Teaching Data Analytics in HRM Courses: Why It's Important and How To Do It



let me begin by introducing our speakers David Coughlin is Cameron professor HR analytics and an instructor of management at Portland State University where he teaches introduction to HRM HR is and people analytics and OB he was awarded the teaching innovation award at Portland State University Talley Bauer is Cameron professor of management at Portland State University and is currently the president for the Society of industrial and organizational psychology she is one of the most influential HR researchers in the world and a fellow at SIA APA and ApS she is the Past editor of the Journal of management and has one major teaching awards from ALM and SIA she was also the first-ever management scholar at Google Talia and David are both part of the author team of the new textbook from stage publishing human resource management people data and analytics this one-hour webinar will be recorded and archived for future viewing we will be sending out a link to view it and access the slides to all registrants in the coming weeks if you have any problems with audio or viewing mode during the webinar please use the Q&A box at the right of your screen and one of our team members will get back to you as soon as possible at the end of the webinar we will be having some time for Q&A from the attendees so please also use the Q&A box to ask any questions to the speakers throughout the webinar please also take note of the webinar hashtag sage talks and feel free to ask questions or leave comments there as well now to get started let's take a moment and get to know our audience by answering a couple of our polling questions okay great let's move on to our second polling question and if you have any approaches that we did not list here feel free to put them in the Q&A box to let us know okay thank you everyone for participating and telling us a little bit more about yourselves without further ado I'm gonna pass it over to Talia and David to get us started so this is Talia and thank you for joining us we're excited to share what we've steamworks in terms of teaching analytics within an HR HR course and why it's important David and I started by transforming our HR information systems course in the HRAs as well as HR analytics and that was successful we built from there and this fall we launched a year-long six course graduate level certificate as well as Sarah mentioned publishing HRM people data and analytics and that's for introduction to HRM so we've seen what works with undergrads as well as grad students across a wide range of ability levels our approach entails really breaking HR and of analytics into three levels of literacy fluency and mastery as you see here and what we found is that students first need a foundation of concepts ideas and examples of HR analytics so it's great to see that a lot of you are already integrating case studies and examples in your teaching and in your courses and then after that they're ready to engage with the material to gain additional fluency and speaking issues understanding what's happening and starting to be able to engage in HR analytics independently by the mastery phase students are ready to tackle big issues and are more sophisticated in their approaches and nimble in their HR analytic skills we think about what we're going to cover today we're really going to focus in on the ideas and activities related to the literacy and fluency levels as we find those really pair well in introductory courses and then moving from there if you're interested there are additional courses that could be offered in terms of depth of material that gets that mastery at this point I'm going to hand things off to David he'll go over the outline for today David thank you very much Kelly and thank you everybody for attending today and it was great to see everybody's responses and it like we have a good variety in terms of how people are applying HR analytics through case studies as Thalia mentioned as well as through some data exercises as well so the focus of today's presentation is going to be briefly how did we get there in terms of the field of HR and then we'll offer a brief definition and treating HR as a science and how to get students excited about that and then the the bulk of the presentation will be on training students and we'll give some examples using the approach that we've found helpful and drill down through some examples from the textbook that our data exercises case study or cases as well as some pop out boxes that you might find useful and we'll finish up with briefly with some future directions ok so in terms of the current state of affairs for HR analytics this has been long discussed but we'll just re-emphasize this here in terms of what's happened with a traditional HR over the past several decades historically HR professionals spent a lot of their time doing transactional activities payroll timekeeping things like that administering benefits and helping people enroll in benefits during open enrollment and through automation we have seen that we've actually been able to reduce the amount of time that people need to spend on some of these sort of classic HR transactional activities in terms of the number of people hours because our HR information systems our enterprise resource plans can do a lot of resource planning platforms can do a lot to automate and facilitate those processes and what this has done is freed up a lot more time for HR professionals to focus their attention on more transformational activities such as validating their selection tools evaluating training programs making sure that their performance management tools are free of bias and in doing so they can also hopefully serve as more of a strategic partner for the organization and help HR align its initiatives using data with the initiatives of the organization in order to inform and support the organization's strategy so what this does mean though is that by refocusing HR with these new time that's available now through automation to focus on these transformational activities is that it means that today often HR professionals are faced with learning new skills and knowledge and at developing different competencies and to that end the society or rather cherm Society for Human Resource Management they have their competency model and one of the key competencies that they've identified as critical evaluation which is essentially where critical thinking logic and data analysis management and so forth live and so that's a big emphasis of this presentation today is how do you bring that competency to life how do you get students excited about that competency and see critical evaluation is something that they can build on their own and through helpful Scalf scaffolding through text books as well as through careful instruction and how to better prepare them for the changing HR environment so this also comes on the heels of a rapid acceleration in terms of technology and analytics