1 00:00:00,080 --> 00:00:03,720 Data and talent are foundational to business. 2 00:00:03,720 --> 00:00:07,720 Today we're speaking with Caroline O'Reilly, the General 3 00:00:07,720 --> 00:00:10,960 Manager of People Analytics at Work Day. 4 00:00:11,360 --> 00:00:14,840 People analytics is all around discovering meaningful patterns 5 00:00:14,840 --> 00:00:18,320 in your people data and you do that to be able to improve the 6 00:00:18,320 --> 00:00:20,960 employee experience and to really accelerate the decision 7 00:00:20,960 --> 00:00:23,440 making in the business. When I talk to business leaders, 8 00:00:23,440 --> 00:00:26,910 the first thing they tell me is. There's just a sea of data out 9 00:00:26,910 --> 00:00:29,670 there and the data is fragmented and it's siloed throughout the 10 00:00:29,670 --> 00:00:32,710 organization and it's just getting bigger all the time. 11 00:00:32,710 --> 00:00:35,150 I heard somebody recently say they were drowning in data in 12 00:00:35,150 --> 00:00:37,270 the organization. It's very, very hard to know 13 00:00:37,270 --> 00:00:38,870 where to get the right insights from. 14 00:00:38,910 --> 00:00:40,830 And there's really a bit of FOMO as well, right? 15 00:00:40,830 --> 00:00:43,150 The fear of missing out on insights that you may not have 16 00:00:43,150 --> 00:00:45,030 discovered. It's why a lot of business 17 00:00:45,030 --> 00:00:48,990 leaders are looking to use AI and ML to automatically surface 18 00:00:48,990 --> 00:00:51,670 those insights that you may have missed in your own people data. 19 00:00:51,670 --> 00:00:54,190 Another challenge that when we talk to business leaders that 20 00:00:54,190 --> 00:00:56,870 they really want to solve. Is to get more and more people 21 00:00:56,870 --> 00:01:00,390 comfortable with data that data democratization and making data 22 00:01:00,390 --> 00:01:03,790 more accessible for people. Caroline, you've described some 23 00:01:03,790 --> 00:01:08,990 of the customer challenges when it comes to people analytics. 24 00:01:08,990 --> 00:01:12,950 What are the components of the people analytics of these kinds 25 00:01:12,950 --> 00:01:15,830 of products? Enabling our business leaders to 26 00:01:15,830 --> 00:01:18,670 accelerate their decision making, really advising leaders 27 00:01:18,670 --> 00:01:21,580 on how to make better decisions. And usually the way that works, 28 00:01:21,580 --> 00:01:24,420 we come up with some hypothesis that we want to prove or 29 00:01:24,420 --> 00:01:26,620 investigate, pulling that data together. 30 00:01:27,060 --> 00:01:30,860 And as we try and solve more complex questions in each 31 00:01:30,860 --> 00:01:34,340 business, it's about bringing in 3rd party data and splicing that 32 00:01:34,340 --> 00:01:37,020 and mixing that with your people data to be able to answer 33 00:01:37,220 --> 00:01:39,740 questions that the business has about their people and about the 34 00:01:39,740 --> 00:01:42,180 business. The real trends that we see are 35 00:01:42,340 --> 00:01:45,060 getting that data in the hands of the decision makers much, 36 00:01:45,060 --> 00:01:48,860 much faster than it has been before getting it in real time. 37 00:01:49,460 --> 00:01:52,620 And getting that data to the people who can take action on it 38 00:01:52,620 --> 00:01:54,380 as well. It sounds like people in 39 00:01:54,380 --> 00:01:59,340 Business Today have a greater recognition of the importance of 40 00:01:59,340 --> 00:02:02,260 data than they did in the past. We've moved much more from 41 00:02:02,260 --> 00:02:06,300 reporting to thinking about having agile tools that can move 42 00:02:06,300 --> 00:02:08,180 with the business. I think we really saw this in 43 00:02:08,180 --> 00:02:11,300 COVID, right, where we had to move really quickly to answer 44 00:02:12,020 --> 00:02:15,100 business questions much, much faster than we ever did before. 45 00:02:15,300 --> 00:02:20,240 So what are the strategic questions that people analytics 46 00:02:20,280 --> 00:02:24,040 ultimately helps address? I suppose there are common 47 00:02:24,040 --> 00:02:27,360 questions that businesses are asking, like how are people 48 00:02:27,360 --> 00:02:31,400 using our offices and how are people feeling if they're 49 00:02:31,400 --> 00:02:34,280 working completely remotely or they're working in a hybrid 50 00:02:34,280 --> 00:02:35,800 mode? What skills do we have in the 51 00:02:35,800 --> 00:02:38,240 organization? What skills gaps do we have? 52 00:02:38,320 --> 00:02:41,640 There are a core set of questions that all our customers 53 00:02:41,640 --> 00:02:43,740 are asking. But then there's also those 54 00:02:43,740 --> 00:02:46,220 unique questions that for instance, you know for customers 55 00:02:46,220 --> 00:02:48,740 doing an MA or something, that's a unique thing that's happening 56 00:02:48,740 --> 00:02:50,940 in their business and they should have agile tools that can 57 00:02:50,940 --> 00:02:53,020 enable them to answer those data questions. 