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[Radar Recap] Scaling Data ROI: Driving Analytics Adoption Within Your Organization with Laura Gent Felker, Omar Khawaja and Tiffany Perkins-Munn

Laura, Omar and Tiffany explore best practices when it comes to scaling analytics adoption within the wider organization
Apr 2024

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Guest
Laura Gent Felker

Laura has been at Salesforce for the last decade specializing in data and insights for the Quote-to-Cash (QTC) lifecycle. She built a best-in-class data organization from the ground up focusing on data visualization, data engineering, and data science. Her goal is for QTC stakeholders to develop a data literacy culture that can make actionable data-driven business decisions. In her tenure, Salesforce has rapidly grown from a $4B to a $30B+ company. Therefore, automation, continuous learning, and leveraging the right technology have been imperative for her team's success. A big piece of Salesforce’s culture is giving back through the 1-1-1 model. In 2021, Laura joined GlobalMindED on the Board of Directors to advise with technology implementation to scale for the future. GlobalMindED helps close the equity gap by connecting first-generation college students to mentors, internships, and jobs. As a leader in technology and data, Laura is passionate about creating an equitable and inclusive business environment for her team, stakeholders, and the world around her.


Photo of Omar Khawaja
Guest
Omar Khawaja

Omar Khawaja is a data & analytics thought leader with over 23+ years of experience in the software, consumer goods, life sciences and manufacturing industry. Omar is passionate about creating business value with data & AI. He has also multiple years of experience in leading the development & operations of data & analytics platforms. Omar is currently working as Global Head of Data & Analytics / CDAO for Givaudan. Before joining Givaudan, Omar worked at Roche for 3.5 years leading the BI & Analytics team for Roche Diagnostics. Omar spearheaded the implementation of data mesh for Roche Diagnostics & before joining Givaudan he was driving the overall platform strategy for Informatics. Before joining Roche, Omar worked at Novartis in Basel, Switzerland, where he has performed various roles over the last ten years. Originally from Pakistan, Omar first started at Novartis in his home country, where he was the Chief Information Officer (CIO) for more than two years. He then moved to Switzerland in 2012 as CIO for Novartis Pharma Services (Exports Headquarters for Novartis).He held other roles like the Regional Business Partner for emerging markets, and spent the last 4 years at Novartis in data & insights roles. He led the sales insights team in the Commercial IT organization and prior to joining Roche, he was leading the commercial analytics team as part of the Analytics Center of Excellence for Novartis. Omar is also an active member of the LinkedIn data & analytics community. You can also listen to his other podcast interviews & webinars. For fun, he enjoys spending time with his family, hiking and reading.


Photo of Esther Munyi
Guest
Esther Munyi

Esther oversees the data strategy, governance, and architecture at Sasfin. She is focused on developing and implementing cutting-edge data solutions, and establishing data as a core asset and enabler for Sasfin. Esther was recognized as the CDO of the Year 2023 at the FINNOVEX South Africa Awards, and as one of the Global Data Power Women in 2023.


Photo of Richie Cotton
Host
Richie Cotton

Richie helps individuals and organizations get better at using data and AI. He's been a data scientist since before it was called data science, and has written two books and created many DataCamp courses on the subject. He is a host of the DataFramed podcast, and runs DataCamp's webinar program.

Key Quotes

You can have the best tool in the world, but if you have bad data, bad processes and you don't have people that are data literate—it's not going to be successful.

Traditional data skills are no longer locked in the IT department or the data department. We have more and more citizen data roles evolving all the time.

Key Takeaways

1

Regularly showcase real-world examples of how analytics have driven improvements, cost savings, or revenue growth within the organization. Demonstrating tangible benefits can motivate increased adoption and make the case for analytics as a critical tool in the organization's success toolkit.

2

Implement mechanisms to regularly collect feedback from users at all levels regarding the analytics tool's usability, features, and integration needs. Use this feedback to make iterative improvements, showing responsiveness to user needs and fostering a culture of continuous enhancement.

3

Develop tailored training programs that address the varying skill levels and roles within the organization, ensuring everyone can leverage the analytics tool effectively.

Transcript

Richie Cotton (00:00):

Alrighty. Hello everyone. Welcome to the latest session. Please let us know where you're joining from in the chat. Let us know what you are excited to hear about in this session. And as ever, you can ask questions for the audience, not the audience, questions for the speakers throughout the session and we'll get to your questions at the end. See a lot of people joining. Alright, we got OH'S scrolling very fast to keep up. We got Trupti from India, we got Miriam from Serbia, we've got David France, Haer calling from, I dunno, where's calling from? Matteas from Poland. We've got Aldo from Peru. We've got, who else have we got Sandra from somewhere scrolling too fast man, I can't give too many of you.