ability not just with an HR of course but across multiple functional areas of the business and this comes from Josh Burson and as he described it we're kind of at this point of rapid acceleration or the elbow right now where we're seeing ourselves accelerate the technology is increasing very rapidly our abilities are increasing rapidly and part of this has been made possible by computational processing power different software programs that have made HR analytics and data analytics in general more accessible to broader audiences and so we see this through our enterprise resource planning platform vendors such as Oracle and s AP and IBM and we also see it through tools such as tableau power bi as well as programming languages like R and Python and for more massive amounts of data platforms and and software such as Hadoop by Apache and so in terms of this is probably also something I'm sure a lot of you have seen over the past year Deloitte's been doing this several years now in terms of pulling a number and surveying a number of organizations and getting a feel for how they view HR analytics among other emerging areas in HR right now as well as what they feel about their current capabilities in that area and so most recently in 2018 Deloitte as part of their global human capital trends report identified that 85% of the surveyed companies rated HR analytics as important or very important and 70% of those companies reported actively working towards integrating HR analytics into their strategic decision-making however less than half of those surveyed companies rated themselves as ready or very ready for the HR analytics trend and this is where we've identified a talent or a skills gap here and its students today are at a really have a really great opportunity to start learning some of these skills so that when they go on the job market they can help hopefully close some of these skills gaps or at least have the competencies or the baseline foundation they can work from – in order to launch themselves into modern HR positions so in terms of how we're defining HR analytics here the way we try to get students excited about this is treating HR as a science and really framing it from that regard for a lot of students the idea of the scientific method of the scientific process is something that maybe they learned in middle school or high school and perhaps haven't revisited it since then so there's some I would say a nostalgic effect as they as you review the steps of good scientific process or method and often trying to reinforce that this is just really rigorous problem-solving that we're talking about here just applied to an organizational context and specifically to HR and so we really see this about as being the confluence or the marriage between core HR processes and systems and data informed decision-making to help us arrive at HR analytics as we know it today now in terms of HR analytics this is a broad conceptualization in terms of not only just how do we acquire and analyze the data but also how do we report the data how do we tell a story about the data that's going to be compelling yet accurate and how do we make sure that what we're doing is actually aligned with the organizational sensations objectives hrs objectives and make sure that we're informing and supporting the organization strategy and one thing that I've noticed is cha for some students is they often ask so what's the difference between HR analytics people analytics cumin have capital analytics talent analytics workforce analytics and so forth and for our purposes we're going to some people might add some more nuance there we're gonna treat these as all or more more or less synonyms for today's talk but we also try to draw and that many of these principles are although they're getting more widespread attention today they've been H some HR departments have been applying these for years and industrial or additional psychology has been a pioneer in this area since earlier in the 20th century bringing some of these what we now call HR analytics to life and the support of original objectives and I think more people are starting to get excited about this generally at least I've noticed with HR students as data science has become just a very popular term in popular media and I've seen students who get very excited about the idea of wow I've always been interested in working with people and the idea of people but I'm also interested I've liked my math and my statistics classes and is there a place for me and now with this opportunity in the HR environment we can start teaching students who have that interest I've also found that and we found that students who you maybe wouldn't expect to have an interest in data analytics as its applied to HR discover a passion for it maybe when they took their math or their statistics class in the past they never really saw a connection to something that was meaningful for them maybe it was more of a theory based statistical ass for example as opposed to application with data analysis and so we see this as a great opportunity to get people excited about something whether or not they plan to do actually be the data analyst on their team in the future just understanding these concepts can be really really illuminating for them and to that end we try to bring this to life in the textbook through cases and so for instance we have a case study at the beginning of chapter two on Chevron and how they've leveraged human or HR analytics within their organization to predict turnover as well as for Workforce planning purposes and things of that nature and so we try to bring in these rich examples so students can understand how this is being a what are the commonalities and how our organizations ultimately using this as I mentioned before to inform and support their strategy and so we usually end or we do end each case with a number of discussions to help them think more critically about this we also have some videos too via the online supplement to the textbook and in which there are people are different people are interviewed about their HR practices in general as well as more specifically about what they might be doing around HR analytics so while this is meant to be a traditional introduction to HR and human resource management in general textbook we do try to pepper in and reinforce this idea of HR analytics and data and all the different places where data might exist and help people think very carefully about how it might be used so in terms of how we're trained we we have envisioned training students we see that there's four or five general areas that we can focus on in order to train students to fill those HR analytics talent gaps and so the first is obviously to focus on those core HR concepts that are not going to go away understanding what employee selection is as a system what does it mean to validate a system what are some of the different legal considerations that we need to focus on as well which is obviously very important within HR environment and