58 00:02:53,180 --> 00:02:58,180 Let's drill into the data. So much of this is completely 59 00:02:58,380 --> 00:03:02,100 interwoven and dependent on the data. 60 00:03:02,820 --> 00:03:07,420 What kinds of data feed into people analytics? 61 00:03:07,930 --> 00:03:10,210 Can be any data that's related to your people. 62 00:03:10,210 --> 00:03:13,650 So it could be listening data, it could be skills data. 63 00:03:13,650 --> 00:03:17,730 It's stated by performance. It's stated by org levels and 64 00:03:17,730 --> 00:03:20,810 org design. We've heard that it can take up 65 00:03:20,810 --> 00:03:24,890 to 30 days to create, for instance, a deck for the CHRO. 66 00:03:24,890 --> 00:03:28,290 And let's get into a cadence of creating that same deck every 67 00:03:28,290 --> 00:03:31,370 month perhaps. And when we talk to business 68 00:03:31,370 --> 00:03:33,770 leaders, they want to get that done in an automated way. 69 00:03:33,850 --> 00:03:36,290 And that's where they use tools like people analytics to be able 70 00:03:36,290 --> 00:03:38,860 to surface that. And so this is where they can 71 00:03:38,860 --> 00:03:43,900 see data around or composition around diversity, inclusion. 72 00:03:44,180 --> 00:03:46,780 You might want to ask a question, what's your female 73 00:03:46,780 --> 00:03:48,540 leadership look like in the organization? 74 00:03:48,540 --> 00:03:51,180 Or you may want to look at female representation as you're 75 00:03:51,180 --> 00:03:53,780 doing promotions or you may want to look at retention and 76 00:03:53,780 --> 00:03:56,260 attrition. And these are core pieces of 77 00:03:56,260 --> 00:03:59,540 people analytics that are almost standard in many different 78 00:03:59,540 --> 00:04:03,220 organizations. 1 aspect is it's very core sets of data that are 79 00:04:03,220 --> 00:04:05,580 shared through the different companies that we talk to. 80 00:04:05,910 --> 00:04:09,310 But then on the other aspect, there's people questions that 81 00:04:09,310 --> 00:04:12,030 businesses want to ask that are very unique to their business, 82 00:04:12,030 --> 00:04:14,630 where they want to take in listening data, where they want 83 00:04:14,630 --> 00:04:17,510 to take in external data sets like benchmarks. 84 00:04:17,550 --> 00:04:20,070 And that's where they need to blend that data together and get 85 00:04:20,070 --> 00:04:23,270 those really rich data sets like we answer their unique 86 00:04:23,270 --> 00:04:25,310 challenges and their unique business questions that they 87 00:04:25,310 --> 00:04:28,110 have. Caroline, you mentioned employee 88 00:04:28,110 --> 00:04:33,910 experience several times. Where does people analytics fit 89 00:04:33,990 --> 00:04:36,190 into creating a better employee experience? 90 00:04:36,430 --> 00:04:38,870 During COVID, when we all moved to remote work, there was 91 00:04:38,870 --> 00:04:42,070 someone who had just started. That week sitting beside me in 92 00:04:42,070 --> 00:04:44,110 the office and I remember turning to him and saying like, 93 00:04:44,110 --> 00:04:46,150 are you going to be OK? And just started this week in 94 00:04:46,150 --> 00:04:48,430 the office. It really helped to have in the 95 00:04:48,430 --> 00:04:51,710 employee listening to. So we sent out a survey every 96 00:04:51,710 --> 00:04:54,590 week to our employees. It's very powerful to hear from 97 00:04:54,590 --> 00:04:57,710 employees in their own language what they're feeling and what 98 00:04:57,710 --> 00:04:59,830 they're concerned about. And so you can pull all that 99 00:04:59,830 --> 00:05:02,070 data together. So I can see a heat map of where 100 00:05:02,070 --> 00:05:04,030 I need to focus. That's really powerful and that 101 00:05:04,030 --> 00:05:05,910 really improves the employee experience. 102 00:05:05,990 --> 00:05:12,540 How does people analytics help you connect with these folks 103 00:05:12,540 --> 00:05:16,100 that are working remotely? It helps us connect by answering 104 00:05:16,140 --> 00:05:18,300 business questions that we have. For instance, when we came back 105 00:05:18,300 --> 00:05:22,060 to the workplace, we wanted to know that the people who were 106 00:05:22,060 --> 00:05:25,260 working remotely feel more connected than the people who 107 00:05:25,260 --> 00:05:28,620 were in the office. And by using people analytics, 108 00:05:28,620 --> 00:05:32,620 we were able to determine that we were trying to navigate how 109 00:05:32,620 --> 00:05:34,140 often should we come into the office. 110 00:05:34,300 --> 00:05:38,660 We looked at how people focus. We took in zoom data to see how 111 00:05:38,820 --> 00:05:41,230 much. Meetings people were doing when 112 00:05:41,230 --> 00:05:43,550 they were in the office versus how many meetings they were 113 00:05:43,550 --> 00:05:46,350 doing when they were at home. And what we discovered was that 114 00:05:46,630 --> 00:05:49,030 people needed focused time. They didn't get focused time 115 00:05:49,030 --> 00:05:51,870 when there were multiple zooms. So people needed a hybrid 116 00:05:51,870 --> 00:05:55,110 approach of time to focus to actually finish things. 