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Week 12 - Transcript
(Interview With Sally
Smith) Latest Update
Timothy Bednall We're here today with Sally Smith. Sally is the Head of People Analytics at National Australia Bank or NAB, as it tends to be known, by its employees. Sally, very grateful for you being here today because you know, obviously you know, both got very, you know, busy schedules, so you know, I really appreciate your taking the time. Tell me, I guess I'll start by asking a little bit about. I mean your role, you know, within our National Australia Bank, what does what does a typical day in the life of ahead of People Analytics look like? 00:01:38.580 --> 00:01:56. Sally Smith Yeah, before I get to that Tim, will just want to say thank you for including me. I'm particularly passionate about growing the field and really keen that we have students that had came to understand what people analytics is in practice for one of the major organizations in Australia. So by way of background, I've worked in people analytics for round, about six or seven years now, and in that time I've been involved in various aspects of the work so. Usual sort of pieces around reporting, providing common KPI's answering questions about Tata and that kind of thing in the middle of that six or seven years I held rolled out. I would describe as the highlight of my career. So what that meant in practice was our people insights. Projects were under untangling complex. People related questions using data as well as science, so not just the usual suspects of opening and trawling spreadsheets and applying algorithms, but we would work backwards from what are some
of the people related business issues that are experienced in other parts of the organization? And also looking at some of the expensive HR practices and finding ways of doing more with less. More recently, I've added to that portfolio and included aspects about reporting portfolio so that tends to be standardizing some of the metrics and reporting them through executive reports or HR related dashboards. Uh, and at the moment we're looking at how do we take sort of what's been a very tactical solution in reporting and scaling that out too? Many leaders and analytics specialists at the National Australia Bank terms of an average day in people analytics. I would say no. Two days are the same, which is what I love about my role. 00:03:57.970 --> 00:03:58. Timothy Bednall Fantastic. 00:04:01.190 --> 00:04:04. Sally Smith Uh, so one day it could be conversations around a particular issue. So if I give you an example of one of our call centers has some kind new starter attrition and the HR team, there were keen to get underneath what might be causing that. So scoping out a potential sort of way of untangling that and finding root cause.
When we got into that and explored it with HR as well, they equally felt the pain point because we were madly recruiting and training people only to see them run out the door. So there was pain on on both sides of the equation. In terms of that business experience into, uhm, the amount of effort and energy that I took from HR perspective and the first thing that we wanted to do was to ensure that it was a real issue. So I hear lots of things from. Our business colleagues, uh, about issues, but sometimes they're quiet isolated to specific pockets, or it's, set, the metric might be added for weather app. Might be out of sync with what they used to. But when we compare, say that with a benchmark of what might be a reasonable level, there's not sufficient upside to warrant a deep analytics project on it. Just to go back to this particular example, when we had a look at our call center situation and we compared their level of new starter attrition, when I say attrition, that's people leaving NAB. 00:09:23.700 --> 00:09:24. Timothy Bednall Umm? 00:09:34.400 --> 00:09:40. Sally Smith To get another job externally when we compared it to a a benchmark level. 00:09:41.400 --> 00:09:49. Sally Smith And estimated the cost, uh, we knew there was sufficient upside to warrant further investigation. 00:09:50.350 --> 00:10:14. Sally Smith So the first thing that we did was to share that number that I knew from that moment we had executives in our Pachar team as well as our business area. Hooked because costs NPS, they're all on executive scorecards. And Tim, I don't know about you, but I don't know any executives. It's not keen to find ways to improve their metrics. 00:10:14.450 --> 00:10:15. Timothy Bednall Guys yeah yeah. 00:10:15.460 --> 00:10:38. Sally Smith Absolutely. So I had a lot of interest at that point and we knew that it was worthwhile going through the pain of obtaining all of the data points. So this is the usual suspects of trying to source business data as well as HR data. They're difficult to join because there's four different hierarchies at NAB to try and synchronize things. 00:10:39.100 --> 00:10:42. Sally Smith And it takes time, energy and effort to to do it. 00:10:41.980 --> 00:10:42. Timothy Bednall Wow.