(00:53):

Anyway, nice to see you all. We're give just a few more seconds for everyone else to join us and then we're going to kick off. Alright, so in fact, let's just dive straight into this. Anyway, I'm Richie. And one of the big problems with making use of data is that you have to spend money on both tools and employees. And that means that at some point someone in management is going to want to see a return on investment for spending. So in this session we're going to look at what sort of a return on investment you can expect, and we're going to discuss the changes you need to make to your organizational processes and your culture in order to achieve those returns. And we've got three fantastic guests to guide you through the process. So first up is Laura Gen Volgar. She's the go-to market ... See more

analytics lead at MongoDB and she's previously the senior director of insights and scalability at Salesforce.

(02:02):

So welcome Laura. And secondly we have Omar Kja, he's the chief data and analytics officer and also Global head of Data Analytics at VO Dan. And previously he was the head of business intelligence at Roche Diagnostics and he's also a founding member of the data product leadership community. And last but not least, we have Tiffany Perkins month. She's the managing director and head of data analytics for marketing at JP Morgan Chase and was previously the managing director and global head of research for analytics and data at like Rock. So yeah, welcome all three of you. Now, all three of our guests have got a lot of experience in building and running data teams that are tightly integrated with business and frankly between them they've solved all the data team problems that you haven't thought of yet. So we only bring you the best of the best here at radar. So let's hear what they have to say. Now to begin with, since we are talking about return on investment, it'd be nice to know what that actually means. So just in practice, what does return on investment mean for data initiatives? Tiffany, do you want to leave this one?

Tiffany Perkins-Munn (03:16):

Yeah, sure. So first of all, thank you Richie and radar for having us. I think it's going to be a really exciting discussion and I know I am, I'm assuming Laura and Omar as well are really excited to be here. So just in really simplistic terms, when I think about return on investment, it's really about how do you use data and analytics to achieve a specific business goal? And I think often in firms that I've worked in anyway, people get caught up in value, meaning dollars or revenue. And yes, that's important because obviously revenue growth is a key sort of business goal related to how are you acquiring customers, are there cross-selling opportunities, et cetera. But there are lots of other value metrics that we also want to take into consideration when we think about business goals. So are we reducing costs, right? Are there operational efficiencies?

(04:11):

Are we optimizing processes? Are we allocating resources appropriately? There's also customer experience metrics. Are our customers happy with us? Are they willing to recommend us to others? Are we retaining them? Are they loyal? And the one that I think we forget about a lot is actually are we making ourselves smarter internally? Like are we making quality decisions? Are we making those decisions faster? Are we building business intelligence tools to help us make more accurate speed to market kind of decisions? And then in my space, just to give an example, one of the things that really matters in terms of value to a specific business goal is risk management. Are we detecting fraud? We're in a space where privacy matters, there are lots of fraud issues out there. Are we using data and analytics to mitigate those risks? Are we detecting fraud? Are we ensuring that we are compliant with regulations? How are we actually using algorithms, predictive models, et cetera to protect our customer's assets, to mitigate financial losses and basically to build that trust with the consumer. So when I talk about ROI and value, I'm talking along that continuum of metrics that matter.

Richie Cotton (05:38):

I think it's really important that is not always going to be directly tie back to the revenue of your company, but actually there might be some department specific metric that you're working towards on that. Excellent. Yeah, really comprehensive set of metrics there. I like it. Alright, so beyond simply buying tools at a high level, what do organizations need to do to improve their data capabilities? So Laura, did you work for a tool vendor? I want to get you to talk about what you do beyond tools. Do you want to go first on this one?

Laura Gent Felker (06:14):

Yeah, absolutely. So beyond tools, I think process is one of the most imperative elements of data. So really making sure that we identify end to end on what I call the data supply chain to identify where are the bottlenecks so we are able to get a very fast and efficient set of data and metrics so we can measure that quickly. Like Tiffany is talking about using data if beyond tools, if you don't have the adequate process and then the adequate trained up skills of the future for people, not just in the data and analytics org, but through and through across the company to enhance the data literacy. I think you could have the best tool in the world, but if you have bad data, bad process and you don't have people that are data literate, it's not going to be successful. Of course, we always want to leverage the best in class tools, but there's those two other key pillars that are really important in it.

Richie Cotton (07:16):

Excellent. Yeah, you have the right supporting infrastructure. Omar, do you have anything to add to that?