in addition when focusing on HR analytics we also want to emphasize the importance of gaining technical skills whether that's through information technology statistics mathematics information systems in general having some conversational knowledge at least on what a neat enterprise resource planning platform is as well as helping students develop the soft skills those communication skills how do they telling story using the data that they've acquired they've analyzed they've managed and to do so in a way that's actually going to inform or prescribe action and that gets the last point and part of it is trying to help understand and help students understand how HR fits into the broader business environment and how HR can be a very valuable business partner and a strategic partner organizational initiatives and so one of the things that we would imagine is going to happen and I'm already starting to see this happen as I look at different job postings on LinkedIn and so forth for entry-level HR jobs is I'm noticing a trend where some knowledge of HR information systems basic data analysis and Excel or some other platform maybe SAS or SPSS and sometimes even occasionally R or Python I'll see you mentioned in some of these job postings and so likely the trends is going to be than in the next ten years that probably most people in HR will be dealing with data directly and maybe either as consumers where they're evaluating reports or looking at their dashboards and so it's important for them to get up to speed in terms of to understand what's being presented to them or to actually do some of the analysis themselves now we've broken it down into these 7 HR analytics competencies that we think students it's important for students to learn one of the most important cornerstones is actual human resource management theory and practice and systems as well as social science and psychological theory and helping them use that and leverage their knowledge of that in order to understand and interpret data that they might be presented with as well as how to ask more informed questions about what initiatives should we target or what problems need to be solved now in addition we see business acumen as a standalone copy competency as well and so traditionally students get trained very well on these first two competencies of theory and business and then depending on what their interest on interest is they may or may not have much exposure to data management statistics data analysis measurement and so forth and that's where there's a lot of opportunity we've seen this in the espalier mentioned the HR information systems and people analytics course that we developed and we've taught that students can very quickly learn up the pick up these data management concepts in the context of HR and they can learn quite a few different statistical skills data analysis skills even for us were in the quarter system in just a 10-week term by the end of the term they can be applying regression based approaches and so forth to validate a selection tool and so forth I'll actually show you an example of that a little bit later on and then also story telling something as I mentioned earlier is that something that we would like to emphasize and then finally a lot of the traditional programs and ways in which a char is pot focused on ethics and employment law and we like to take this a step further and focus on specifically ethics and laws it relates to data and the use of people data human data within organizations one of my favorite quotes and it's I believe it's actually the title of his blog post from last year's David Green who I believe the title is something to be effective don't forget the H in HR analytics don't forget the human in HR analytics and so that's something that we try to emphasize to our students as well as in the textbook now the way we see this and as other people have talked about this there's a continuum and there's people that know very little of it about know very little about data and then there's people who we would call heavy quants that have advanced degrees in computer science data science statistics mathematics and so forth and then we have kind of this emerging sweet spot which is the like wants people that know enough about the underlying analysis they know enough about the underlying data and they know enough about the business context so they can translate between different groups so from those people they're doing some of the more advanced analytics that can hopefully pose meaningful questions for them and help interpret what they find and they can hopefully go to those frontline managers those supervisors and employees and try to identify potential problems that could could be solved or at least answers provided to using data analytics and so this is really this analytical translator role that integrating data analytics into HR courses even at the undergraduate level can really be meant to enhanced and so we also like to conceptualize in order to break down how does a typical HR analytics project unfold and how can we tackle each one of these different areas as part of this process in order to train students up on it we like to frame this in terms of the HR analytics project life cycle and so it ranges from everything from question formulation data acquisition or collection data management analysis the interpretation and storyteller component component to actually deploying and implementing and prescribing action and so what I'll do next is work through partially through this model to give you some examples of how we've seen this bead effective effectively deployed in the classroom as well as some examples from the textbook that we've identified on different data exercises in cases and so forth they can bring together different aspects of this or bringing to life different aspects of this project lifecycle so beginning with this idea of question formulation I find that this is one of the areas that students especially by the time they get to an intro HR class they maybe haven't been doing a lot of coming up with their own questions often questions are given to them and this is very typical so I think one of the important things is getting them more comfortable with thinking ok what are the values of the organization what are some of the strategic objectives what are the objectives of HR and what are some questions that would be good to answer so meaningful questions where we can find meaningful answers and this really helps focus people in on doing purposeful analytics versus analytics for analytics sake the last thing we want is someone to be sending down at the end of a dark hallway just typing away programming away on their computer just running multiple models that are not necessarily in the service of anything the organization actually cares about so we want to try to identify and help students figure out what questions are going to be meaningful and we try to provide them with examples of what are what is a good question how do you ensure that