117 00:05:55,110 --> 00:05:59,070 And so it was using our own data that we decided we would do a 118 00:05:59,070 --> 00:06:02,870 5050 in the office, 5050 working, you know, in a flex 119 00:06:02,870 --> 00:06:05,510 way. So by using our own data, we 120 00:06:05,510 --> 00:06:06,870 came up with that recommendation. 121 00:06:06,870 --> 00:06:09,230 Caroline, we hear a lot about skills. 122 00:06:10,260 --> 00:06:13,500 What do we mean by that? People and skills are like this 123 00:06:13,500 --> 00:06:16,740 now, right? And companies are really using 124 00:06:16,740 --> 00:06:19,940 skills to totally supercharge their organization. 125 00:06:19,980 --> 00:06:23,300 We're moving to skills based hiring and you've probably heard 126 00:06:23,300 --> 00:06:26,620 the the phrase, you know, quite quitting like last year. 127 00:06:27,100 --> 00:06:30,900 But we've moved to this quiet hiring which is talking about 128 00:06:30,900 --> 00:06:32,980 hiring in a different way than we did before. 129 00:06:33,060 --> 00:06:36,340 Previously our hiring was very rigid and that we put out our 130 00:06:36,340 --> 00:06:38,460 job, right. We put out our advertisements. 131 00:06:38,870 --> 00:06:41,230 But moving to skills based hiring is really looking for 132 00:06:41,230 --> 00:06:45,670 people with the skills and maybe they don't have the university 133 00:06:45,670 --> 00:06:49,270 degree or maybe they don't have certain X years experience, but 134 00:06:49,270 --> 00:06:52,110 do they have the skills? When we talk to companies, I 135 00:06:52,110 --> 00:06:56,550 feel the ones who are moving to a skills based hiring and skills 136 00:06:56,550 --> 00:06:59,390 based talent are really supercharging their 137 00:06:59,390 --> 00:07:01,150 organization. It's really helping them to 138 00:07:01,190 --> 00:07:04,240 internally recruit. It's really helping them to look 139 00:07:04,240 --> 00:07:09,320 at their mentoring, it's helping them to re skill their workforce 140 00:07:09,320 --> 00:07:12,000 because with the talent shortage that there is, we can't go out 141 00:07:12,000 --> 00:07:15,080 and hire all these people. So by being more granular about 142 00:07:15,080 --> 00:07:19,480 what skills you need you can really get the best talent from 143 00:07:19,480 --> 00:07:21,040 maybe somewhere that you didn't expect. 144 00:07:21,720 --> 00:07:23,760 Another thing I love about skills based hiring is that. 145 00:07:24,140 --> 00:07:26,460 It really is starting to take the bias out of hiring. 146 00:07:26,460 --> 00:07:28,740 You know you're looking for the skills and then that's open to 147 00:07:28,740 --> 00:07:31,220 everybody. It's really the future of hiring 148 00:07:31,220 --> 00:07:34,980 as we see it, Caroline. Some people say that skills are 149 00:07:34,980 --> 00:07:38,340 the next evolution of people analytics. 150 00:07:38,340 --> 00:07:41,580 Can you tell us about that? The way we hire people, The way 151 00:07:41,580 --> 00:07:43,620 we think about retraining people. 152 00:07:43,620 --> 00:07:45,700 The way we think about mentoring people. 153 00:07:46,340 --> 00:07:48,460 We're looking at that from a skills perspective. 154 00:07:48,580 --> 00:07:51,580 A company who's done this really well actually is a censure. 155 00:07:51,580 --> 00:07:54,340 They've really supercharged how they use skills in the 156 00:07:54,340 --> 00:07:57,500 organization and they know what skills they have and they don't 157 00:07:57,500 --> 00:08:01,700 do that from an employee saying I have XYZ skills. 158 00:08:02,020 --> 00:08:05,700 They apply that based on what the employee has done and what 159 00:08:05,700 --> 00:08:09,860 projects they have done, and they use this then to do much 160 00:08:09,860 --> 00:08:13,540 faster replanning of the skills that they need and. 161 00:08:13,860 --> 00:08:15,700 Who they need to reskill as well. 162 00:08:15,700 --> 00:08:18,700 So I think like Accenture are a great example of how to 163 00:08:18,700 --> 00:08:20,780 supercharge your organization with skills. 164 00:08:20,940 --> 00:08:25,300 Caroline, let's talk about customer use cases. 165 00:08:25,460 --> 00:08:30,620 Can you give us some practical examples of how organizations, 166 00:08:30,620 --> 00:08:34,460 maybe in different industries, are using people analytics? 