Omar Khawaja (07:24):

Yes, Richie, thanks. And please first allow me to show my appreciation for inviting us and glad to be here today sharing this panel discussion with Laura and Tiffany. Let me complete the triangle, right? Laura talked about processes, I'll talk about people and people in my view are the key pillar of any data initiative they make and break everything. And when it comes to people, you can range from top executive committees of the company or in different shapes and forms. They can be the board of the company, they can be the executive leadership team of the company and drilling down all the way to the people on the front lines, on the frontline managers, the sales teams, the people on the shop floors. And these peoples have the job to do. They know what they're for in the company, they come to the office, they do their work. And for data leaders to be successful, it's important to understand what these people jobs are, these personas are what their needs are before any tool discussion can start. I mean process examples that Laura mentioned are so important in this. Do these people receive the insights they need or not? What's in it for them to use the tool and what kind of tool they need? Maybe somebody is very happy with a mobile phone in a company and another set of personas may not even have our company mobile phone.

(09:00):

This can be device dependent, this can be the area they work. You might have a very fancy tool that works on a cloud and the person works underground where there is no internet connectivity, how that person needs to receive the insights in that area. The tool comes always later. It's about the people, it's about the process, the business outcomes that Tiffany mentioned are so important. So those are just my 2 cents on that.

Richie Cotton (09:30):

Okay. Absolutely there with you. The people side of things are very important. It seems like non-technical people are then going to need some kind of data skills, but there's not going to be quite daunting for them. So do you have any advice on how to improve the data skills for these workers? Maybe Omar, do you want to go again? We'll do reverse order this time.

Omar Khawaja (09:53):

Yeah, sure. I can take a stab at that. And how about I reverse the order of the question as well, Richie.

Richie Cotton (10:01):

Okay.

Omar Khawaja (10:02):

It's not about the business people only learning tools and the tech. I think with this modern age, people are becoming tech savvy. The data, traditional data skills are no longer locked in the IT department or only in the data department. We have more and more citizen data roles evolving while that is happening. For each of those personas, there should be a targeted data literacy program, AI analytics literacy program as well. But I want us to step back. I want us that teams that are more tech savvy, the teams that are more data savvy then also needs to understand the business as well to make an impact. An organization is there for the purpose. They have their vision, their mission. They're there to achieve that target no matter which industry they're in. And the data teams needs to understand in which business they operate, how does the business value chain works? Who are the key stakeholders and what decisions there needs to make. So we already can see that there is a nice balance. It's not just one way education about tools and technologies and making people tech savvy. I think that's not that difficult part. It's about understanding the business and meeting where your customers are. That will make a lot of difference.

Richie Cotton (11:29):

Interesting. So you're going to have some technical skills and some business skills and hopefully that's going to help people communicate. Alright. Tiffany, do you want to expand on this? Do you have any advice on how you can improve communication between your technical employees and your business employees or other?

Tiffany Perkins-Munn (11:52):

Yeah, so yes, thank you Richie. So I can't stress this enough, but I think this is so critically important and it's really storytelling, storytelling, storytelling, storytelling. I know it sounds a little weird in the data space plus

Laura Gent Felker (12:07):

What on that, right?

Tiffany Perkins-Munn (12:09):

But people have been talking about it more, but it's really about training on storytelling techniques to help the technical teams convey insights in a compelling and relatable manner to non-technical but super smart audiences. It's not about dumbing down information, it's just about learning how to tell the story. And I know that we, at least in a lot of the firms I've worked in, people get caught up in the jargon, the acronyms, and by the time they explain what they're doing, everybody's like, wait, what just happened? What are they doing? Who do I need to talk to? No one knows. So some combination by the way of this storytelling exercise and then kind of collaborative workshops where you have these brainstorming sessions that encourage open dialogue, idea exchange, problem solving with the storytelling, I think really helps to bring together the technical and the non-technical and it actually teaches everyone how to speak in very simplistic, easy to digest language that anyone can understand.

Richie Cotton (13:15):

I love that. Oh go.

Laura Gent Felker (13:18):

I wanted to add to that what Tiffany was talking about is we all have to be speaking the same language and know the language of our stakeholders end to end. So as a leader in the analytics organization, I pivot between data engineers, data scientists, super duper technical people to our business stakeholders that are in sales and other go-to-market people. And I've talked to my team about this, we have to be able to pivot both languages. You can't be bringing up a Python script to a sales leader, but you need to be able to use that python script to tell a story. So meet them where they are. So I can't agree more and more on that and just wanted to add to that, Richie.