that question is aligned with the strategy and so part of the way that we frame this in the textbook and this actually comes from Chapter two or refocus on strategy and data analytics within HR and first introduce these concepts is we introduce the scientific process and so we in doing so we're really just again emphasizing this idea it's rigorous decision-making process and so we start with identifying the problem which is the way we frame question formulation so really thinking about what how can you identify what the problem is describe what the problem is before you start thinking about well where are we going to source the data or acquire the data to help find a solution to this problem moving on to another phase of that HR analytics project lifecycle another thing that we've found effective is to get students to think very critically about the quality or the integrity of the data and more and more often in organizations I'm hearing a great greater concerned around data governance data provenance as well as data lineage lineage so not only are the data clean are they useful but also where are they coming from how did they get here and so being able to tell that part of the story of the data as well and part of this means when it comes to acquiring good data is recognizing what data is going to be appropriate to analyze and sometimes obviously and this is a hard thing to teach but it's I think it's most useful to do this through examples it's showing what happens if you use what we like to call garbage data if you pump in garbage data you get garbage data on the other other side in other words garbage in garbage out and so that's something through different exercises and showing all the different ways in which data can become poorly arrived dirty or that there's improper data validation procedures that have been followed or no data validation procedures that have been carried out what some of the consequences are that we can still often run them through some type of analysis whether it's a statistical model or something like that and we might get output but how meaningful is that output going to be and I think this becomes increasingly important as we see more and more accessible data analytic software where there's point-and-click and you can create a model this especially with machine learning now it's relatively easy with some of the new tools like Microsoft Azure to create a diagram and create a machine learning model without necessarily having to think very critically about what is going into that model in the first place and so again we like to emphasize this garbage in garbage out and so as an example from the textbook we have a data exercise on that we give to students and the basic background is this we give them an excel file and this is downloadable from the sage website well there's a number of different fields or variables of interest here everything from employee ID last name and so forth their job level facility start date and those types of things and really these are somewhat arbitrary but the point is to illustrate all the different ways or at least some of the common ways in which data come to us and need to be cleaned and so here's a screenshot from this this is actually from chapter three and as you can see we have the fields presented here and if you do a quick scan let's say with the start date column you'll start to notice that oh wow there's one entry all of them a lot of them seem to be in a standard date format and then a couple are written in as June 6th with actual the the word June written out as well as July is in there and so helping students recognize that our programs aren't going to be typically smart enough to know what we met and this is something that I think is best taught through example and so we show them in this exercise they can walk through and learn how to use your simple Excel tools data validation tools as to identify different out-of-bounds values things that are unrealistic that perhaps someone entered by air as well as set data validation such that those values that would be out of bounds will be recognized and they can be addressed and we also show how to use filters and things like that and even pivot tables in order to identify some of these issues and then to clean the data where possible now next as we move along that HR analytics continuum we like to focus as many people do on distinguishing between descriptive predictive and prescriptive analytics where descriptive is typically the HR metrics or describing something that's already happened in the past you're looking at past data and often these are things that are commonly reported in decision support dashboards or manager dashboards that HR professionals and consumers of HR data might be using and then predictive analytics we're moving on to the point where we are taking data from the past creating a model using that model to predict future events and then validating that that model using additional data down the road and then with prescriptive we're taking what we know from descriptive and predictive and we're actually prescribing action a plan of attack for people based on what we found and so we'll start now with descriptive analytics and give an example of how we've seen this effective or B effectively deployed to students to help them understand what descriptive analytics even means and so for instance one exercise might be to simply calculate annual voluntary turnover rate for an organization often these things are automatically computed forests and a lot of our platforms today but it's sometimes helpful for students to see one how simple these calculations can actually be and what's the underlying math and I think it gives them a little bit closer to what's happening and when they can do that it starts to make a little bit more sense to them in terms of what for example a turnover rate even means and so here's an example from chapter 10 where the focus is on employee retention and turnover in that chapter as part of the data exercises we give them some sample data they work through do some very simple Excel formulas and computation and they arrive at an annual turnover rate and again this is meant to just illustrate for students how simple some of these metrics are and hopefully to if any of them are coming into this with any kind of phobia around numbers or if they are have concerns about their general data literacy we can hopefully kind of lift the veil or elucidate what's really happening most of the time and usually these descriptive analytics are relatively simple to understand and compute once you unpack them now moving on to predictive analytics this is what we'll say in the textbook this is an entry level intro to HR textbook so we don't go into true predictive analytics in the sense of actually cross validating the findings or having training datasets that then you ultimately develop your model on and then apply that model to a test dataset what we do do is set them up for what we or look what I like to call predict