HR, We Have a Problem - Connecting data governance and data privacy in HR to shape a seamless employee experience.

[00:00:00] Welcome to the HR Huddle Podcast presented by Sapient Insights Group, the ultimate resource for all things HR. It's time to get in the huddle.

[00:00:15] Hello everyone, welcome back to the HR Huddle. I'm your host, Terry Zipper, CEO and Managing Partner at Sapient Insights Group,

[00:00:28] and I'm back for another exciting episode of HR. We have a problem.

[00:00:33] This is the show where we break down the big and most relevant HR issues of the day. We help you make sense of what they mean for you,

[00:00:41] and we talk about what you might do about them. Joining me today is Danielle Bushin.

[00:00:47] Danielle leads people analytics, data governance and stewardship at Sinoffey. I hope I said that right. A global healthcare and pharma company. Welcome Danielle, always great to catch up and excited to have you on the show.

[00:01:01] Thanks so much, Terry. It's great to be here. That sounds like a big set of responsibilities.

[00:01:08] People analytics, data governance and stewardship. How did that? What was the journey there to people analytics from your current life of more HR-related background?

[00:01:20] Yeah, so I mean it's interesting. Sinoffey is an organization that's in transition. It's moving to a level of digital enablement and AI focus in particular around the core pharmaceutical business that is really the future of biotech.

[00:01:37] And as part of that, all of the corporate functions are moving in the same direction. So I joined Sinoffey and by the way, some people call it Sanoffey. Some people call it Sinoffey.

[00:01:46] We're all still debating internally what the name really should be. But I joined in August of last year in a new role of HR data governance.

[00:01:55] We call it people in culture and I'm aligned to the analytics team because the quality of our data is what drives our analytics.

[00:02:02] I spent four years running people analytics in a big financial services firm here in Canada, we're invased.

[00:02:08] And this opportunity came up to really create a new culture of data driven decision making and HR in an organization that hadn't had it before.

[00:02:18] And we really haven't thought in a disciplined way about what data could be as a strategic asset for the organization.

[00:02:26] So that's the opportunity I have now is to really make that part of our ethos and our culture and to be thinking about everything from the employees perspective on their own data and data privacy and the ethics of data in the world of AI.

[00:02:41] So how do we collect and integrate data through all of our end-to-end processes? And that's something that the HR function really needs to step up to I think everywhere because it's what's enables that sort of consumer grade experience in the work experience.

[00:02:57] So that's what processes drive the collection of the data in a super seamless way and give you back those experiences that you expect to be really effortless.

[00:03:07] That kind of surprises and delights it makes for the best work environment, and I think that's a challenge for all HR organizations.

[00:03:15] I think data is often overlooked for the more exciting and cool HR technologies and bright shiny objects, I think as we like to call them, but it's obviously critical to the success of our programs.

[00:03:31] And that's what I really would like to focus on today is really to talk about data governance, why it's important and what we can do to simplify it or make it easier overall and really get people more engaged in it.

[00:03:45] Does that sound like a good plan to you?

[00:03:47] That's a great plan. I think data governance in general is something that people think about as a compliance problem, this big hurdle that needs to be overcome.

[00:03:55] And I think it does need to be more accessible. It also needs to be seen as an enabler, it's what makes it easier to do great work.

[00:04:03] Yeah, yeah, for sure. Well let's get into the huddle. So we have certainly talked about data on this show, we've talked about all the magic and taking advantage of your data, but it can be a behemoth.

[00:04:18] I mean, before we go too far into this, let's talk about what we mean by data governance. What is it that we're trying to explore here specifically?

[00:04:28] So I think of data governance a couple of ways. First, I do not think of it as a compliance activity. I see it as that thing that is the muscle that we all need to exercise

[00:04:39] to inform our processes, to take ownership of them and then to allow the technology to support execution.

[00:04:47] And that means that we need owners of the processes who are thinking about the definition of their data.

[00:04:53] And then we need to use that to inform our collection strategies, our cleaning strategies, our storage models, the ethics of how we deploy the use of data.