Richie Cotton (14:09):

No, that's wonderful. I like you're both talking about storytelling because we had a session on storytelling earlier today. So for anyone who missed that, you're going to have to catch up the recording for anyone who intended, I'm hoping you, you're aligned on this, that storytelling is a really good idea in the data world. Alright, so from storytelling I think we can talk more generally about culture and what constitutes a good data culture. So in order to change how you work with data, you're going to have to change the data culture at your organization. What are some good goals around this? What should you be aiming for if you say, okay, we want to change the culture to get better at data. Oh my, you've not spoken for a while. Do you want to talk us through this? How might you change your data culture?

Omar Khawaja (14:59):

I'll give it a go Richie. And then Laura and Tiffany, please feel to join in. So there are two ways to looking at it, right? This is one of the misuse terms, a lot data driven, culture data-driven culture. And then we recently also come up with is your company data driven or AI driven or data informed and all of these things out there. I think companies have a culture without data, without ai, this is how people live tell stories, they talk to each other, they react to each other, they work with each other. That's a company culture is existing already in any company. Where does data and AI play a role in that? That's an important thing to understand. That's an important thing to embed and infuse in that. Otherwise we will end up, oh, you have a finance culture or you have a HR culture, or is there a supply chain culture?

(15:56):

And then we have a culture silo maybe. So we need to, as a data leader, I think it's important to go to that point, understand how the company's operating, what is the company's culture? And then infuse this data thinking over there. This means understanding the people a lot. This means building upon earlier storytelling comment, how can I tell the story to a salesperson versus to an executive versus to a engineer in a factory for example, or versus to a planner sitting in supply chain will be very different and you need to really make a point that what's in it for them. So as a leader who's talking about data analytics, ai, they need to really understand how the people will use their data and analytics and insights for an instance. Can they do their job better? For example, if they use the data in their work, can they get insights in a very different way rather than reviewing 20 reports and then doing something and then doing something with it.

(17:06):

This is how you can understand that. So I think that's one thing. The second aspect is towards the data team as well. I think there is a high need to take a different approach from what we have been doing in past. We need to show a little bit more love towards how we treat data, how instead of protecting it and shackling it, how can we unlock the potential of the data? How can we give it the same love to improve data quality, to maintain the data pipelines? It's freshness, it's completeness so that people can make informed decisions. Right? Omar? Omar, I think this aspect is very good.

Tiffany Perkins-Munn (17:48):

This hashtag free the data. Free the data, free the data.

Omar Khawaja (17:54):

I love it. I love it. Tiffany. Yes.

Richie Cotton (17:57):

Excellent.

Laura Gent Felker (17:59):

I always called it being a data shopkeeper versus a data gatekeeper. You want to keep your shop nice and clean, you want to be shopping at the best in class place where you feel good to go to, but you want to open your doors to other people. And I think that's going to help unlock a data culture to empower the user, but also having those right governance, you don't want to go into a messy shop, you're just going to be really overwhelmed in that kind of situation. But if you go and it's nice and clean and you can find your stuff real quick, you're going to feel good when you leave and you check out. So that is one analogy as Omar was talking about it and Tiffany said, free the data. I was like, keep that data shop nice and clean.

Richie Cotton (18:45):

Alright, so we've got lots of great ideas to unpack there. So I think Omar, your first idea was start with your business values and make sure the data is aligning to those and supporting whatever your business goals are differently. Free the data. And then Laura, I've forgotten already. Oh, make sure that your data, you're governing it well actually. So we've got a session later on today all around data quality, data governance. So if you're interested in that, please do come back for that final session there. We're going to discuss that in detail. Alright, so one thing I'd like to talk about is who are the people that are involved in this? So you say, okay, we're going to change our data culture, we're going to get better at working with data. Which teams need to be involved, which roles, what do they actually do? Yeah, go on. Tiffany, do you want to talk us through what's the process here? Who needs to do what?

Tiffany Perkins-Munn (19:33):

Yeah, so I think that this can happen in one of two ways. It's either, and it depends on the culture of the organization, so you have to sort of be aware of that, but it either happens from the top down where the executive leadership says this is a change we're making or it happens and I've been in organizations where it's happened both ways or it happens in a grassroots way where there's groundswell, there's interest and it's not a bubbles up to executive leadership. Regardless of the path it takes though, I think the same teams or groups of people need to get involved to make the change. And those people are the executive team because you need the executives at the top to champion the initiatives, to set the tone for the data culture transformation and obviously to allocate the resources. So let's not forget that.

