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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.
Second question could be around no. We we're keen to focus on maintaining and ensuring our colleagues are taking regular leave. So how do we set KP eyes and sort of track those through financial year?
Another day might be a being involved in some of the HR transformation work, so we don't send stand still and the work that we do with our underlying ITN reporting infrastructure. So it will be our cross HR project to look at at how do we upgrade some of our core HR information systems across the organization.
Timothy Bednall Fantastic, well my students at the moment are actually working on a kind of dashboard project, where they're essentially, and you know, working with well, actually made up data set and working out. I mean one is that they can actually take that data and look at the best way of presenting it so.
So that's, uh, that's awesome. I mean really interesting projects and you know many ways I. I guess you must feel a little bit like a detective. You know, in terms of, you know, looking at that data and being able to, you know, try to work out what the root causes of things like, you know, turnover and you know the accumulation of annual leave. You know Mark must be.
Sally Smith Yeah, absolutely a detective and I think one of the most exciting moments is when you find an interesting insight in a data set and no for those hours or days. You're the only person that knows it.
So and I always get to light up when we find something counter intuitive and I share it with a one of our stakeholders. And usually they lean in and say I'm really and then that leads to another question and good analytics. One question, one could question always leads to another. So it is a bit like a detective. As you say, a team. Or the other analogy, I would give you is it's like following the vein of caramel.
00:06:43.950 --> 00:07:00. Timothy Bednall I do like that analogy. I do have a bit of a sweet tooth so you know I do like that analogy as well. So what what's been, you know? One of the more interesting insights you know that you've discovered, you know, as a part of this process.
00:07:07.210 --> 00:07:22. Sally Smith There there are. There are quite a few, and for those of you that are keen to understand, some of the examples that now there are two IT news articles that features some of our projects so you could have a look at those as well.
To answer your question, one of the one of the most interesting pieces that we found was it it actually related to the example of the attrition in the call center. So when we started out, the number was very high.
The executive is in that area were particularly concerned about the level and because it was impacting impacting customer service, and it was also impacting some of the other metrics. Business performance metrics in that area. So they were keen to under to get underneath that from a perspective of, well, how do we improve the experience that our customers are have when they're calling in as well as creating a better environment for the teams to work in? Interestingly enough.
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
reminds me a little bit of the the metaphor of, you know, the blind men and the elephant. You know they and they're trying to feel it, and they go. Oh, this this feels like a spike and you know this feels like a rope and this feels like a you know hose, so yeah.
00:12:26.610 --> 00:12:52. Sally Smith Well, I guess I really I really like that and I think that's an excellent analogy in this circumstance because I think I I got energy when I saw people having conversations and exploring some of the challenges they had together. And the second aspect was it involved him in the process so they threw out all the ideas that we could test with analytics. So we had a nice long list of things to test. We prioritized it.
00:12:53.760 --> 00:13:05. Sally Smith And so back to your original question as to what was surprising when we got the results from doing their quantitative analysis, they were not what everyone thought.
00:13:05.310 --> 00:13:06. Timothy Bednall Me.
00:13:06.140 --> 00:13:18. Sally Smith So everyone's got fear. Was there something wrong with HR process? But actually the analysis showed that there's a measure in their call center of.
00:13:19.480 --> 00:13:23. Sally Smith Uh, so less stressful, stressful pressure, so great of service.
00:13:22.530 --> 00:13:24. Timothy Bednall Oh OK, OK.
00:13:24.710 --> 00:13:36. Sally Smith Yeah, so creative services and indicate it is generally an indicator of stress or pressure and when that gets out of a healthy range, that's when other metrics start to go haywire.
00:13:36.170 --> 00:13:48. Timothy Bednall Oh, I see. So sort of I over work and you know feeling. I mean like it was took too much pressure in terms of what they had to achieve, yeah.
00:13:48.160 --> 00:13:53. Sally Smith So essentially it's a function of race. Essentially it's a function of resourcing, so.
00:13:53.110 --> 00:13:53. Timothy Bednall Umm?
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 No, it's a, uh, what's the best way to describe it? A bit of a goat track if I'm honest.