[00:05:03] And at the very tail end of all of that is the opportunity to consume and use data to do the democratization and the fancy insights and the visualization.

[00:05:11] And that's usually where people start is thinking about those pieces. And that's the outcome for sure, it's what we all want to get to.

[00:05:18] But if you make the time to really think about the governance of those processes at the input stage, then you get the magic.

[00:05:26] Yeah. So basically policies, processes and procedures, right? But the things that people equate to compliance.

[00:05:36] I agree with you. I think at the end of the day, the outcomes are what we're focused on and we do spend a lot of time on how we want to get those outcomes through reporting and data analytics and those types of things.

[00:05:50] I think we get hung up on how that's going to work in a technology versus how do we even make sure we have good clean data and the data secure and it's

[00:06:03] private and it's used by the people who can use it and should use it and sort of what they should be doing with it.

[00:06:11] Yeah, I think depending on where you are in the world, there are different levels of focus on how you use it, how private does it need to be in our organization we're recorded in France, we have a huge presence in Europe. So GDPR is really important in our world.

[00:06:27] And that brings with it certain obligations and focus on thinking about the approach to advising people of how you're using data.

[00:06:36] And it brings a rigor to the analytics that if you've got the right masking in place, if you've got the right employee notifications in place about what are you trying to do and how does it benefit them.

[00:06:48] And you can assure them that their data is well protected. I think that opens so many interesting doors for the HR function and it's kind of a new way of thinking about data as a strategic asset.

[00:07:01] I think the challenge for HR people for those owners of processes and the people who are working in centers of excellence and thinking about what they want to know is to really define their requirements upfront.

[00:07:16] Know what it is they're trying to achieve, be thoughtful about how they create transparency. There should be no secrets in terms of how data's being used.

[00:07:25] So that doesn't mean it can't be used in some really interesting and creative ways.

[00:07:29] Yeah, I agree. I think what we spend a lot of time thinking about the requirements we need for new tech but not necessarily the requirements that we need to actually get the data and the answers that we need from that data.

[00:07:46] Right?

[00:07:47] Right. You talked about the bright shiny objects and all the great technology that's out there and there are some fantastic technology that vendors are putting out.

[00:07:56] But I think it behooves us all to remember that every vendor is going to show you the best version of that product that they can and they are going to have their demo set up with a perfect data set.

[00:08:08] And we all know that our data sets are never perfect.

[00:08:11] And so every feature is going to look fantastic and what I really encourage people to think about, especially in the vendor selection processes is what does our data actually look like?

[00:08:22] What's this maturity?

[00:08:24] What problems do we have in the hygiene of our data today?

[00:08:28] A garbage and garbage out right?

[00:08:30] If you want that bright shiny object to be as shiny as the vendor makes it look, you have to be prepared to invest in the data to make the system run well.

[00:08:41] And I think that's something that people sometimes forget.

[00:08:44] Yeah.

[00:08:45] We do talk a lot about strategic HR on the show and some of the most mundane things can actually support an organization in terms of being strategic if you will.

[00:08:58] You talked a little bit about the importance of this data and data governance being a strategic aspect of HR.

[00:09:08] Where does that fit into the overall spectrum?

[00:09:11] How can people sort of visualize how do I make sure my data governance aligns with the business outcomes that we have for the organization?

[00:09:20] For me data governance is all about understanding the foundations of your HR function.

[00:09:27] It's David Ulrich describes the human capability model and he talks about that as a text on me for HR.

[00:09:34] He's talking about the data governance, the framework of the HR function itself.

[00:09:38] He doesn't use those words typically, but he talks about grouping human capability into sort of four big buckets one around leadership, one around talent and competence and one around the capabilities of the organization how it's organized and it's underpin by all the transactional HR effectiveness work.

[00:09:57] That is exactly the strategic opportunity that HR has.

[00:10:01] It doesn't matter which tool set you picked, it doesn't matter where you are in the evolution of the organization if you're a startup, if you're really complex multi-national.

[00:10:12] If you know what your business objectives are and which processes are most essential for your talent roadmap and your HR function, you can deploy the right tools against that.