ish analytics it's all most predictive analytics you've done the first step you've identified or developed a model and then that could be deployed again in the future so for example we could use an equation or a model derived from or based on regression to predict future applicants job performance based on their selection tool scores in other words just employee selection tool or assessment validation criterion-related validation specifically and so in the textbook we also have example with the data exercises where we give them some sample data where the criterion is customer service so that's the performance outcome we're interested in predicting for presumably some type of customer service job and we give them some backstory to provide some of that rich qualitative context that helps them interpret the findings and to make it more meaningful for them and then the students get to practice estimating the criterion-related validity of the different selection tools which in this case is a proactivity personality inventory emotional intelligence inventory and a situational judgment test score and in doing so we walk through that free add-on packet gives the stats or the data analysis pack add-on for Excel show them how simple it is to point-and-click through setting up a regression model and ultimately to receive output from it and so we work all the way up to a multiple linear regression model where we can introduce this idea of statistical control by having more than one predictor variable in the model and so this is not meant to give them obviously complete fluency in this but this helps them gain that sense of mastery where again it helps when they hear the word model and specifically a regression model they'll have a clearer understanding of what that might mean in DHR context and why that might be valuable and this can be really helpful when they're working with vendors and vendors start using the word algorithm or model so they can start asking questions about well maybe what are the underlying approaches that were being used to actually estimate the data now kind of a common thread that we apply through everything as I alluded to before is this idea of ethics and legal issues and one of the questions we like to kind of back students up at all times so as they're going through the rigorous problem solving their purse ient if it is to have them think and take a step back and engage in some type of ethical decision-making framework and really this can be distilled down to just because you can should you and so just began this has become increasingly more important with how much access we have to employee data today as well as with data privacy and security concerns which are a couple areas that we like to focus on quite a bit in the classroom as well as in the text and so when students take and a moment to pause sometimes they realize oh this is something that yes we could scrape the data we have this new scraping tool for example and I'll show you an example of that a second or perhaps we could choose how we want to display a data visualization to an audience of people who may not know much about data analysis or data analytics and so there's a lot of control that comes with that and a lot of room for ethical decision making as well and so here's a classic example that we present in the textbook on an in chapter 3 we do a whole section on data visualization and in HR context and this actually comes from the early 80s a study that Cleveland and colleagues did where they found very simply if you scale the Y and the X axes on a scatterplot to make them larger so on the Left you'll see kind of more of a standard scaling and then on the right image you'll see that it's actually been scaled such that the the the Y and the X axes have much larger scales people tend to perceive that the that the scatterplot on the right is displaying a much stronger association than the one on the left even though they're based on the same underlying data and so again this comes down to this idea of what is our responsibility as analysts and how can we teach students to start thinking about these issues critically because sometimes this becomes challenging when some platforms we use maybe even Excel plots and so forth that they have these default settings and so thinking very carefully about when do we override those defaults in order to express something more authentically and more accurately and so people can really use it to make more accurate decisions and as I mentioned a second ago also it's really incredible all the tools we have access to in Python and are today and I'm sure there's other platforms that make this readily accessible you can scrape data from websites fairly easily and so then the question becomes okay just because you can do this you can gain access to this information should you do this and so throughout the book we have different spotlights on legal issues and ethical issues where we turn students attention to that so beside is that there's a big responsibility in HR to be good stewards of the data and another example that we we arrive on as well as far as a spotlight and ethics is the use of wearables and fitness trackers trackers and what the implications are for data privacy as well now moving along with that HR analytics project lifecycle after they've analyzed the data they've managed the data and so forth it's time to interpret the data and tell a story around the data and one of our colleagues who teaches our storytelling course in HR she likes to break it down to the three C's of storytelling of communication connection and clarity so helping students develop those skills of what is a clear story what is a good story or narrative structure and how do you make sure that the story is appropriate in terms of the length and so what I've observed is that students often want to focus on 10 different things in a 10 minute presentation and instead getting them to focus on two or three things that they can communicate and add some repetition and to reinforce the ideas as well as how do they establish that connection how do they communicate clearly with an audience that might not know as much as they do about HR in general as well as about data analytics concepts or how do they communicate with an audience that's going to know a lot about the technical aspects or maybe the legal aspects and so forth and so being aware of their audience and being able to communicate effectively and tell a story in that way and so we do show through data visualization we have an exercise as part of the textbook as well where we have them compute in chapter 11 where they calculate copper ratios for different employees and we show them how simple it is to calculate that and then we have them create a simple scatterplot in order to see if there's any type of linear and nonlinear pattern that could be indicative of perhaps pay compression or salary compression or even inversion and so here we're just having them plot 10 year on the x-axis and compa ratios that