(20:27):

Then you also need the data and analytics teams obviously, right? You need to empower them. All of the scientists, the analysts, the engineers, they are the ones who are going to be able to really drive data literacy. What are the best practices? How do we innovate across the organization in this space? And then obviously the partners are the business units and operations. You have to get them to integrate the data into the daily operations, into the decision-making processes and the performance metrics. Human resources has a key role because they're going to develop the data literacy programs, the workshops, the resources for employees across the different levels. And then I think really good organizations have these change agents, the people who can promote the data culture, who can share the best practices, who can facilitate the knowledge sharing. Sometimes there are transformation offices or change management teams or sometimes it's the design team that pulls that together. But regardless, I think those five roles or those five teams are really critical in making a lasting long-term data culture change.

Richie Cotton (21:46):

Lots and lots of teams involved there. So we've got executives, we've got data practitioners, hr, business functions, all sorts. Most of the business can be involved in this. I'm curious,

Tiffany Perkins-Munn (21:57):

Can I just say Richie really quickly that when people, I think one problem that a lot of organizations face is that they try to do it with one or two of those teams. The data team tries to affect the change that the executive leadership hasn't adopted or the businesses want to do something but they can't seem to figure out how to make it a broad offering because HR isn't involved. So every team has a role and I think that's an important takeaway for people to consider when they're thinking about modifying data culture.

Richie Cotton (22:31):

Alright, so it's going to have to end up being pretty broad scale with lots of people involved. I'm wondering how do you get started? Do you have to mobilize the whole business at once or are there a few people who you can begin with? Laura, do you want to take this?

Laura Gent Felker (22:46):

Sure. I mean I start with a small cohort of people and I can't agree more with Tiffany, making sure the right infrastructure essentially is in place to be able to mobilize. So you want that executive team through and through to continue to keep beating the drum, not just once, not just one all hands, but we want to keep talking about the data culture at the organization. But I always try to find a cohort of advocates within the business to start with and then they actually will speak the best to say, hey, this is the best thing, this is the outputs of being a data informed culture and these are the particular projects where it has helped me and why and what's the art of possibility? Because not everybody knows everything that we can do across data and we don't have to do data science at day one, right? Nor would I recommend that. But with that said, a lot of people might not even know what's possible in their data. So I've always found that advocacy route and finding a small cohort of people that want to be involved and that want to help make that change and be that change maker and then people start really, really coming along with that.

Richie Cotton (24:09):

Alright, so you get the people who want to be involved first and then hopefully things can grow from there.

Laura Gent Felker (24:14):

Then that snowball, it's kind of like going down the mountain. I live in Colorado so I'm all about snow and it's just to keep, the ball keeps getting bigger as we go down the mountain. And so I really believe that if you get a few strategic partners and you really market it accordingly, the people that might not have been as excited about a data informed culture are going towards a more data organization. They'll then say, oh, that's in it for me too.

Tiffany Perkins-Munn (24:47):

Ask to Laura. Oh, I'm sorry, just to add ahead to Laura. We

Richie Cotton (24:51):

Call those

Tiffany Perkins-Munn (24:54):

People in the business friendlies, go find some friendlies who will champion your idea. And then you take that small cohort that Laura's talking about and you build on it, build on it, build on it, and if they come out of the business where the objectives are set, you'll get a lot of traction. Then people will start to see how it's impacting different initiatives and they'll start to, it'll have a little bit of a snowball effect like Laura said.

Richie Cotton (25:21):

I like that. Building things up one step at a time. Okay, so Omar, go on.

Omar Khawaja (25:28):

I just want to say plus one to Laura and Tiffany and it's like you have a word of mouth ambassador in the company, within the company as well. It's like a reference customer who's speaking about the benefit of the data literacy program or how they were able to do their job better with something, whether it's a process improvement or a tool improvement or just the awareness and the knowledge that they have doing these lunch and learn sessions, coffee chats, virtual or virtual in person. This really creates that snowball effect. It triggers that snowball effect cannot stress enough to do that.

Richie Cotton (26:04):

I like the idea of having just some regular training sessions just to make sure that people are on board. One thing that's been mentioned a couple of times is that executives need to be involved. We don't really talk about which executives, is this something that a chief data officer would own or is this got to go all the way to the CEO? Who's going to be in charge of changing data culture? Do you all want to just say who you think should be in charge?

Tiffany Perkins-Munn (26:32):

So I think a lot of executives will need to sign off on it, but clearly you can't have everybody at the table being the owner. It feels like a strategic initiative, so like a chief strategy officer, but in partnership, as we've all said here with the data owners and the business owners, but because it is a company-wide exercise, I think you want it to be something that sits not in a business unit but across the organization. It might sit as one idea with a chief strategy officer type, but in partnership with obviously the key players who would be needed to make it successful. That's just one idea. I could also think of others by the way,

Laura Gent Felker (27:18):

And I think it depends on the organization, the size of the company, the culture that already exists. But I mean I think the CEOA lot of times does set the precedence right on the culture of the company and stuff. But depending on where processes lie, it could be the CFO, the CIO, chief chief strategy officer. It really depends on your organization. But the CEO probably wouldn't own the initiative, but they'd be that advocate to set the tone and then one of those chief officers would actually own it through and through.