[00:10:23] You might have parts of your work that are very simplistic and stored in a spreadsheet and parts of it that are super sophisticated and stored in a complex object oriented model doesn't matter.

[00:10:38] It's about thinking through what are those data attributes that are most important and reflect to your business objectives.

[00:10:46] People often get worried when they're thinking about data governance, they have to know informatica or calibra or one of these other tools that are designed to help you organize your data.

[00:10:56] And what I say to people is from an HR perspective, you don't need to know all of that but what you do need to know is your business objectives and what requirements you have.

[00:11:06] You can partner with an IT expert to help you with the tool set. That is 100% something that can be done.

[00:11:14] But if you don't know what you're trying to achieve for the HR function itself and can't define and disambiguate those weird places in the process that well, yeah, sometimes it's like that but sometimes it's like this and oh yeah, but we can't do it that way in this one country because of X and Y.

[00:11:31] If you can't put those things down on paper in a way that says these are not exceptions. These are deliberate decisions that we have made and supportive our HR processes and it's what we want to have for our organization.

[00:11:44] Then your data will never really be very clean so that's the opportunity is to really know, be an expert in your processes and the associated data that they generate.

[00:11:55] Now, it comes back to the rules and sort of you know documenting what you want from it because if you don't, you're never going to get it.

[00:12:05] We're experiencing that a very small scale with the CRM where two years ago we were putting certain data into a field and now we're like, what exactly were we doing with that data?

[00:12:15] And what is that supposed to mean? And is that supposed to help us make some decision like I don't know what to do with this.

[00:12:21] So it could be very different for very small companies but this, I think the earlier you start thinking about this anytime you implement a new system, the better off you're going to be over the long term and documenting it.

[00:12:35] I mean, it just, it feels mundane and hard and boring but at the end of the day, it's how you communicate with the people in the organization about what this information represents.

[00:12:49] It's also how you make something that seems really complicated and maybe have a lot of variability and it feel really simple.

[00:12:58] And so I think of that as an exciting puzzle to solve. People use pick up their iPhones or whatever smartphone they have every day and they just expect it to work for them.

[00:13:08] And it feels super simple and you can access all these different features and capabilities.

[00:13:13] I guarantee you the engineers at Apple did not feel like it was easy while they were designing it.

[00:13:18] But that was always make it have that consumer grade experience that feels totally effortless.

[00:13:24] Yeah, I just interviewed the CMO from I solved a few weeks ago and we talked a lot about employee experience sort of bringing marketing and HR together and how what was driving the employee experience.

[00:13:37] You mentioned that a minute ago about this whole data governance process and sort of data overall.

[00:13:44] What do you see is the connection here between data and the ideal employee experience?

[00:13:51] Employee experience in today's world is the consumer experience in many ways.

[00:13:57] And we have Amazon and Uber and Google and everybody in meta to thank for that because they have made it part of our everyday life.

[00:14:05] And all of those organizations put data at the center and at the heart of the way they design their product, the way they build their experience, they curate their data, they think about their data, they view it as the asset and the engine of the organization.

[00:14:20] And I think a lot of traditional companies that march in the tech space see data is that thing out on the side that kind of needs to be dealt with at some point.

[00:14:30] Oliver Mollender is a blogger who focuses on data and IIs an entrepreneur and he has this great visual of data really at the center in those tech companies and data this thing that's off on the side that I might get to after I think about my processes and my tools and everything else.

[00:14:47] And when you do that, you lose sight of the opportunity that data is there to create sort of strategic insight and to integrate the way you're organizing your data into the strategic opportunities that you're pursuing.

[00:15:04] And I think in the world of AI where AI can bring insight and create capability for any kind of business bringing that data right to the heart of it will really help uncover opportunities that weren't there before.

[00:15:20] That you didn't even necessarily recognize and so when you're thinking about the employee experience.

[00:15:27] The employee doesn't necessarily even know what it is they're looking for in the workplace, but when you bring that to the forefront and make a process easier that I've had to do every single day I had to log in I had to do my time tracking I had to do this thing I had to do that thing I had to log out of this portal before I went to the next portal.