Sally Smith D Yum. 00:10:44.910 --> 00:10:52. Sally Smith Also in tandem with that that work we started a bit of an action learning or sort of research group, so. 00:10:53.470 --> 00:11:11. Sally Smith By that I mean the team that we're hiring people, the teams that were upskilling our learning peoples. And then there's also a team that support new starters as they come into the contact center. And interestingly enough, everyone had a different idea around what was wrong. 00:11:11.760 --> 00:11:12. Timothy Bednall Umm? 00:11:11.880 --> 00:11:22. Sally Smith So the recruiting team thought we needed to change some of the standards so they were tinkering away on the parameters for recruitment. 00:11:22.630 --> 00:11:31. Sally Smith Now learning Team thought that all of the courses had to change so they were rewriting causes like Matt, Matt, our team in the contact center. 00:11:32.350 --> 00:11:44. Sally Smith That we're watching people go out the door, thought everything else was not quite right before the work that they did, and they thought maybe we're best just to take H are out of the equation and do it ourselves. 00:11:45.020 --> 00:12:03. Sally Smith And UM, kind of devalue actually started right there because it was interesting when we ran a few workshops. Those groups haven't actually met each other so often when I think people analytics, I think spreadsheets, you know, causation, modeling all that kind of thing, but you can achieve a. 00:11:59.670 --> 00:12:00. Timothy Bednall Umm? 00:12:02.270 --> 00:12:03. Timothy Bednall But in fact, it's actually. 00:12:04.320 --> 00:12:28. Timothy Bednall It's actually bringing people together, and I mean getting them to share their perspectives and
Sally Smith Uh, instead of the issue being. 00:13:57.090 --> 00:14:18. Sally Smith People processes the the result indicated that what we would call the environmental conditions or the pressure in the call center was having that spiral effect on new starter attrition, so that the result and next steps were counterintuitive because it wasn't that we had to rewrite causes or do other things. It was some. 00:14:13.030 --> 00:14:13. Timothy Bednall Umm? 00:14:19.220 --> 00:14:30. Sally Smith If we get our resourcing right, which is around how many FTE we can have from a financial perspective and having decision rights on? 00:14:30.780 --> 00:14:31. Timothy Bednall Milk. 00:14:31.770 --> 00:14:36. Sally Smith Particular projects that stabilizes that new starter attrition. 00:14:37.910 --> 00:14:56. Timothy Bednall That makes a lot of a lot of scenes, and in some ways it sounds like it's a bit of a vicious cycle from the point of view that you've got people leaving and therefore making the problem worse and spreading. You know more work over fewer people. So gosh that that is an interesting finding. 00:14:56.740 --> 00:15:03. Sally Smith Absolutely, and you describe it exactly as one of our key slides because there's the vicious cycle and we also saw. 00:15:01.440 --> 00:15:02. Timothy Bednall Umm? 00:15:04.410 --> 00:15:14. Sally Smith And impact to the time it took for someone to get to a level of high performance. So in that tough environment, more people leaving as well as it takes longer to upskill. 00:15:14.470 --> 00:15:30. Timothy Bednall Of course, and all all the time taken to recruit and select and date, you know, on board people is is
probably, you know, in the end a lot more expensive than you know. Having a better resourced workforce, so I would imagine. 00:15:30.710 --> 00:15:44. Sally Smith Yes, so it was some. I think it was incredibly valuable because you know six months down the track when those stabilized that grab service metric. We actually saw new starter attrition come back into a healthy range. 00:15:31.370 --> 00:15:31. Timothy Bednall Yeah. 00:15:39.100 --> 00:15:39. Timothy Bednall Umm? 00:15:45.240 --> 00:15:56. Sally Smith The added advantage is it meant that everyone that was tinkering with the process in HR could stop and focus their energy on other things because that wasn't the root cause. 00:15:45.300 --> 00:15:45. Timothy Bednall That's fine. 00:15:54.320 --> 00:15:55. Timothy Bednall Paint it. 00:15:56.830 --> 00:15:57. Timothy Bednall Fantastic. 00:15:58.600 --> 00:16:17. Timothy Bednall Uh, is it? You probably know, you know, I'm also an industrial organizational psychologist, so you know, I, I find these, you know, sort of root cause analysis so you know, really interesting and you know, I think I think there's a great project or, you know, great example of people analytics in action. 00:16:18.080 --> 00:16:40. Timothy Bednall And so I guess I'd like to talk about, you know, a little bit about yourself, and you know how you actually found yourself? Are you in this role? And how did you get your start? You know in this field, and you know how? How did you actually advance so this, you know particular field that you're that you're in. Sorry, this particular role that you're in his head of people analytics. 00:16:36.920 --> 00:16:37. Sally Smith Yeah.