167 00:08:34,929 --> 00:08:37,850 People analytics is key for me because I'm a product leader and 168 00:08:37,850 --> 00:08:41,370 engineer and leader, but I'm also a people leader and so I 169 00:08:41,370 --> 00:08:45,050 always want the data about how I am as a people leader and also 170 00:08:45,050 --> 00:08:47,730 how our product is right. So I I need data from both 171 00:08:47,730 --> 00:08:49,690 perspectives. So we're very passionate about 172 00:08:49,690 --> 00:08:52,730 people analytics in work day. Our people analytics team comes 173 00:08:52,730 --> 00:08:56,050 up with hypothesis that we want to answer by looking at our 174 00:08:56,050 --> 00:08:59,090 people data and work day. And one of the questions that 175 00:08:59,090 --> 00:09:02,930 people leaders often ask is why do people stay in a team or why 176 00:09:02,930 --> 00:09:06,090 do they stay in a company? And we've all heard the 177 00:09:06,650 --> 00:09:09,210 hypothesis around that, oh, people stay because of their 178 00:09:09,210 --> 00:09:13,210 manager, or people stay because of their compensation, or people 179 00:09:13,210 --> 00:09:15,610 stay because, you know, they feel challenged at work. 180 00:09:15,610 --> 00:09:18,970 And we wanted to actually go into the data and really figure 181 00:09:18,970 --> 00:09:21,010 it out. And they discovered that the 182 00:09:21,010 --> 00:09:25,450 core reason for people to stay out work day was the ones who 183 00:09:25,450 --> 00:09:29,570 had a really challenging, a really valuable career 184 00:09:29,570 --> 00:09:32,280 conversation with their manager. And that was something that 185 00:09:32,280 --> 00:09:35,080 surprised us because we've always thought that it was maybe 186 00:09:35,080 --> 00:09:37,040 the manager or maybe it's the work that they were doing. 187 00:09:37,040 --> 00:09:40,000 But it was around having great career conversations. 188 00:09:40,040 --> 00:09:43,000 And that discovery was so important from our people 189 00:09:43,000 --> 00:09:48,640 analytics team that we have injected in career conversations 190 00:09:48,640 --> 00:09:51,560 during the year that all of us have with our managers because 191 00:09:51,840 --> 00:09:54,680 we saw in the data that it was so important to have that 192 00:09:54,760 --> 00:09:57,320 another company is lease Plan and lease plan are a Dutch 193 00:09:57,320 --> 00:09:59,640 financial services provider for fleet management. 194 00:10:00,220 --> 00:10:03,380 They are a global organization. They're in 32 different 195 00:10:03,380 --> 00:10:06,340 countries and they have completely transformed their 196 00:10:06,340 --> 00:10:09,740 hatred operations. They really wanted all their 197 00:10:09,740 --> 00:10:12,540 hatred operations across those different entities to come 198 00:10:12,540 --> 00:10:16,300 together to be on the same baseline and to give them the 199 00:10:16,300 --> 00:10:18,780 real insights that they needed to make their decisions. 200 00:10:19,500 --> 00:10:23,060 They wanted to ask questions like how do our employees feel 201 00:10:23,060 --> 00:10:25,340 when they are on board and do they feel different in one 202 00:10:25,340 --> 00:10:28,460 country over the other? Why do people leave? 203 00:10:29,250 --> 00:10:31,610 How are we recruiting in different countries and how is 204 00:10:31,610 --> 00:10:34,730 that different? And they really wanted to get 205 00:10:34,890 --> 00:10:38,210 those insights into the hands of the people leaders and HR 206 00:10:38,210 --> 00:10:41,570 business partners, so they could be armed with the data that they 207 00:10:41,570 --> 00:10:45,010 needed. If you look across these 208 00:10:45,090 --> 00:10:48,970 particular use cases or other ones I know you speak with so 209 00:10:48,970 --> 00:10:54,410 many work day customers, are there a common set of 210 00:10:54,410 --> 00:11:01,040 SuccessFactors or attributes that drive a successful people 211 00:11:01,040 --> 00:11:03,480 analytics implementation or deployment? 212 00:11:03,960 --> 00:11:07,840 It's the companies who want to get the data into the hands of 213 00:11:07,840 --> 00:11:10,360 decision makers. Now we've had companies who have 214 00:11:10,360 --> 00:11:12,840 moved from a model where they have been extracting data from 215 00:11:12,840 --> 00:11:16,080 different entities and pulling it together and they immediately 216 00:11:16,080 --> 00:11:18,080 felt. The state is old already. 217 00:11:18,080 --> 00:11:21,200 We want to transform our business to be able to get that 218 00:11:21,200 --> 00:11:23,320 data into the hands of decision makers. 