[00:15:44] And I had to remember the password that's not integrated and all of these other crazy things that happen in people's work lives.

[00:15:51] When you ease those burdens and make the experience of being at work that much more effective for the individual it lets them focus on the things that allow them to really add value and that's what they're there for.

[00:16:04] I think sometimes we get caught up in oh yeah you can't do that because of this single sign on problem or that regulatory issue and we sort of stop there and say well I can't do anything about that but that's where we should really own the work experience and make it something amazing roadblocks right.

[00:16:25] So his I saw the picture his visual I thought was also very cool and it's very accurate in terms of the just the tech companies today there.

[00:16:35] The data is embedded right it's the center kind of the universe everything flows out from that whereas most organizations the data sits outside that bubble.

[00:16:47] And I'm guessing that's the kind of thing you're dealing with right now is how do we get the data from outside the bubble inside the bubble where it becomes the center of this universe.

[00:16:59] What do you think are some of the biggest challenges teams are grappling with today kind of trying to do that kind of thing.

[00:17:06] I think some of it is basic is in green habits its habit forming if your data has not been at the center and what's real easy to say oh yeah we've implemented that tool it will just run now or he is taking care of that piece for us.

[00:17:21] You lose sight of that strategic asset of the data so bringing it into the bubble first of all making it a habit to be thinking about it as part of your end and process every day.

[00:17:31] That in itself is huge and if you haven't as an HR person if you haven't been thinking that way that's a new muscle to flex and a new habit to build I also think that it's about ruthless prioritization.

[00:17:45] None of us will ever have a perfect set of data with one bubble in the middle I think last count at Sonofi we were looking at about 200 separate bubbles and that's just the reality of big complicated organizations right.

[00:17:58] Lots of apps lots of regulatory constraints from different parts of the world lots of different domains of data and specialty products that all need to be managed.

[00:18:09] And that doesn't make it easy but doesn't make it feel seamless and effortless and there are tons of roadblocks that creep in both because of decisions that have been made over the years and technical debt that never gets resolved and legacy decisions that say why do we do that.

[00:18:27] And you start asking around and nobody even knows anymore why do we do that so all of that makes the bubble into many like we're having a bubble bath now and so that's hard.

[00:18:38] Yeah, but I think it's about really taking ownership it's about saying that's my bubble and I'm going to bring it to the center and I'm going to make sure that it is really something that I have cultivated and curated and then I understand how it's going to deliver quality and value.

[00:18:54] No, do you have a particular pet peeve when it comes to sort of how people are using the data or things people say about the data that just sort of really got to change speaking of habits I really got to get them to change this habit.

[00:19:10] I've got really got to change this habit or we're never going to we're never going to make the successful.

[00:19:17] It's probably a pet peeve and then just a risk that I see all the time that the pet peeve is oh yeah no that's a regulatory thing we can't change that.

[00:19:25] And so they've been told that for years and years and people have always said yeah that's regulatory but then when you press and you say okay show me the regulatory requirement what are we talking about what can't we do why can't we do it nobody knows.

[00:19:38] And it's become this urban legend of that's regulatory we can't touch that but actually it's not even true and so we create handcuffs for ourselves in HR that actually are unnecessary and so what I try to remind people of is be curious be thoughtful ask more questions.

[00:19:57] Because when you can do that you may uncover the barrier that you thought was there isn't barrier at all so that's the pet peeve piece for me is just keep asking questions be curious don't take things at face value.

[00:20:09] And then other piece of it that I think is a challenge is not just this idea of what's regulatory but also that there's something that happens when an organization has layers upon layers of stuff over time.

[00:20:25] That we don't go back and we don't clean house.

[00:20:28] We just keep layering on the cake gets bigger there's more ingredients in the cake and sometimes you just need to edit and say actually too much stuff going on here we don't need to upload this anymore and be really good at knowing when you need to start

[00:20:42] on setting stuff or deprecating will tech you're never going to keep all the layers of code that told you exactly how finance organized the cost center hierarchy 25 years ago.