Sally Smith And I think I attended. I've tended to do it from an evidence based or a database decision. So in an analytic sense, it's always been. 00:18:45.180 --> 00:19:06. Sally Smith Using data spreadsheet statistics, that kind of thing. We really took it to the next level when we started tackling some of the more complex problems and leaning on the science, so I also tend to do a lot of reading and general articles because there's plenty of expertise that's gone before us and sometimes NAB data doesn't have the answers. 00:19:06.570 --> 00:19:07. Timothy Bednall Me. 00:19:07.460 --> 00:19:12. Sally Smith The the second thing is we do I tap into a network of. 00:19:13.400 --> 00:19:20. Sally Smith Of people in similar roles outside now because what's happening at numbers also tends to be happening in other corporates. 00:19:20.290 --> 00:19:21. Timothy Bednall Me. 00:19:21.770 --> 00:19:33. Sally Smith And the yeah, the the the third thing I think is a really key learning in that time is HR is at its best when everything works together. 00:19:33.820 --> 00:20:02. Sally Smith So I have, so there's sort of around about 400 people that work in HR, and now I'm so I haven't amazing colleagues and specialist disciplines, and I think the reason why I've been successful in this work is because we tend to take a horizontal view across and join the dots between everything, because when everything is In Sync, it tends to have the maximum impact on business outcomes, which at the end of the day, that's that's what we're here for. 00:20:02.350 --> 00:20:19. Timothy Bednall Makes complete sense and you know with an HR department at large it must be, you know, quite difficult to ensure that all of the different functions in HR. Actually, you know In Sync with one another and supporting you know common common goals, I suppose. 00:20:21.310 --> 00:20:23. Sally Smith Yes and where?
Sally Smith From the other thing I'll mention, two is the analytics is not just spreadsheets, it is qualitative and. 00:20:32.150 --> 00:20:36. Sally Smith Research through conversations with colleagues. 00:20:37.110 --> 00:20:38. Sally Smith So we do. 00:20:38.990 --> 00:20:49. Sally Smith I do tend to look for things where they might be out of sync because he's using. You can do is pull one lever back in to be alignment to be in alignment with them. 00:20:50.770 --> 00:20:54. Sally Smith With other areas, I think if I give you an example of that it. 00:20:55.930 --> 00:21:05. Sally Smith No nab is, I think, the the the occasion AB is our business banking and our business bankers. So one of our projects was to look at what the best bankers do differently. 00:21:06.460 --> 00:21:19. Sally Smith And a key insight from that project was actually looking at peoples experiences Overflow network capability development career as well as reward. 00:21:20.350 --> 00:21:26. Sally Smith And the results of that project indicated, I think, where it's almost like a. 00:21:29.230 --> 00:21:42. Sally Smith Things were a little bit out of sync. We could offer great career development programs, but if we weren't necessarily synchronizing our salaries and and things around that, then we could have the best career development in the world, but. 00:21:43.800 --> 00:21:49. Sally Smith It wouldn't. It wouldn't appeal to a broad population until you get the two working together. 00:21:49.830 --> 00:22:05. Timothy Bednall Yeah, well that that certainly makes sense. And you know if if I'm aspiring I I think you know from aspiring to you know more senior role without you know much greater you know number and breath you know depth of responsibilities then. 00:22:05.860 --> 00:22:19. Timothy Bednall
Sally Smith Probably 2 fold. Actually one is the variation in in work activity. Across now is so different it can be quite difficult to understand and that variation from a 36,000 foot view. 00:24:03.630 --> 00:24:17. Sally Smith So if we can use a data set to base it on actual behavior over a certain time, we can offer more granular insights and preferences around different populations or by role, or by demographic attribute or what have you. 00:24:17.760 --> 00:24:18. Timothy Bednall Umm? 00:24:18.680 --> 00:24:36. Sally Smith The other thing that we then do is to look to see if there's teams working in different ways to then compare and see if their experiences or views of those experiences are different. So if some teams are more engaged and have and feel their working more. 00:24:38.130 --> 00:24:45. Sally Smith Productively for want of a better words are they? Do they tend to be more in the office or or less so? 00:24:40.200 --> 00:24:40. Timothy Bednall Umm? 00:24:44.390 --> 00:24:45. Timothy Bednall Me. 00:24:45.900 --> 00:25:05. Sally Smith That really helps one give us sense around how things are working and how it varies between different States and locations. 