they computed on the y-axis here and just showing them how data visualization can be a great way to understand the data and again this can hopefully help them ask better questions as they start to exam explore the data they have access to now to finish up we we do see some future directions for HR analytics some of which we've already discussed today but I do want to emphasize that I what we've observed is ethics is of course going to be an ongoing conversation and as we have new tools as more people have access to these tools I think that it's going to be incumbent on HR instructors and those who work in in the in firms to communicate to both their students or their employees the importance of being good stewards of the data and to think very critically what we do in this situation given what we know what are the prevailing standards and where are their legal gay grey areas that we need to fill in using ethical decision-making and then the next thing is wearables I already mentioned that with fitness trackers there's a number of opportunities especially in the area of workplace safety with different sensors people can wear that can detect noxious gases as well as personal protective equipment like hard hats that can detect impacts and concussive forces and so how can we use that those data and that introduces a whole new challenge for HR especially when it comes to how often those data are livestreaming there quickly streaming and they start to get into that realm about often people would call big data they might not be structured in a way that's going to be ready for analysis and that's where good training on data management comes into play and the textbook we talk very briefly about blockchain as this is a very quickly emerging technology and it's starting to be discussed in terms of applications in HR and so it'll be really interesting to see how this shapes up in the next I'd say five years or so how blockchain will district' shape the way that maybe payroll is done in organisations and as well as other things that I think we haven't even thought about yet in terms of potential applications they're still I think there's great value and students understanding traditional centralized databases relational databases and that tends to be most of the emphasis within the textbook is what are some of the key concepts around and HR information system or database management system and what does it mean to centralize your data and so forth and then finally a kind of a new for not a new frontier but an important frontier is how do these things trickle down to small and medium-sized organizations large organizations have a number of luxuries often in terms of having sufficient sample size to run more advanced types of analytics and even move into the area of machine learning but with smaller organizations they can sometimes be limited depending on what it is that they're investigating in terms of what types of analyses are going to be appropriate but with that said it's pretty amazing what some of the new platforms that are available for small and medium sized enterprises and specifically with HR information systems today that can be great tools for small and medium sized organizations and so I think seeing those tools continue to develop will be really interesting in the future and to that end we do in the text book have a pop out in every chapter that specifically focuses on how just and not just HR analytics but what does HR look like in small and medium organizations and in some cases we will offer or some bread and space best practices or some different considerations for small and medium sized organizations so just to wrap up we talked about the current state of HR analytics brief definition of what HR analytics is and then spent most of our time talking about what training students looks likes and what Talia and I have found to be effective in the classroom and I should add that we've taught this both in-person and fully online and we found that it can be done in both ways and one thing I will say is that having careful tutorials laid out for the students such as the ones we provide in Excel for the textbook can be really really helpful and sometimes it might seem like wow this is 20 steps long but I've found we've found that students really like having less ambiguity when it comes to doing these more technical things and so that's one thing that we found bit quite effective is having a clear tutorial for students to start with is really critical in terms of eliminating that barrier to entry for them and hopefully getting them very excited about how to apply this and how they might it might even shape the trajectory of their career for some and then we finish up talking about some future directions so at this point I'm going to pass this back to Sarah and unshare my screen here and this might take a second here and Sarah can you see the screen no I can't thank you excellent thank you very much thank you so much David and Talia for your informative presentation now we're going to spend some time addressing your questions from the audience so if you guys can please continue to send them in through the question box on the right side of your screen or using Twitter you can use Twitter with the hashtag sage talks we'll try to get to all your questions by the end of our time but if we can't we will address them after this the speakers have agreed to do a follow-up blog post and we'll post some of the questions and answers on our sage blog called sage Connect okay so let's get started David Italia can you talk about the benefit of this approach for non HR majors who might be taking an introduction to HRM course absolutely I have a few anecdotes that and I'll just share one of them I think this is a great marketing area for HR right now one of the best students I've ever worked with I worked with her the past couple years and she graduated this past year I remember she took my HR intro to HR class as an undergraduate and she was taking it as an elective because at the time she was a supply chain and operations major and so she would sit there in class and she was interested but she was someone that at least outwardly did not express a lot of confidence through her body language kind of just like physically would kind of hide herself in the classroom but engaged wrote excellent did excellent work in their class and then about halfway or about four weeks in the term when I introduced the first data exercise to the students she lit up and then we had some more data exercises and we used independent samples t-test to evaluate a post-test only with control group training design very simple I simply I did some data it's much like one of the examples from the textbook that we used today and that got her so excited that she switched majors and she had always been interested in psychology in humans and this was the bridge she needed to get into HR and and so from that point forward she ended up taking my more advanced HR information systems and analytics