[00:20:52] Right they've replaced that technology three times since then HR doesn't need to keep being able to reference it so move on give your submission to let go of the past as you start to invent for the future.

[00:21:05] Yeah and we were kind of blown away to by the numbers of just the sheer numbers of HR applications that even small organizations with under 50 employees are using ten or more different solutions that actually touch hr in some way so.

[00:21:23] There's a lot of data going in where is it going what kind of information can we get out of that.

[00:21:29] Think about the reporting and that usually is the thing at the forefront right it's well I can't report on this because our data scrap how do I I need this kind of reporting and there's a lot of trial and error running reports to see where is the right data how stumbling onto those fields but this is a it's a key outcome is.

[00:21:52] To be able to deliver reporting especially to the metrics that we share with executives mean these are the kinds of things we need to come out of this data and it couldn't be more important to get that data right.

[00:22:06] And that doesn't always mean that HR has to invent all of that data that's actually something that I think we do it our peril at times it we all have an hr s of some kind.

[00:22:16] Bigger slicker fancier simpler more basic doesn't matter and we all know who reports to in the company that's sort of the guts the nuts and bolts of HR but that's not the whole language of HR we talk a lot about head count and we want to know.

[00:22:33] The cost base and the metrics of our head count and how many people are active and inactive and all these different lenses of the data and when you go and ask somebody in finance for their definition of active and inactive or who's paying for that head count or even what is a head count is it a person or is it an economic unit of measure of an FTE.

[00:22:54] We all have these different ways of talking about it and it's incumbent upon the HR professionals to learn to speak the language of their peers in finance in real estate.

[00:23:06] In in in IT who talk about users which is like sounds a lot like heads users are probably people too but what about the bots those are users and so we get hung up on all these differences of interpretation instead of really making sure that we found good ways to talk to each other.

[00:23:24] And to be able to do that reporting in ways that reflect the lens at each of those stakeholders really wants to see the data through and I think that's the place where.

[00:23:34] HR can really be a strategic enabler when we can learn to reflect to those other languages in how we're looking at the people data.

[00:23:44] We gain so much credibility and we help the organization move forward and come to alignment on things that sometimes have been problems for years.

[00:23:52] Yeah, it's a really good point and I found HR tries to own a lot more than they need to or not necessarily own it but take responsibility for it when sometimes it's not their responsibility but you know they need to be able to help people get where they need to be with respect to some of these things so well it may not be theirs to own it's theirs to help shepherd.

[00:24:17] And also there's to reflect correctly if somebody else is defined that particular hierarchy that's super important to the way your organization is designed.

[00:24:27] Reflect that back they are the source of truth yeah they own it be a great consumer reflect it back and then when they realize oh that's actually no we intended the left data that's great partnership as well and it doesn't mean you have to reinvent the wheel for everybody else all the time.

[00:24:43] Yeah, so how do you see AI coming into the picture for this because obviously it feels to me like a is a this is a great place for AI to intervene right and help us with outmoded outdated thinking and data and that kind of thing but it's the concern over data quality and governance was.

[00:25:08] I think the fourth most cited reason for people not using AI and intelligent tech there's something like 27% of organizations in our survey last year saw data governance as a major barrier to using AI assisted tech because they didn't think they had good data.

[00:25:28] Well, and it may be that they're worried that data governance is a barrier because it's the sort of big brother view of do you have the right ethical standards in place do you have the right controls in place has your album been appropriately tested is there in here and bias.

[00:25:44] And those are all incredibly important questions to ask I don't think they're a barrier I think that what we as HR professionals what we need to do is embrace the idea that responsible AI is at the heart of the future of work.

[00:25:58] We can use AI to make many things easier but we need to do it responsibly so I don't see that as the barrier I think the challenge is in the hygiene and quality of the underlying data that you're training with.

[00:26:11] This is all good AI is trained on models of some kind right what data you put into it is what it's going to learn from and if your data is really messy it's going to live to learn to live in a world of really messy and that may not give you the answer to that.