'cause obviously Tim where in Melbourne, but those in Sydney, Brisbane, Adelaide and Regional Australia have a very different experience of of our head office locations at the moment. 00:25:06.210 --> 00:25:12. Sally Smith The other example that I'll lend on for that too is back when we transition to remote working. 00:25:13.210 --> 00:25:22. Sally Smith Last year there's no guidebook for shifting 35,000 employees to working remotely, so. 00:25:22.350 --> 00:25:40. Timothy Bednall That's right, and and it really, it really turned on a dime, didn't it? I mean it it just happened so quickly. At least that was my perception of my own workplace. You know, one one week we were
coming into the office every day. And then you know, a couple weeks later we were almost entirely from home. 00:25:40.950 --> 00:25:47. Sally Smith Yes, Yep, and that was very similar for the vast majority of nerve employees so. 00:25:41.730 --> 00:25:42. Timothy Bednall Yeah. 00:25:45.750 --> 00:25:46. Timothy Bednall Me. 00:25:48.280 --> 00:26:01. Sally Smith Same sort of thing of can we tap into different data points across the organization, pair it with information that comes through our employees surveys to get a better sense of of what's happening and the variation. 00:26:02.370 --> 00:26:13. Sally Smith In work experience, it's very topical, I think at the moment because in our head office locations on most that employers are are based in Melbourne, 'cause that's where I head offices. 00:26:14.210 --> 00:26:15. Sally Smith But it really shapes. 00:26:16.450 --> 00:26:26. Sally Smith Recommendations on the approach to return to the office and to the types of things that we want to understand and any encourage. 00:26:27.230 --> 00:26:56. Timothy Bednall And there's certainly a real wealth of I mean employee data. I mean, that's available. I've been, I mean, reading quite a bit of the work of. I mean, Rob Cross recently on organizational network analysis. And you know, he's talking about, you know how they will often use, you know meta data from, you know, from you know things like calendar appointments, and you know Microsoft Teams meetings and things like that to get a little bit of a sense of. 00:26:56.710 --> 00:27:28. Timothy Bednall You know who's collaborating with who, and you know whether whether or not and you know, sort of meta data on in individuals as well. Like you know our people checking their emails at 11:00 PM at night. Or you know, engaged in our, you know, rather, you know unhealthy work habits and and so I think I think it's really, you know, quite interesting that you know you can use a lot of those you know metrics to, you know, get a bit of a sense of.
Sally Smith I'm always aware that. 00:29:22.620 --> 00:29:25. Sally Smith Privacy and ethics, uh? 00:29:26.570 --> 00:29:36. Sally Smith Incredibly important, and our folk in the Privacy Office and ethics area, and I think some of my closest contracts at damn. 00:29:35.700 --> 00:29:37. Timothy Bednall Me. 00:29:37.190 --> 00:29:47. Sally Smith Because we have a philosophy of people, data for good and doing reporting that aligns to notice guidelines and privacy practices. 00:29:48.900 --> 00:30:02. Sally Smith But we I guess we apply in over overlaying to run around with this past the pub test and if I was to have a child barbecue about the work that I do, is that something that people would be comfortable with? 00:29:56.240 --> 00:29:57. Timothy Bednall Me. 00:30:02.970 --> 00:30:07. Sally Smith And yeah, handle hard. I would say analysis that we did through. 00:30:08.160 --> 00:30:22. Sally Smith That sort of meta analysis, tracking macro trends and hotspots is something that added insight and added additional layers of potential actions to take and hotspots to monitor through that through that period. 00:30:14.990 --> 00:30:15. Timothy Bednall Umm? 00:30:22.940 --> 00:30:23. Timothy Bednall Umm? 00:30:23.840 --> 00:30:24. Sally Smith Uhm?