00:16:46.950 --> 00:17:01. Sally Smith Uh, I'm in summary. I think I've so I started my career in consulting. I worked at Accenture in Change management. I really love that because as part in doing change management system implementation, you really understand what makes a business check.
00:17:02.450 --> 00:17:28. Sally Smith I then thought, OK, well where to from here so I followed what I really enjoyed at that point into learning and development at NAB. Went into organizational development and if I'm honest I'm always been probably a bit of a square peg in a round hole and HR. We have a reputation of being soft and fluffy and I like numbers and at some point I thought maybe I should be in finance.
00:17:29.610 --> 00:17:41. Sally Smith So I went to finance and one of the really lovely people in finance at the at the time described me as the most numerate person they've saved in HR, which is always typography.
00:17:42.310 --> 00:17:53. Sally Smith Uh, I would say that I learned in my short stint in finance that I really enjoy people and culture and and HR. And so I came back into.
00:17:54.160 --> 00:18:01. Sally Smith Uh, what was workforce reporting? Workforce analytics people analytics? The various iterations that it's been?
00:18:02.150 --> 00:18:32. Sally Smith I've always followed what I enjoy. I think when you realize in your career what gives you energy, then that's the path of caramel to follow in the ice cream and the yeah and I I I think I already described the the role where I started working on some of the complex projects is has been a highlight because they're often the sticky things that are difficult to solve for. And if you've also in the IO space 10 mil. Appreciate that some of them are very.
00:18:32.880 --> 00:18:34. Sally Smith Sticky to try and untangle.
00:18:34.900 --> 00:18:35. Timothy Bednall Absolutely.
00:18:35.110 --> 00:18:35. Sally Smith Uh.
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
Up, you know I, I'm not sure I'd necessarily want to go into that role list. The remuneration you know kind of matched with that so I can completely understand that that perspective so.
00:22:19.980 --> 00:22:32. Timothy Bednall So we won't even had time in now, but the at the moment, what, uh? What are some of the biggest challenges? I mean, that NAB is facing at the moment that you you bring in kind of in people analytics lens to.
00:22:33.850 --> 00:22:39. Sally Smith Probably, what would I? What would I say is how big is 1 now?
00:22:37.500 --> 00:22:38. Timothy Bednall Me.
00:22:40.510 --> 00:22:50. Sally Smith Something that is certainly topical, not sure I would write it. Write it as the biggest something that is topical is the return to the office in our post coverage.
00:22:48.100 --> 00:22:49. Timothy Bednall Oh yes, yeah.
00:22:50.920 --> 00:22:56. Sally Smith Or in a rather new covered normal so.
00:22:56.810 --> 00:23:00. Sally Smith There's probably a couple of examples here that might be interesting for everyone is.
00:23:01.550 --> 00:23:26. Sally Smith One is having a look at how our people were working at the moment in terms of our current work patterns. So we have obviously ways of survey in people to get a sense of preference as well as views on their current work experience. We're looking at ways we can draw on other data points to understand variation in work patterns so.
00:23:27.560 --> 00:23:38. Sally Smith How many? How many people? How many days? Sorry, do people tend to work in the office versus remotely, so there's an indicator of when someone goes in and in and out of the building.
00:23:39.180 --> 00:23:41. Sally Smith So we can get a sense of that.
00:23:41.790 --> 00:23:48. Sally Smith Uh, and the purpose of doing Daddy is to really shape up some decisions around.
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.
Timothy Bednall You know how employees are doing and and and and also you know how the the real organization differs from, you know, the the formal reporting lines that you know might actually look at, like in a, you know, formal organizational tree.
00:27:46.140 --> 00:28:03. Sally Smith I, I think that's one of the most fascinating projects that we've done over the last couple of years. Actually, the advantage of using signals from teams and outlook is that you don't have to interrupt employees with another survey. I don't know how many surveys.
00:27:49.670 --> 00:27:50. Timothy Bednall Umm?
00:28:04.660 --> 00:28:14. Sally Smith Reviews it now we use a lot and there are occasions where their high value because there's no other way of of getting insight around a particular topic.