Tiffany Perkins-Munn (27:53):

Right. Okay. Agreed.

Richie Cotton (27:56):

Alright, so whenever you're trying to change culture, there's always in fact trying to change anything at all that workers, there's always someone who complains. And so I think we need to talk a little bit about change management. So if you have employees who are kind pushing back going, well, this is the way I've been doing things for three decades now, I'm not going to change my practices. How you get them to, how do you encourage them to make more use of data or change your tools and things like that. So best practice for change management. Who wants to go first?

Tiffany Perkins-Munn (28:27):

I can start.

Richie Cotton (28:28):

Okay,

Tiffany Perkins-Munn (28:29):

Cool. Only because I have a funny analogy. When I first started out, forget way before Tom Davenport came out with the article that said, here's the sexy new career data scientist. So a decade before that I started in investment banking. And so I would go into the company and I would say, yeah, we're going to use data and analytics to talk to your clients. And they would say to me, what are you talking about? I've had these relationships with these clients for 20 years, 25 years. What on earth are you going to tell me using data and analytics that I don't already know about these clients? Because it's a very relationship driven business in that space. And so scalability is not the same as when we think about it in the consumer facing spaces. It's just a funny analogy to your point, but I think one of the things that you do to help adopt change, I would say two things actually.

(29:27):

One is I've learned over the years that pilots are really helpful, like POCs proof of concepts, we're not going to boil the ocean, we're not going to change all your processes today and start doing things new. We're going to do, take a little sample of people and do a pilot and let's see if it works. And then if it does, then let's talk about the next step, like incremental steps to get people to buy in coupled with their, this is key by the way, their involvement and ownership. They have to be involved in the decision making process. We have to go out and get their input, particularly from the naysayers, and we have to empower them to take ownership of the changes and the impact because then they feel accountable. And once you get people to feel accountable for something, they are more likely to be in support of it and to try to drive it forward successfully.

Richie Cotton (30:25):

I love the idea of a proof of concept. Laura. It seems like you might have some ideas around this.

Laura Gent Felker (30:31):

Well, not around the proof of concept, but honestly I was thinking as Tiffany was kind of talking, I really want to understand why people have that kind of fixed mindset. What is that stemming from of why they're afraid of the change? And that's really important for me to know as a stakeholder, if they say, Hey, I've been doing this for the last 10 years and I don't want to change. Is it because they fear their job's going to go away? Is it because they don't see value in what you're doing? And so really understanding that why with folks that kind of fear that change, you can then address it accordingly. And that's been how I've kind of approached it in the past and it's really been successful

Richie Cotton (31:22):

Actually speaking to your colleagues and finding out why they don't want to do something that actually sounds like very useful advice. Okay. Omar, do you have any advice on what a good first step might be for encouraging people to make more use of data or do data-driven decision making?

Omar Khawaja (31:42):

I think stepping into their shoes, understanding their pain points and the opportunities that they have with data is the starting point for me. Then you can relate to whether it's an initiative in terms of tools, introduction or an initiative in terms of process change that why this will help them either achieve that new opportunity or address the pain point that they have. So it may be you are trying to introduce a self-service data management tool, or maybe you have a new fancy data visualization tool or you have a new data catalog launch or you have a new and the best data warehouse in the cloud, for example. You choose the right audience and the wrong tool. You are ending up in a blank faces in a meeting room, right? What's in it for me? So you have the best data warehouse in front of our end business executive.

(32:34):

He's like, so what do I do with it? Do you want me to become a data engineer? So we need to definitely match the audiences we are speaking to. And earlier as we were discussing the importance of communication and targeted communication, that's so important when it comes to this. So addressing those pain points, finding those quick wins because you can't have a six month project to address a pain point which is happening today. We need to find technical quick wins. It doesn't have to be perfect, but it needs to work for that persona, that user. And I think those quick wins make a lot of impact. You get your customer referrals, you get that. Once again, that snowball effect started if you have those quick wins.

Richie Cotton (33:23):

Excellent. I like that. Start small and then build things up. We're going to go to audience questions in two minutes. So very, very quick answer on the last question. So we've talked about getting started, but as data sets keep getting bigger, a lot of organizations struggle to keep up. So what are the main blockers to scaling up your analytics? You've got something already, you want to go bigger, more data sets, bigger data sets, faster analysis, things like that. Tiffany, do you want to take this?