Sally Smith And so I think they are the types of things that. 00:30:28.890 --> 00:30:40. Sally Smith Modern people analytics change not just in Australia but internationally as starting to lean into because they're not just looking at their usual HR of metrics, but they're pairing. 00:30:41.910 --> 00:30:49. Sally Smith What often being used in HR with business outcomes as well as you know non traditional big data sources like? 00:30:49.760 --> 00:30:50. Timothy Bednall Umm? 00:30:49.830 --> 00:30:56. Sally Smith Come measure it with the the the metrics from the middle level outlook. 00:30:56.980 --> 00:31:23. Timothy Bednall Umm, and it's certainly I mean a very big issue because you know, even you know that metadata. Can you know sometimes contain information that you know employees, or rather you know not share and you know like like for it. Like for example, if you know you had contacted you know someone from a different organization about a potential you know job opportunity. 00:30:58.050 --> 00:30:58. Sally Smith Things. 00:31:23.860 --> 00:31:45. Timothy Bednall Or, you know, I I think I remember when there was a national privacy debate you know about, I mean, telephone records and metadata they could. They could say, well, we know at, you know, 8:00 PM last night you know you called that sex hotline. But we don't know what you talked about though. So yeah, it was a wrong number. 00:31:42.660 --> 00:31:43. Sally Smith That's the wrong number. 00:31:46.550 --> 00:31:48. Timothy Bednall That's that's that's right, so. 00:31:46.750 --> 00:31:47. Sally Smith Yeah.
Sally Smith When we look for finding things that make nab a better place to work and a better place for our customers, it's balancing the native needs of three. 00:33:30.570 --> 00:33:39. Sally Smith And Tim, if you may have seen in some of the analysis we've done with you know these types of data sets over the years. And if the. 00:33:39.850 --> 00:33:59. Sally Smith Students are doing analysis with come with data sets and what have you lot of relationships in HR data is converted. This it's a horseshoe but it one way or the other where broadly speaking there's usually a zone where it's it's about right. If you go too far in One Direction or too far in the other. 00:33:49.200 --> 00:33:50. Timothy Bednall Umm? 00:34:00.790 --> 00:34:14. Sally Smith Then you might be experiencing issues around productivity, engagement or whatever it might be. So yeah, it's it's finding those owner of me. It's just about right. 00:34:14.660 --> 00:34:38. Timothy Bednall Yeah, yeah, the judicious use of data as a as a as opposed to, you know, going in with no evidence versus going too far with your analysis. And you know, making it overly complex and you know with no clear takeaways or actionable insights. Yeah, I know you know exactly what you mean there. 00:34:39.240 --> 00:34:39. Timothy Bednall Yeah. 00:34:40.990 --> 00:34:50. Timothy Bednall So I'm I next question is where do you see the field of own people analytics advancing to? You know in the next three to five years. 00:34:51.400 --> 00:34:55. Timothy Bednall End up and what do you see is the driving forces of these changes. 00:34:57.260 --> 00:35:02. Sally Smith Next three to five years. I very much depends on. 00:34:59.260 --> 00:35:00. Timothy Bednall When?
Sally Smith I think the level of maturity or where organizations are currently he's. So I've I mentioned we have a a network and I know quite a few people. 00:35:15.900 --> 00:35:32. Sally Smith In large organizations in the field, and some are very progressed and combining people data with business data to understand what influences performance and engagement others are. 00:35:34.210 --> 00:35:38. Sally Smith Really trying to deal with some of the call people, metrics and core reporting issues. 00:35:39.590 --> 00:35:48. Sally Smith So for those that have have progressed further along the continuum of the challenge, I think the next three to five years is. 00:35:43.610 --> 00:35:44. Timothy Bednall Umm? 00:35:49.200 --> 00:35:50. Sally Smith Ringling mold 00:35:51.650 --> 00:35:57. Sally Smith going from understanding to actually being able to offer prescriptive insights. 00:35:57.910 --> 00:36:14. Sally Smith So for instance, what we know what great managers tend to do differently and project oxygen is the classic example used from Google. We know what, what, what behaviors and what and what activities are more likely to engage teams. 00:36:14.980 --> 00:36:22. Sally Smith So how do we use some of our HR19 infrastructure? Too often nudges and suggestive action in that direction. 00:36:23.040 --> 00:36:23. Timothy Bednall Umm? 00:36:24.120 --> 00:36:38. Sally Smith So that that goes from a one off insight or a static dashboard to people getting messages or emails or reminders that are more likely to to be constructive in terms of suggested behaviors.