[00:26:27] I think that's hard for people to appreciate when they're designing AI into their operations to support informed decision making do you have the right controls and so human sensibility and oversight on that decision making process.

[00:26:45] That's one thing that becomes even harder when you're actually not talking about AI when you're talking about good process automation and intelligence system automation.

[00:26:56] Because then it's just right or wrong and if you trained badly it's going to give you bad answers and you're going to end up with processes that fail or worse yet transactions that are done incorrectly.

[00:27:08] Just because of the process automation that you tried to put in place to ease a burden in the early days of all of this we would build products that.

[00:27:16] That looks great for the client and we knew that behind the scenes we had hamsters running out of wheel and people moving spreadsheets around and stuff happened in all over the place that we haven't quite figured out that automate.

[00:27:27] But we knew that we also had process checks in place that helped us catch those things and I think as we modernize more of our work move to process automation even more than we have so far and start to adopt AI.

[00:27:42] The value of the data quality and the data hygiene work that is easy to ignore becomes even more important to the heart of the HR function.

[00:27:52] It's what allows us to really know that we have tuned this thing so that it will do exactly what we want it to do every time.

[00:28:00] That would be nice.

[00:28:02] Yeah, it's a good aspiration.

[00:28:05] Yeah, what have you seen for certain proven practices like do you see a lot of organizations putting together like data councils some sort of cross functional group that meets and talks about data governance policies and processes or what do you see happening in that space.

[00:28:25] Certainly for large multinationals and company larger employers data governance councils are very much part of the fabric of the organization whether they sit within.

[00:28:35] The data office or our more cross functional varies quite a bit.

[00:28:39] The idea that we think about organizing our data into some key domains with areas of accountability for different types of data based on how that data is originated is pretty foundational to how you organize that sort of data governance activity.

[00:28:55] Beyond that, I think some organizations are very focused on ethics charters principles of operations others are more focused on the dialogue of ownership and documentation of what which of these systems is really the provisioning point in the source of truth.

[00:29:13] And you can get into endless debates about whether that's really the source of truth or not.

[00:29:18] To me, the important part is having that framework for the conversation and the dialogue and being able to say well actually when it's people related data here's who we're going to hold accountable and when it's

[00:29:30] procured to pay data or transactional finance data or client information here's who we're going to hold accountable.

[00:29:37] Client data is always such a fascinating one and I think it speaks volumes for people data as well in client organizations everybody wants to be the owner of their client.

[00:29:49] But most often there are people working with that client in four or five different business lines or product areas or practice groups and they all think they own the client.

[00:29:59] There's only one client and the same is true with people data there is one body it has a name that person has a right to a certain amount of privacy and entitlement around their own data and how it gets used.

[00:30:13] And the company has all kinds of interests and tracking their history and understanding things about the way they work and being able to really reflect back a great employment experience which will only improve their value in their brand as an employer.

[00:30:28] The different departments that person worked in over time the different managers they had the different jobs they held will all sort of change that data over the space of months or years.

[00:30:39] Doesn't times the fact that they are one individual and you want to be able to have a really solid time series view of that person.

[00:30:47] Yeah, and we want some predictions right we want to know what might happen based on all the things that have happened in the past what could be the future for this person or this group of people.

[00:31:00] Yeah, and that's actually one of the most challenging areas of governance I think is this idea of you but how do you use predictive data in a way that is responsible and well informed and then the users the consumers of that data actually recognize what a prediction tells them.

[00:31:16] If we publish an attrition model that says here are the people who are most likely to leave and in here's a risk score for how far out it might be before they leave if you hand that to a frontline manager who goes running up to Terry and says, Terry why are you about to leave and Terry said, I'm not leaving.

[00:31:33] What are you talking about.

[00:31:36] That's a problem that's putting the information in the wrong hands with the wrong level of knowledge and awareness.

[00:31:42] And so I think the predictive work it's so important that our people analytics specialists are at the forefront of that and are educating and deploying those resources in ways that are well informed aggregated in a way that can help the company understand materiality of risk but also protect the innocent along the way.

[00:32:04] Yeah, so let's bring this down to kind of the individual level.