219 00:11:23,320 --> 00:11:26,560 Now we're moving away from just a core set of people 220 00:11:26,560 --> 00:11:28,560 understanding the data. We want to have that data 221 00:11:28,560 --> 00:11:31,880 democratization, where more and more people in the organization 222 00:11:32,320 --> 00:11:35,080 can interpret these results. And that's why we spend a lot of 223 00:11:35,080 --> 00:11:38,240 effort into writing a story around the data and writing a 224 00:11:38,240 --> 00:11:41,480 narrative around the data. So the HRBP or the business 225 00:11:41,480 --> 00:11:43,940 leader? Can understand what that data is 226 00:11:43,940 --> 00:11:46,820 telling them and what that insight is telling them and then 227 00:11:46,820 --> 00:11:49,660 they can drill down and do another level of analysis for 228 00:11:49,660 --> 00:11:52,660 themselves. So that frees up then the 229 00:11:52,700 --> 00:11:55,460 analyst team to work on other strategic questions that the 230 00:11:55,460 --> 00:11:58,580 business needs to ask and it gets the data into the hands of 231 00:11:58,580 --> 00:12:01,580 more people and and empowers them. 232 00:12:01,580 --> 00:12:04,020 That's where I see the company is making the biggest impact, 233 00:12:04,020 --> 00:12:06,180 more and more people having access to the data, having 234 00:12:06,180 --> 00:12:08,340 access to it. Now, it sounds like you're 235 00:12:08,340 --> 00:12:15,340 talking about creating a data centric mindset or culture where 236 00:12:15,460 --> 00:12:19,460 data is infused or used throughout the organization to 237 00:12:19,460 --> 00:12:23,620 help support the business strategy and help folks make 238 00:12:23,620 --> 00:12:27,260 key, important decisions. You don't get there overnight. 239 00:12:27,260 --> 00:12:29,580 The companies who are starting out on that journey, what 240 00:12:29,580 --> 00:12:32,340 they'll usually do is start off with a small team or a tiger 241 00:12:32,340 --> 00:12:34,260 team who are interested in analytics. 242 00:12:34,840 --> 00:12:37,880 And they will start implementing areas that they're interested 243 00:12:37,880 --> 00:12:39,440 in. Imagine they have a hypothesis 244 00:12:39,440 --> 00:12:43,600 around diversity inclusion and they may implement a product 245 00:12:43,640 --> 00:12:46,680 like people analytics, which shows them some key insights and 246 00:12:46,680 --> 00:12:48,880 trends around diversity inclusion. 247 00:12:49,280 --> 00:12:51,000 They will come together maybe that month. 248 00:12:51,000 --> 00:12:53,280 They will focus on that data set. 249 00:12:53,800 --> 00:12:57,360 They will enable those leaders to do the next drill down and 250 00:12:57,360 --> 00:13:00,160 then over time those leaders become really, you know, 251 00:13:00,160 --> 00:13:02,910 proficient in. Interpreting that data 252 00:13:02,910 --> 00:13:05,630 themselves and then they widen it out to more of the business. 253 00:13:05,630 --> 00:13:07,710 By pulling together these different data sets and by 254 00:13:07,710 --> 00:13:11,070 blending different people data together, you can really uncover 255 00:13:11,070 --> 00:13:13,790 and discover fascinating insights into your organization 256 00:13:13,790 --> 00:13:16,150 and your business. I can tell you the most 257 00:13:16,150 --> 00:13:20,470 innovative companies that I speak with talk about it just 258 00:13:20,470 --> 00:13:24,230 this way. Creating that data mindset, the 259 00:13:24,230 --> 00:13:29,070 democratization of data. You're a general manager of 260 00:13:29,070 --> 00:13:32,270 analytics at Workday. Tell us about these tools. 261 00:13:32,350 --> 00:13:34,710 We have a number of tools around analytics. 262 00:13:34,710 --> 00:13:38,030 The first one is people analytics and I always describe 263 00:13:38,030 --> 00:13:40,550 people analytics as an analyst in the box. 264 00:13:40,630 --> 00:13:44,550 It's a prebuilt application that is surfacing trends around your 265 00:13:44,550 --> 00:13:45,990 people data. So it is running in the 266 00:13:45,990 --> 00:13:49,870 background, surfacing people trends for you and is also. 267 00:13:50,240 --> 00:13:53,560 Surfacing anomalies that you may not have seen and it uses ML and 268 00:13:53,560 --> 00:13:56,400 AI to do that in the background. So it's telling you where you 269 00:13:56,400 --> 00:14:00,480 may need to focus on diversity, inclusion on or composition, on 270 00:14:00,480 --> 00:14:02,960 hiring or on talent or on skills. 271 00:14:03,520 --> 00:14:06,200 And so the wonderful thing with people analytics is that it 272 00:14:06,200 --> 00:14:08,160 gives you a story around the trend. 273 00:14:08,160 --> 00:14:11,840 So first of all, you see the trend, you see the story and 274 00:14:11,840 --> 00:14:15,200 then it's tightly woven into Workday, so you can see the data 275 00:14:15,200 --> 00:14:17,760 behind that story. So you can drill in right into 276 00:14:17,760 --> 00:14:20,520 the workday data. And slice that how you want to 277 00:14:20,520 --> 00:14:23,600 be able to see where that story came from because that's the 278 00:14:23,600 --> 00:14:25,880 important thing about data. We always want to be able to 279 00:14:25,880 --> 00:14:28,720 explain where that data came from and where that insight came 280 00:14:28,720 --> 00:14:30,880 from. The other product that we have 281 00:14:30,880 --> 00:14:33,320 is called Prism Analytics. You asked me about people 282 00:14:33,320 --> 00:14:35,280 analytics and the different data that that involves. 