Tiffany Perkins-Munn (33:55):

Yeah, sure. So by the way, this is a super easy question. I'm not going to say anything that everyone on here doesn't already know. There's no secret sauce here. The blockers are always the same data quality, is the data accurate? Is it consistent? How do you avoid discrepancies? What's the reliability? Then you have infrastructure limitations. Do you have the right data storage, processing power, scalability of existing infrastructure to kind of handle increasing volumes and complexity. And then I think as we move into this data space, we have skill gaps. So where's the bridging that gap in data management analytics, data engineering so that we can effectively process and analyze these large data sets. We also have lots of integration challenges. We're migrating, we're moving from the lake to the cloud. We have siloed data sources, we have disparate data formats. So I know all of this sounds familiar. And then ultimately we have regulatory compliance issues. So as we move into new systems, new processes, we have to make sure we're compliant with data privacy regulations. Are we upholding security standards and ethical considerations when handling and analyzing our data? So that's sort of a myriad of issues, but I think all of those contribute and it depends on where organizations are in their journey really, right? To how quickly they can scale the analytics function.

Laura Gent Felker (35:27):

And to package that all up, it's identifying the bottleneck in the process and the issue. And so Tiffany laid out all the issues really well. That's where I can see is depending on where you are in your journey, you might have different bottlenecks. And then how do you relieve that bottleneck? Is it a data quality issue? Is it an infrastructure that, and how do you make sure there's funding towards that bottleneck?

Richie Cotton (35:57):

Right. Excellent.

Tiffany Perkins-Munn (35:58):

Thank you for wrapping that up so beautifully, Laura.

Richie Cotton (36:00):

That's wonderful. Yeah, so data quality and governance and then identifying bottlenecks. Excellent. Okay, we're going to have to go to audience questions now. We've got a lot of great questions. So thank you all for your insights so far. And this question comes from Ani saying, how do we reconcile an analytics led culture with gut feeling, decision making, maybe entrenched in the organization's history? Is this a battle of change management or do we need proof points? I'm not sure what proof points are, but I like that idea. You get some points for providing proof. Yeah. I know we talked a bit about change management. Do you want to talk about is there still a role for gutfield decision making?

Tiffany Perkins-Munn (36:46):

So yeah, you go go Laura. You go.

Laura Gent Felker (36:50):

I mean, I think that there can be, right, not everything's predictable, but there's still human led decision making that should be backed by data. And so being able to change the culture, it just depends, again, going back to the advocacy or friendlies Tiffany was talking about, which I really liked that I might start using that term. Tiffany.

Tiffany Perkins-Munn (37:17):

Yeah, I think Laura's absolutely right that you want to think about how we can't lose the value of human intuition when things are going well or not. You have a gut feeling. I think all of that is very important, but then how do you tie that to the quantitative results that you want to achieve? And I think this idea of proof points, proof of concepts, pilots, anything that you can utilize in a quantitative way to show why moving from a more qualitatively driven culture to a more quantitatively driven culture matters, I think is very important.

Richie Cotton (38:01):

I like that. And Omar, do you have anything you wanted to add on that

Omar Khawaja (38:06):

Plus one to the previous recommendations? I think the idea should not be to remove the human aspect from decision making. It's very important that we stay there. It's all about raising the trust level and the data so that people can rely on the data to make that decision. And is it easily accessible for them in time? If I need to make a decision today and I'm getting something in five days or five weeks, what good is it that for me? So we need to provide that all saying right information to the right people at the right time in the right format, on the right device. All those rights are absolutely important and we need to establish this trust for that. People who are the decision makers can trust that data to make that decision. And you combine that with your supporters, you combine that with communication, you combine with role modeling from the top executives that will initiate that change that we need. It is about change management,

Richie Cotton (39:08):

The point that, yeah, if you can't trust your data, you're probably not going to use it for decision making. Or if you are, it's not going to go down very well. Okay. We've got time for one or two more questions. So alright, let's go for this one. So A has asks, do you know any differences how data is leveraged in the private sector versus public sector? So maybe you can talk about this in the context of is it going to change your data culture whether you're in private sector or public sector? Yeah, lemme know your thoughts actually since we're supposed to be talking about ROI. Is what you're going to measure as your return on investment, is that going to differ if you're in a nonprofit sort of place? Does anyone want to answer this? I

Omar Khawaja (40:03):

Take, let me do that in two aspects, right? I have seen that there are public private partnerships are also a starting when it comes to data, especially on data sharing for example, because it's no longer just about the data within an enterprise. For example, in private sector. It's also about how can I leverage something which is outside? And that also means that there needs to be a fundamental shift in the culture of the organizations both on private and public side, on data sharing and collaboration. That's quite a big industry-wide change we are talking about, right? It's huge. At the same time, whether the organization is let's say public or private or they are not in profit, for example, there are goals that the organization still needs to achieve. That organization still needs to work efficiently. For example, they need to make the big bang on their investment still they need to measure whether they are initiatives in terms for data and ai, are they being used even or not? So those principles are still common. Of course the organization goals itself might be very different.