00:28:15.310 --> 00:28:17. Sally Smith The UM, so we used.
00:28:17.480 --> 00:28:38. Sally Smith The essentially the metadata that comes from Outlook and teams to trend to monitor and follow the transition to remote working, and that was incredibly interesting because everyone had different theories around what would happen and it was different to reality and the.
00:28:39.920 --> 00:28:43. Sally Smith The added value that we did from that was directly combined.
00:28:45.060 --> 00:29:06. Sally Smith Schools and things that came from the surveys. We've team scores from the metadata and to to find hotspots around well being or where work related to be prioritized, or conversations needed to happen around resourcing. And that's not something that we could have done.
00:29:06.950 --> 00:29:15. Sally Smith Uh, without access to Andy ability to combine that sort of data set, one thing that I will touch on in regards to that though, is.
00:29:14.520 --> 00:29:15. Timothy Bednall Umm?
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 I think the.
00:31:49.650 --> 00:31:51. Sally Smith PM, uh, that's something I think.
00:31:53.030 --> 00:31:56. Sally Smith And it's not just in an employee since beginning customer sense as well.
00:31:57.040 --> 00:32:09. Sally Smith Uh, every organization is a is acutely acutely aware of, and one of the reasons why. Before we tackle anything, we do get the ethics and privacy overlay.
00:32:09.820 --> 00:32:24. Sally Smith On top of, UM, the team views on whether this passes. This is a project that passes the pub test counter thing. I'll add to that is probably early in my in the five or six years that I've worked in this area.
00:32:24.930 --> 00:32:29. Sally Smith Do you benefit sort was often from an organizational perspective, so.
00:32:29.300 --> 00:32:34. Sally Smith So uhm, now how do we manage cost around sick leave or or? How do we?
00:32:35.170 --> 00:32:38. Sally Smith Uh, manager, unlike aren't expected.
00:32:38.970 --> 00:32:39. Sally Smith Some plans.
00:32:39.900 --> 00:32:49. Sally Smith Uhm, absence in the context of, you know, work allocation will work well, so scheduling in some reparation of teams and what have you.
00:32:50.800 --> 00:33:11. Sally Smith I think now there is more focus on optimizing for organizational and individual outcomes. So if we're looking to understand what great bankers do differently, great bankers have a balance of outcomes for our customers, so more satisfied customers.
00:33:11.900 --> 00:33:17. Sally Smith It's also about financial returns as well as their well being and engagement, so.
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.
Timothy Bednall Umm?
00:36:40.360 --> 00:36:49. Sally Smith But those are still on the reporting and, and I mean, we're still under reporting and trying to drive maturity and improved data quality.
00:36:49.330 --> 00:36:50. Timothy Bednall Umm?
00:36:49.690 --> 00:37:00. Sally Smith There's there's plenty of work to be done in that space, and I don't know about you, but I don't know anyone that says we've got great quality data. It's all in order. Great dashboards. It's wonderful.
00:36:59.490 --> 00:37:00. Timothy Bednall Yeah.
00:37:01.120 --> 00:37:16. Sally Smith Ummm, but where we're looking to bring in more specialist data management data engineers to get to get, I think a better setup or better people Delta engine to make that time to insight quicker.
00:37:02.310 --> 00:37:02. Timothy Bednall But
00:37:17.210 --> 00:37:24. Sally Smith So obviously specialist skills and broader non HR expertise will be required both in data engineering.
00:37:25.670 --> 00:37:27. Sally Smith Good reporting and good statistics.
00:37:28.220 --> 00:37:53. Timothy Bednall so so actually a lot more collaboration between HR and you know other types of professionals such as data scientists and data engineers. And I mean database experts and and that that kind of thing. And I suppose with a a large organization like NAB, you've probably got a lot of you know different pockets of data that we're kind of always set up independently.
00:37:53.640 --> 00:38:03. Timothy Bednall And they don't really talk to one another and I get. I guess it must be quite a job to, you know, bring all of those different sources of data together and to match them up and get them to talk to one another.