283 00:14:35,520 --> 00:14:38,040 That could be any data that you want to blend with your people 284 00:14:38,040 --> 00:14:40,600 data now. And the way that customers want 285 00:14:40,600 --> 00:14:42,880 to ask questions about their people data is just fast and 286 00:14:42,880 --> 00:14:46,200 growing and unique. And so Prism analytics allows 287 00:14:46,200 --> 00:14:49,640 you to pull in 3rd party data that you can blend with your 288 00:14:49,640 --> 00:14:52,360 people data to ask those unique business questions that you may 289 00:14:52,360 --> 00:14:54,040 have. And the reason it's so important 290 00:14:54,040 --> 00:14:56,400 is that the people data is so sensitive. 291 00:14:56,400 --> 00:15:00,200 We are hyper focused on security of that data in work day. 292 00:15:00,240 --> 00:15:03,480 That's our number one priority. Our customers really make that 293 00:15:03,480 --> 00:15:06,960 strategic decision to keep that people data in work day, but 294 00:15:06,960 --> 00:15:09,640 they want to blend it with third party data and using Prism 295 00:15:09,640 --> 00:15:12,520 analytics you can blend that third party data and bring that 296 00:15:12,520 --> 00:15:14,850 third party data in. Which are people data. 297 00:15:15,050 --> 00:15:19,370 Another product that we have is Pecan Employee Voice and that's 298 00:15:19,370 --> 00:15:24,370 a way of surveying your employees and Pecan tells you 299 00:15:24,370 --> 00:15:28,930 where to focus either on a Geo level or on a managerial level 300 00:15:28,930 --> 00:15:32,250 or where you need to focus on aspects of your people data. 301 00:15:32,330 --> 00:15:35,970 We also have our core reporting functionality that's in the box 302 00:15:35,970 --> 00:15:39,250 in Workday, which enables you to create your own reports and ad 303 00:15:39,250 --> 00:15:41,570 hoc reports and you can build your dashboards as well. 304 00:15:41,910 --> 00:15:45,270 Caroline, you mentioned AI and machine learning. 305 00:15:45,710 --> 00:15:50,630 Can you tell us where these approaches fit into analytics at 306 00:15:50,630 --> 00:15:54,670 Workday? Data is just getting to be so 307 00:15:54,670 --> 00:15:56,950 vast and there's so many different data points it is 308 00:15:56,950 --> 00:15:59,190 going to become increasingly hard to. 309 00:15:59,500 --> 00:16:02,020 Go through that data in a manual way and surface insight. 310 00:16:02,020 --> 00:16:04,980 So more and more our business leaders are going to want to use 311 00:16:05,100 --> 00:16:07,820 ML and AI to be able to surface those insights because there's 312 00:16:07,820 --> 00:16:09,540 just so many different data points now. 313 00:16:09,580 --> 00:16:12,180 We've been using ML and AI for almost a decade now. 314 00:16:12,180 --> 00:16:15,140 One of the places we use it is in Skills Cloud, which is a 315 00:16:15,140 --> 00:16:17,420 taxonomy of your skills data. Now. 316 00:16:17,460 --> 00:16:20,260 It's not a static taxonomy of your skills. 317 00:16:20,260 --> 00:16:23,420 It's using ML and AI to grow over time and to learn and to 318 00:16:23,420 --> 00:16:26,890 evolve. We also are using ML and AI in 319 00:16:26,890 --> 00:16:29,250 our people analytics too and what we call our storyteller 320 00:16:29,250 --> 00:16:31,290 engine. So when we talked about the 321 00:16:31,290 --> 00:16:33,810 people data and the points that you need to be able to discover 322 00:16:33,810 --> 00:16:37,410 those insights, it's fast. And so we run ML and AI over 323 00:16:37,410 --> 00:16:40,650 your work day people data to surface those trends to you. 324 00:16:40,850 --> 00:16:44,370 Trends about diversity, trends around your skills, data trends 325 00:16:44,370 --> 00:16:46,850 around recruiting or your org composition. 326 00:16:47,380 --> 00:16:50,140 And we're showing you how you compare to your wider 327 00:16:50,140 --> 00:16:53,740 organization as well. And I see Mlnai is really going 328 00:16:53,740 --> 00:16:55,860 to help us with that automation of tasks. 329 00:16:55,980 --> 00:16:59,340 We often hear people tell us It takes me 30 days to go through 330 00:16:59,340 --> 00:17:03,140 my people data and to create a package for the OCHRO about the 331 00:17:03,140 --> 00:17:05,500 people trends. You know they want to move to 332 00:17:05,500 --> 00:17:07,660 tools like people on IT, which will do this for them in an 333 00:17:07,660 --> 00:17:10,819 automated way that they can export to presentation for the 334 00:17:11,140 --> 00:17:13,020 CHRO. But also more importantly, it 335 00:17:13,020 --> 00:17:16,140 surfaces those anomalies you may have missed if you were doing it 336 00:17:16,140 --> 00:17:19,579 in the same way every month. Caroline, you mentioned security 337 00:17:19,579 --> 00:17:24,140 and privacy earlier. How can organizations protect 338 00:17:24,140 --> 00:17:29,060 this very important employee confidential data, but at the 339 00:17:29,060 --> 00:17:33,460 same time take advantage of the capabilities that are available 340 00:17:33,660 --> 00:17:37,620 with analytics tools? That's such an important part of 341 00:17:37,620 --> 00:17:40,700 people analytics, Michael, and it's one that we take really 342 00:17:40,700 --> 00:17:44,500 seriously is around the security of this data that we are hosting 343 00:17:44,500 --> 00:17:47,060 on behalf of our customers. We are very careful about. 