course and over this past summer she was creating tutorials in Python and teaching herself I taught her are in another course and she was she's really run with it so I think there's that side as getting people who maybe didn't always ever think about HR as being an area they get into helping attract them the second area is that I find people from different functional areas it's a way to I think from outsider's perspective help lend credibility sometimes to HR because often I find with accounting and finance students finds a supply chain as well they want to see data and so this is a way for them and their eyes to say oh this is a not that it wasn't legitimate before but in their eye they needed that kind of connection so I think this is something that can really bring this to life for other students and sometimes those students from other areas bring some really outstanding technical skills and quantitative skills that they've learned through their their other areas of concentration tell you did you want to add something yeah it's funny I've written the mind out credibility I think that's the huge one if we think about HR or relatively late to the party in dealing with HR and with analytics in HR we're marketing and finance and operations have always had a lot more numbers and analytics associated with them so I think what you described as the bridge is what I've seen students are much more open to HR being important and kind of rigorous when they see the cases and the hands-on exercises so it actually I think has been helping it's probably the bigger the bigger thing is as you said somebody coming into HR because they love the H the human to say okay this is going to help you be more able to help individuals so for them that's the bridge for people in other areas it's like okay I get it we care about people because that's going to help us be more effective in business and I see that you're speaking in the language I can understand so I actually think it's a win-win thank you Thalia another question we have is can you talk more about the H in HR analytics and how data and analytics can benefit employees and people in organizations absolutely one of the themes that we run through the book is and this is partly because I believe all this is authors of the textbook we have engaged in research and occupational safety and health is that we do give many examples throughout the textbook of how data and lungers HR in general how we can be used to address employee well-being employee safety and outcomes related to those and focusing on employee engagement and so that's not only are we looking at these organizational outcomes like preventing turnover and things like that but we're also looking we also focus quite a bit on the importance of fit in organizations as well as in one chapter we talk about even applicants reactions to different selection tools and procedures and what the consequences can be like that for people who don't perceive those favorably and so that is something that I try to emphasize in the classroom quite a bit is making sure that they always take a step back and realize that there's people behind this and then asking very carefully well one do people know that we're using their information like this that's an important thing and then to do in what ways can this help benefit the employees in our workforce absolutely as you mentioned a lot of us do research in the area of safety and we also have a fairness lens so a lot of us do fairness and justice kind of research and I think that's a way to think about it I think it's intuitive to say well I care about people and that's one on one and so I like barb she does a good job I'm gonna give Barbara raise and if I just use cold numbers that might not be as good for or for me as a manager and an example it's kind of an extreme example but it's one that's kind of a feel-good example Google used a lot of analytics to understand what was going on with their workforce and as a result of that they actually determined that what they needed to do was give not just barber A's or a few people arrays or a certain job category but they engaged or what they call the Big Bang and they gave everyone across the organization a 10% raise now that's a nice story and not everyone has those resources but it is an example of how we maybe make assumptions about how numbers don't help people hurt people they do hurt people they're not going to help them but I don't think that's necessarily true and I think the more that we can exchange share those good examples with students the more they get to see that actually being systematic and rigorous and having high quality data to inform decisions is really very fair and it's really a very nice way to support the humans in the HR absolutely and just to quickly add on to that – that's a great point tell you about fairness and justice perspective and related to that is the idea of bias and as well as potential discrimination and and also bias within our tools that maybe were not aware of and so that against gets to this idea of data acquisition integrity and measurement and that's something that I think a lot of a lot of us have a tendency to do and I think especially students as they're getting first getting into this is they see a number and they assume that that's truth and so helping them understand that there's a human behind for instance the performance the supervisor rating a performance performance and as part of performance appraisal or evaluation and helping them think critically well what are the different sources of bias here and how can we uncover that using analytics and help triangulate whether or not we think that a tool that we might be using might have bias in terms being injected into the data that then if we go to analyze those data we're going to end up with biased results on the other side and this I'm really pleased that I'm hearing this discuss much more broadly now the idea of algorithmic and model-based bias and how bias that's enters into so a statistical or computational model can actually be amplified on the other side and so I think that's something and I'm again I'm very pleased that this is now into the popular culture that people are more aware of this Thank You David we have a whole bunch of questions so I'm gonna move us on to the next one question is how do you deal with the great range of readiness or aptitude among students for the key concepts and analytics could you repeat that question please I didn't catch the first up sure it's how do you deal with the great range of readiness and aptitude among the students for the key concepts in analytics oh that's an excellent question that is very excellent because it is often quite a bit of range and you know some of this in terms of the readiness goes all the way back through grade school and I do get the impression that some students have experience but I don't know might it's somewhat traumatic experiences with math maybe they take a statistics course in high school or something like that