/constraint from your playbook?

    1. It gives leaders/managers more autonomy on how they allocate compensation across teams. Instead of operating under the dual constraints of 'heads' and $$, they simply have to make it work within the Cap. Need to makes some big changes to reinvent their department? Make it work under the Cap. Want to expand into something new? What can you give up to stay within the Cap? Have 5 all-star, 'A' players that need to get paid or they will walk out the door? Then pay them, just be ready to make the cuts elsewhere to remain within the Cap. 

    2. It forces the organization to be more flexible. The overwhelming tendency in an FTE-influenced budgeting scheme is for managers to guard 'their' FTEs like grim death. Have a position sit open or vacant for too long and managers will scramble to fill it with just about anyone, just so they don't 'lose' that precious FTE in the next budgeting cycle. Have a solid employee that wants to transfer out to a role in a different department? A role  that might better suit their skills and enhance their career development? Better be willing to give up an FTE buddy to make that happen.

    3. It allows HR pros to be more consultative and progressive when talking about things like merit increases, equity increases, offers above salary band maximums, counter-offers, retention bonuses, and most everything related to comp. Remove that FTE constraint and now more of the comp game is open for discussion and adaptation. HR is working with the business around what is important to the business - the relative cost of performance and how to get the most production from available resources. HR can now be in the game of reporting/advising on Salary Cap Utilization instead of counting up heads, something that in most instances does not really matter.

    We had a few other Big Data plays that we shared in the Webinar that were pretty neat as well (Hiring Manager batting average, turnover prediction, Health Care claims per capita), but for me eliminating the FTE might be the simplest and easiest one to get started with. 

    Have a great weekend!

    Thursday
    Jun192014

    WEBINAR: HR Moneyball: How to get started with Big Data for HR

    You have heard the hype: Big Data is taking over the business world, and HR’s going to be expected to make decisions—not through feelings, relationships or gut instinct—but via numbers.  The problem is… your HRIS, ATS and Performance Solutions are all different systems and weren’t built with the big-data revolution in mind. In short, you feel less than ready for workforce analytics—you’re just trying to get the basic reports generated.

    We feel your pain, people. That’s why I am glad to participate in the June installment of the Fistful of Talent FREE webinar series with a jam titled, HR Moneyball:  The FOT Bootstrapper Guide To Getting Started With Big Data. Join Kris Dunn and I Data nerdfor this webinar on Thursday, June 26 at 2pm EST(sponsored by ThoughtSpot, a cool business intelligence startup), and we’ll share the following goodies with you:

    A brief review of where HR stands with Business Intelligence (BI)/Big Data. We’ll cover some of the trends, what the bleeding edge is doing, the 3 types of data sources available to HR shops and what the CEOs and business leaders you support are asking for related to data and BI out of the HR Function. We’ll also talk about what your options are when HR is the last priority for an over-burdened IT function.

    Why HR pros need to shift/lean forward. It’s not what happened, it’s what going to happen. Getting your head around business intelligence and data means you have to shift your focus from reporting the past and move to predictive analytics. We’ll give you examples of great reporting decks from the HR Hall of Fame and tell you how they have to change to meet the call from predictive analytics out of your HR shop.

    - The Five Best HR Plays for Business Intelligence (BI) and Big Data. Since we’re all about helping you win, we wouldn’t do this webinar without giving you some great ideas for where to start with a data play out of your shop. You’re going to stop reporting turnover and start predicting it. You’re going to stop reporting time to fill and start showing which hiring managers are great at—you guessed it—hiring.  We’ll give you five great ideas and show you how to get started piecing the story together.

    - A primer on what’s next once you start channeling Nostradamus. Since you specialize in people, you naturally understand the move to using Business Intelligence (BI)/Big Data that helps you predict the future is only half the battle—you have to have a plan once the predictions are made. We’ll help you understand the natural applications for using your business-intelligence data as both a hammer and a hug—to get people who need to change moving, and to embrace those that truly want your help as a partner.

    You’re a quality HR pro who knows how to get things done. Join KD and I on Thursday, June 26 at 2pm EST for HR Moneyball: The FOT Bootstrapper Guide To Getting Started With Big Data and we’ll help you understand how to deploy Moneyball principles in HR that allow you to use predictive Big Data to position yourself as the expert you are.  

    Hope you can join us on June 26 at 2PM EST.

    Wednesday
    Mar052014

    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!

    Friday
    Feb142014

    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!

    Wednesday
    Jan222014

    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!