344 00:17:47,060 --> 00:17:52,020 That's PII data obviously. And that is why we have infused 345 00:17:52,020 --> 00:17:54,940 our People Analytics product into the core of Workday, 346 00:17:54,940 --> 00:17:57,700 because we don't want to have our customers have to extract 347 00:17:57,700 --> 00:18:00,260 that data bringing into a pipeline manipulated. 348 00:18:01,000 --> 00:18:03,440 With people analytics, it's actually infused into the core 349 00:18:03,440 --> 00:18:05,240 of Workday. So you can protect that with 350 00:18:05,240 --> 00:18:08,080 your Workday security model, the one that you're used to using 351 00:18:08,080 --> 00:18:11,160 already, so that you're protecting your PII data as you 352 00:18:11,160 --> 00:18:13,280 always have done with the Workday security model. 353 00:18:13,760 --> 00:18:17,520 And that means you're only surfacing that data in reports 354 00:18:17,520 --> 00:18:20,280 to people who should see that. And that's a really core 355 00:18:20,280 --> 00:18:23,240 fundamental part of what we do in Workday and what we do in our 356 00:18:23,240 --> 00:18:24,800 People Analytics product as well. 357 00:18:25,180 --> 00:18:28,300 So you're simplifying that data pipeline and obviously 358 00:18:28,540 --> 00:18:31,620 simplicity when it comes to anything to do with security and 359 00:18:31,620 --> 00:18:35,780 privacy is really important. That's exactly it, Michael. 360 00:18:35,780 --> 00:18:38,220 For our people analytics product, that's out-of-the-box. 361 00:18:38,220 --> 00:18:40,180 So you don't even have to see the pipeline. 362 00:18:40,180 --> 00:18:42,980 We do that for you. It's all contained in work day 363 00:18:43,180 --> 00:18:45,940 and then you can apply your work day security model on top of 364 00:18:45,940 --> 00:18:47,820 that. Otherwise you might have to 365 00:18:47,820 --> 00:18:49,860 extract that. You would have to audit that. 366 00:18:49,860 --> 00:18:52,460 And what we hear from customers, if they do have people data 367 00:18:52,460 --> 00:18:55,730 elsewhere, they have to. Manage that security, they have 368 00:18:55,730 --> 00:18:58,370 to replicate it. We simplify all that for our 369 00:18:58,370 --> 00:18:59,970 customers. We keep it in the core. 370 00:18:59,970 --> 00:19:02,570 You secure it in the same way that you normally do for your 371 00:19:02,570 --> 00:19:05,050 workday data, and it's all protected and working. 372 00:19:05,410 --> 00:19:09,290 Caroline, we spoke earlier about building a data centric culture. 373 00:19:09,530 --> 00:19:13,330 How important is that to organizations that want to take 374 00:19:13,330 --> 00:19:16,410 full advantage of the data that's available? 375 00:19:16,920 --> 00:19:18,880 It's super important. The people on the next team 376 00:19:18,880 --> 00:19:21,520 inside of Work Day, they're run by Phil Wilburn, who's a great 377 00:19:21,520 --> 00:19:24,880 friend of mine, a really great leader and they take a very 378 00:19:24,880 --> 00:19:29,160 product oriented view when they are creating dashboards for me, 379 00:19:29,160 --> 00:19:31,720 for instance. So they spend a lot of time 380 00:19:31,720 --> 00:19:34,720 investing in what is the product, what is the dashboard. 381 00:19:34,720 --> 00:19:37,880 We're delivering for all the people leaders in Work Day and 382 00:19:37,880 --> 00:19:40,240 they go out and they do interviews with us. 383 00:19:40,240 --> 00:19:43,000 They're really trying to get to the core of what is the data 384 00:19:43,000 --> 00:19:45,160 that Caroline needs to run our organization. 385 00:19:45,520 --> 00:19:47,600 How does she need it? How does she need to splice and 386 00:19:47,600 --> 00:19:49,840 dice it? And they assign actually a 387 00:19:49,840 --> 00:19:53,240 product owner to each of these dashboards because what they do 388 00:19:53,240 --> 00:19:57,000 not want to do is spend so much time and then for it not to be 389 00:19:57,000 --> 00:19:59,720 adopted. So that team really invests in 390 00:19:59,720 --> 00:20:03,760 the upfront requirements gathering of what that dashboard 391 00:20:03,760 --> 00:20:06,880 is that they're going to create. So that when we all get it into 392 00:20:06,880 --> 00:20:09,080 our hands that we are going to adopt it and use it. 393 00:20:09,080 --> 00:20:12,680 And I think that's a really good model to use as you start your 394 00:20:12,680 --> 00:20:15,000 people on that externality a lot of upfront. 395 00:20:15,320 --> 00:20:17,680 Designing that goes into it to make sure it's going to be 396 00:20:17,680 --> 00:20:20,520 adopted. It sounds like adoption 397 00:20:20,640 --> 00:20:28,320 ultimately relies on having empathy for the end user. 398 00:20:28,320 --> 00:20:33,240 What they need to accomplish? How will that data be useful? 