Richie Cotton (41:17):

Alright, I feel like we've come full circle back to the very first question where Tiffany's talked about, well it might not be revenue that you're measuring, it's going to be some sort of team specific goal, but you just want to make sure you're working towards something you can measure. Okay. Alright, let's do one more question. We've got two minutes left. So Ryan asks, how can we make our data sources and insights widely available across an organization while still maintaining control of the story that data tells, ensuring everyone speaking the same language. So I think that's how do you make sure data is discoverable and accessible, but also make sure that different teams understand each other? It's a tricky, this is a big topic. We need

Tiffany Perkins-Munn (42:04):

Five. It seems like, not to put Laura on the spot, but it seems like a MongoDB kind of question.

Laura Gent Felker (42:11):

Sure. I mean I think the big piece here is to make sure that there's good data owners to the process and to make sure that we educate stakeholders through and through across the board. So if we democratize data that people aren't cutting the data all differently and all have different answers for the same question, that is my worst nightmare. And so that's where part of the data literacy comes into play. Making sure there's sources of truth and data owners assigned to that, and then official reporting to make sure that there's defined official reports for certain things. So I think those are the three elements that come to mind for me with that question.

Omar Khawaja (43:03):

I'll just add hashtag Tiffany, free the data and that will lead you to the place you want to go. Go ahead Tiffany.

Tiffany Perkins-Munn (43:12):

Sorry. Thank you Omar. I was just going to add that I also think it's important for metadata strategies, right? So right now we have all these data sets. I can pull revenue from five different data sets and I have five different revenue numbers and I need meta tags to help me understand so that everyone knows what they're pulling and looking at. So sometimes even when you don't have the infrastructure in place, if you could get a tagging strategy that is consistent so that everyone who's pulling something is actually pulling the same thing, even though it's coming from different places. Because I do understand that technology buildouts can be expensive. Then you get to a place where people can start utilizing the same information and singing from the same songbook, telling similar stories, et cetera about success.

Laura Gent Felker (44:02):

It's all about keeping that shop clean. Going back to my shopkeeper analogy.

Tiffany Perkins-Munn (44:07):

That's right.

Richie Cotton (44:09):

No rats in the basement. Hopefully

Tiffany Perkins-Munn (44:13):

Not in Laura's basement.

Laura Gent Felker (44:14):

Absolutely not. No, absolutely not.

Richie Cotton (44:19):

Alright, wonderful. We've got 30 seconds left. You got 10 seconds each for final advice on how to get better at using data and making more money from it or hitting whatever ROA you want. Omar, do you want to go first? What's your last advice?

Omar Khawaja (44:34):

We talked about ROI change management. I would like to leave with the thought, be the change you want to see in the world. Be a role model of owning the data, practicing it, loving it, curating it, sharing it, taking the ownership.

Richie Cotton (44:48):

Alright, I like that. That's a call to action for everyone in the audience. Go and do something. Make data better at your own organization. Excellent. Laura, do you want to go next?

Laura Gent Felker (44:58):

Have a long-term vision but have a lot of short-term wins so you can keep seeing value over and over and over again with your data projects?

Richie Cotton (45:09):

I like that. Yeah, no waiting for years before you see some kind of benefit. And Tiffany, would you like to wrap us up?

Tiffany Perkins-Munn (45:16):

Yeah, I would say align with business objectives. So make sure your data initiatives are closely aligned with business goals, strategic priorities and key performance indicators so that you can drive tangible value and outcomes.

Richie Cotton (45:31):

I love that. Align with whatever metrics or business goals you've got. That sounds like very useful advice. With that, we are done. So you've got to hold on for the next session. I can't even remember what it is. It's been a long day. Oh, we're doing building a learning culture for analytics functions. So if you're interested in having company-wide training, then that's the session that you need to go to. I'd love to thank all of us because once again, that was absolutely amazing. Oh, we're having a break. First read just some around me, 15 minute or so break and then go to the next session. You've got time to go and grab yourself a drink. Alright, thank you Omar. Thank you Laura. Thank you Tiffany. That was amazing.

Omar Khawaja (46:11):

Thank you guys. All the best.

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