[00:32:10] Yeah, person listening here today because I feel like everybody in HR has some level of responsibility for data even if that is your primary focus you have your responsibility for the data that you're responsible for.

[00:32:24] But if I want to become let's say I want to be more expert in this area or I want to advocate for better data.

[00:32:31] Do you have any suggestions for how people would start to do something like that as they think about how they're going through the same kind of growing pains that you guys are going through it's no fee which is we've gotten so big and we can't use the data we want to use it the way we want to use it.

[00:32:50] So I think it's a very important thing to do is to get a little bit of a little bit of information about how we sort of bring that down to a manage something manageable where I can start having a conversation.

[00:33:00] I think it's I mean it starts with ruthlessly prioritize so figure out what is the thing that if you touch one area of your data and make it better where will that help you the most because you can't do everything at once.

[00:33:16] So figuring out where are the quick wins the things that you can change right away and materially improve a process for your employees or simplify a conversation with finance the things that are going to help you the most the fastest.

[00:33:30] Get some of those quick wins and then chart aroma for the hard stuff you can't do everything at once you need to define your priorities and you need to define those requirements that we were talking about earlier area the disambiguation they will what.

[00:33:45] What are those exceptions what's that regulatory issue invest the time in those conversations because that is what's going to really help you configure your tool set manage your process end to end and ultimately get a quality that reflects what you're trying to do.

[00:34:04] Start small start smart start small pick the things that will make the biggest differences and build a common language right down those definitions when you agree to what do we mean by active head count.

[00:34:16] Write it out it doesn't need to be a fancy tool but it needs to be somewhere where everybody can reference it.

[00:34:22] And that you are sure you have clarity and you just good basic data hygiene management if you have.

[00:34:29] I don't know an hrs that have five different ways of inputting the person's name in some countries the person's name there's surname is all upper case and their first name is lower case and somewhere else it's double by characters because that's what's important to not part of the world and somebody else is standardized at a different way you go and look at your list of employees and suddenly you can't read it.

[00:34:52] Those basics of good data hygiene it's like dental work you do the work up friend you avoid the cavities later on it's not rocket science but it is the stuff that will make a huge difference to the usability of your data down.

[00:35:08] Yeah make friends with somebody in the organization that is a real data guru that's always a good a good friend to have and value those skillsets you know there are people who love being in the granular detail every single day.

[00:35:21] And there are people who want to look at the big picture and data governance is a lot about being able to move between those two fairly well and kind of understand the big picture recognize when the detail matters.

[00:35:33] But reward the people who are paying attention to that detail for you because they are the golden ticket in terms of making your data hygiene sustainable in the organization.

[00:35:43] And that may sit in the HR function it may sit in your people services or shared services team it may be part of all of your HR generalist roles doesn't matter where it sits.

[00:35:54] It is a high value piece of work that needs to be maintained.

[00:35:58] Yeah well this has been very interesting and I think we could probably do we should probably think about a follow on podcast to really get into some of the nitty gritty but I think this has been a good overview of kind of where to start.

[00:36:12] What to think about who to work with and really just how to start thinking in general about how you get some of this stuff done and it's never too early to start I mean I think if you're a small company.

[00:36:24] You've got an advantage where you haven't done a whole lot yet so get started before you get this gigantic behemoth that you have to figure out how you pull back into into control well likewise is never too late right like if you're assuming in that giant bubble bath with all those different bubbles of data.

[00:36:41] Around you it's okay pick one big in you can definitely have an impact.

[00:36:47] Yeah for sure well thanks Danielle for joining me today this has been a great conversation I want to thank our producers the team at brand method media group and our marketing team Lisa and summer.

[00:36:59] And I want to thank you the audience thank you for tuning in that is all the time we have this week for HR we have a problem if you enjoyed the episode you can subscribe to it on your favorite podcast.

[00:37:10] We'd love it if you leave us a review or drop a line and let us know what topics you might like to hear us talk about on the show.

[00:37:19] We will be back in two weeks with another episode of HR we have a problem thanks everybody.

[00:37:40] you