399 00:20:33,640 --> 00:20:36,480 That's completely right. The easiest thing would be to 400 00:20:36,720 --> 00:20:40,640 quickly build a dashboard and just assume it's what the user 401 00:20:40,640 --> 00:20:43,520 needs. But the more you can invest, the 402 00:20:43,520 --> 00:20:47,400 more you can spend time with the users who are going to consume 403 00:20:47,400 --> 00:20:51,360 that, the better the product. And that's why our internal 404 00:20:51,360 --> 00:20:54,400 analytics team really invests heavily in doing the upfront 405 00:20:54,400 --> 00:20:57,920 work in the design and listening to how people are going to use 406 00:20:57,920 --> 00:20:59,200 it. And what they usually do is that 407 00:20:59,200 --> 00:21:02,360 they'll they'll build a first version after spending a lot of 408 00:21:02,360 --> 00:21:04,890 time talking to us, and then they'll change it and they 409 00:21:04,890 --> 00:21:07,290 modify it and they make it better because, you know, it's 410 00:21:07,290 --> 00:21:09,850 always hard to get it right exactly the first time until 411 00:21:09,850 --> 00:21:11,930 somebody has a chance to play around with it. 412 00:21:12,490 --> 00:21:16,690 So it's an evolution as well. And on that point, how can HR 413 00:21:16,690 --> 00:21:22,570 leaders encourage their organizations to adopt these 414 00:21:22,570 --> 00:21:28,000 kinds of data focused tools? Need to start with the group of 415 00:21:28,000 --> 00:21:30,880 people who really want to use these tools first, get them into 416 00:21:30,880 --> 00:21:32,840 their hands. Like just get started, set up a 417 00:21:32,840 --> 00:21:35,040 Tiger team. And it always starts with a 418 00:21:35,040 --> 00:21:37,200 business question, right? It never starts with the tools, 419 00:21:37,200 --> 00:21:39,640 starts with what are you trying to do in the business and how do 420 00:21:39,640 --> 00:21:41,360 you want your people to experience that? 421 00:21:41,360 --> 00:21:43,480 How do you want them to help in the business? 422 00:21:43,680 --> 00:21:45,880 So it starts with those questions and then you look at, 423 00:21:45,880 --> 00:21:47,520 well, how do we answer these questions? 424 00:21:47,560 --> 00:21:50,840 And as a Tiger team gets more and more up to speed and they 425 00:21:50,840 --> 00:21:53,760 get comfortable and they realize that using narrative and 426 00:21:53,760 --> 00:21:57,200 storytelling, they can actually understand the data and get to 427 00:21:57,200 --> 00:21:59,680 maybe do the next level of analysis themselves. 428 00:21:59,880 --> 00:22:03,480 Caroline, what advice do you have for HR professionals who 429 00:22:03,480 --> 00:22:08,040 are just beginning this data-driven people analytics 430 00:22:08,040 --> 00:22:10,200 journey? First of all, don't think about 431 00:22:10,200 --> 00:22:11,960 the tools. Start with what your business 432 00:22:11,960 --> 00:22:14,440 strategy is and how your people support that strategy. 433 00:22:15,040 --> 00:22:18,240 And then think about, OK, what are your business questions that 434 00:22:18,240 --> 00:22:20,480 you want to answer? What are your data questions 435 00:22:20,480 --> 00:22:23,070 that you have? And then look at what tools fit 436 00:22:23,070 --> 00:22:25,510 into those questions. Whatever tools you're going to 437 00:22:25,510 --> 00:22:27,950 use, just make sure that they're agile tools because what you 438 00:22:27,950 --> 00:22:30,350 need to ask today is going to be different next week. 439 00:22:30,350 --> 00:22:33,230 Utilize ML and AI. Data is growing. 440 00:22:33,350 --> 00:22:35,790 I hear it all the time from business leaders I talk to. 441 00:22:36,030 --> 00:22:38,270 It's hard to get a handle on all the data that's in your 442 00:22:38,270 --> 00:22:40,350 organization. It's fragmented, it's siloed. 443 00:22:40,710 --> 00:22:43,030 You're going to have to use tools that are using and 444 00:22:43,030 --> 00:22:45,910 utilizing ML and AI to be able to surface those insights 445 00:22:45,910 --> 00:22:49,030 because it's just getting too vast to do it in a manual way. 446 00:22:49,190 --> 00:22:52,120 And I would say have fun. We really enjoy working with our 447 00:22:52,120 --> 00:22:54,840 people, analytics team and Workday because we discover 448 00:22:54,840 --> 00:22:57,800 really interesting ways that we can provide better employee 449 00:22:57,800 --> 00:22:59,800 experiences for our whole organization. 450 00:22:59,800 --> 00:23:02,280 So have fun. It's a really interesting place 451 00:23:02,280 --> 00:23:04,760 to be. Caroline O'Reilly, general 452 00:23:04,760 --> 00:23:08,680 manager of analytics at Workday, thank you so much for spending 453 00:23:08,680 --> 00:23:10,560 the time with us today. Thank you so much, Mike. 454 00:23:10,560 --> 00:23:11,760 I really enjoyed our chat.