or maybe even in college as well as computer science if they've been exposed to that and so I think that's one of the things is trying to first create this sense of psychological safety that it's okay this is kind of a sandbox you can learn how to do these things by doing and these are things we're all going to make errors and so creating in safe environment I think is the first way to kind of again reduce that barrier to entry for everybody and then the second thing is to understand that people will move at different speeds and this is where I found that I'm Talia and I experimented with this and now it's turned into more of an official program is having students who really get it quickly start coaching other students and this is part of our learn do you teach models so they learn it then they start doing it through some exercises and then those students who move really quickly through it and you may be worried that they're gonna start getting bored you have them start teaching other students in the class that helps them teach it in a much more rich level and then the students that they're teaching get the benefit of having a peer who's just learning these concepts themselves who can recognize what are some of the common things that they didn't understand given how close they are to the phenomenon start giving often a really good explanation and so that's something I would really recommend as well I think part of it also is thinking about you know kind of that literacy the fluency in the mastery not every student is going to want to get to really being fluent and mastering this but every single student should have literacy and so if what you find is a majority of your students are on that end it might be of great value to spend time there and having extra credit exercises for people who want to do a little more hands-on and so it's understanding doing that needs assessment with your group but then being able to move them up in terms of their ability level over time a couple other things that david has done that have been great is creating kind of boot camps so what are some basics that students can take to help them along the way get the basics and tutorials and so those are things that might make sense depending on what you're trying to do if it's just a few students who need to catch up that coaching example works as well okay David and Talia another question we have is how do you teach students to deal with the outliers and missing data that's an excellent question and that's something that with the undergraduate level I tend to punt on that a little bit because I make them aware of this and so when we're going through we'll look at some of the diagnostics and so forth but the data I typically simulate for them is going to be very clean it meets the statistical assumptions for whatever model that they're running if it's an inferential statistical model and a parametric one at that and then with the with the graduate students though that we're teaching in our HR analytics graduate certificate right now we get much more in depth into outlier detection and in that particular coursework we're teaching them via are so we show them different functions and packages that can be used useful for diagnosing outliers different types of diagnostic tools that they can use that are fairly readily available and then helping them it's you know the trickiest part is no we're never gonna fully meet or have a hundred percent confidence that we've met statistical assumptions or we've what exactly as an outlier which is what is not an outlier and so I focus a lot on the role of decision making and judgment calls and have them focus in on documentation and to clearly describe and keep notes on why did they remove this outlier what's the reason for it did they go was their way to correct the value was it typed in or entered and appropriate leads begin with or is it just someone that's perhaps not part of the population of interests that somehow ended in up in the data set and then having them back up to the research question okay what is it you're trying to answer and does it does it make sense to eliminate this case or is this something that we should keep then so I think it's the documentation it's giving them the tools but typically again at the graduate level for sure with the undergraduates I've found that that can quickly get very overwhelming with for them when they're just trying to learn what a regression model is or what a t-test is or analysis of variance and so backing that up into or just keeping it more focused on kind of toy examples that are going to be very clean but making them aware if you're going to do this in real life it's going to be way Messier thank you okay I think we're almost here I have one last quick question for you David and that is what is your take on using platforms like data camp to teach undergraduate principles of analytics so with data camp I I'll admit I'm not too familiar occasionally I'll peek on there to see what approach they're using I think one of the things I've noticed with some of the modules that are available through platforms like that is that they can vary quite a bit in terms of how much information and structure they given how much context they give to students and I think those can be great tools but I think they often at least within an HR context it's useful to scaffold it and to surround it with a rich context so you know we're having issues hiring the right employees so we need to identify better selection tools or validate our existing selection tools and so creating some perceived need for them a reason they should be doing the analyses I think can really enrich that experience for them and I think that I'm sure those data camp tutorials can be really excellent if scaffolded and again I haven't done a good look at many of them and so there could be some that are really are doing a great job integrating context already but that would be my suggestion is to supplement them with a rich HR context great thank you so much David well we're just coming up on time here so I would like to thank David and Talia again for your presentation and your time it's been really informative I know we haven't been able to get to all the questions so I want to thank everyone for joining us today again a special thanks to David and Talia we will be making this webinar available and for the questions that we haven't gotten to I know somebody is asking if they will be able to get the Q&A and we will provide the questions and answers along with the webinar afterwards so please be on the lookout for those emails coming please stay connected with us on our blog stage connection for more information about upcoming webinars and that's it for today again thank you everyone for joining if you have any other questions we would be happy to try to answer them in our blog contact and again to David and Talia thanks for your time today and we hope to talk to all of you again soon

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