The Importance of Data Literacy and Analytics in Business Decision Making

As we explore the world of business analytics, it's essential to recognize the significance of data literacy and its impact on making informed decisions. When it comes to measuring success, organizations often focus on revenue, but this approach can be limited. According to Omar, "it might not be Revenue that you're measuring; it's going to be some sort of Team specific goal." This highlights the importance of having clear objectives in place and ensuring everyone is working towards the same goals.

One of the key challenges organizations face when implementing data-driven decision making is change management. Ryan posed an excellent question: "how can we make our data sources and insights widely available across an organization while still maintaining control of the story that data tells?" Tiffany responded by emphasizing the importance of having good data owners, educating stakeholders, and establishing official reports to define shared truths.

Another critical aspect of successful analytics is democratizing data. According to Laura, "if you democratize data, people aren't cutting the data all differently and all have different answers for the same question. That is my worst nightmare." To mitigate this issue, organizations need to invest in data literacy and provide training on how to work with data effectively.

Laura also stressed the importance of aligning data initiatives with business objectives and strategic priorities. This ensures that analytics efforts are focused on driving tangible value and outcomes, rather than just generating revenue. Tiffany added that having a tagging strategy can help make data more accessible and consistent, even when it's coming from different sources.

The final piece of advice from the speakers was to be proactive in practicing what you preach. Omar emphasized the importance of role-modeling data ownership and taking control of your own data story. Laura advised focusing on long-term vision but achieving short-term wins to maintain momentum and drive value over time. Tiffany concluded by reiterating the need for alignment with business goals, strategic priorities, and key performance indicators.

Building a Learning Culture for Analytics Functions

As we wrap up our discussion on data literacy and analytics, it's essential to recognize the importance of building a learning culture within organizations. According to the speakers, this involves providing training and education on data-related topics to ensure that everyone has the necessary skills and knowledge to work effectively with data.

The final session of the day will be dedicated to exploring best practices for building a learning culture for analytics functions. This will include discussions on how to create a supportive environment where employees feel encouraged to ask questions, share their knowledge, and learn from one another.

In conclusion, effective data literacy and analytics require more than just technical skills – they demand a deep understanding of the business context and a willingness to adapt to changing circumstances. By embracing these principles and taking proactive steps to build a learning culture within our organizations, we can unlock the full potential of data-driven decision making and drive lasting success.

Note: The article is based on the provided transcription and does not condense or summarize the content. Each part of the transcription is fully developed into a readable paragraph or section in the article.

"WEBVTTKind: captionsLanguage: enalrighty hello everyone welcome to the uh latest session please let us know where you're joining from in the chat let us know what you're excited to hear about in this session and as ever you can ask questions for the audience uh not the audience questions for the speakers uh throughout the session and we'll get to your questions at the end see lot of people joining all right we got it's scrolling very fast to keep up we got Troy from India we got IAM from Serbia we've got uh David from France Javier calling from I don't jav is calling from uh Matas from Poland we've got Aldo from Peru we've got who else we got um Sandra from somewhere scrolling too fast me I can't keep there too many of you uh anyway nice to see you all we give it uh just a few more seconds for everyone else to join us and then we're going to kick off all right so uh 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 what 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 got three fantastic guests to guide you through the process so uh first up is Laura gent fuler she's the go to market analytics lead at mongodb and she's previously the senior director of insight and scalability at Salesforce so welcome Laura and secondly we have Omar kaaja he's the chief data and analytics officer and also Global head of data and analytics at Shodan and pry who's the head of business intelligence at rash Diagnostics and he's also a founding member of the data public leadership community and last but not least we have Tiffany Perkins man she's the managing director and head of data and Analytics at uh for marketing at JP Morgan Chase and was previously the managing director and Global head of research uh for uh analytics and data at Black Rock uh so yeah welcome all three of you uh now uh 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 even thought of yet so we only bring the best of the best here at radar uh so let's hear what they have to say now to begin with since we are talking about returnal investment it be ni to know what that actually means so just in practice what does return on investment mean for data initiatives uh Tiffany do you want to leave this one yeah sure so first of all thank you Richie and radar for having us this is um I 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 um to be here so uh 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 like a specific business goal right 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 you know how are you acquiring customers are there cross- selling opportunities Etc 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 is there are there operational efficiencies are we optimizing processes are we allocating resources appropriately um 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 um decisions F faster are we building business intelligence tools to help us make more accurate speed to Market kind of decisions right and then in my space just to give an example um one of the things that really matters in terms of value for to a specific business goal is risk management right are we detecting fraud you know 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 uring ensuring that we are compliant with regulations are how are we actually using um algorithms predictive models Etc to protect our customers assets to mitigate Financial losses and basically to build that trust with the consumer so when we when I talk about Roi and value I'm talking along sort of that Continuum of metrics that matter I think it's really important that it's not always going to be directly tieable back to like the revenue of your company but actually there might be some Department specific metric that you're working towards on that excellent um and yeah really comprehensive set of metrics there I like it um all right so uh Beyond simply buying tools at a high level like what do organizations need to do to improve their data their data capabilities um so Laura you work for like a tool vendor I'm G to get you to talk about what you do Beyond tools do you want to go first on this one yeah absolutely so beyond tools I think process is one of the most imperative elements of data so really making sure that we identify in to end on what I call the data supply chain to identify where are the bottlenecks so we are able to get um a very fast and efficient set of data and metrics so we can measure that quickly um 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 illiterate 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 excellent yeah like you got have the the right supporting infrastructure um Omar do you have anything to add to that uh yes R thanks and uh please first allow me to uh show my appreciation for inviting us and glad to be here today sharing this panel discussion with Laura and Tiffany uh let me complete the triangle right Laura talked about processes I'll talk about people and uh people in my view are the key pillar of any data initiative they make and break everything so uh and when it comes to people you can uh range from Top executive Committees of the company all the top 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 the Frontline managers the sales teams the people on the shop floors and these peoples have job to do uh they know what they are 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 uh I mean process examples that Laura mentioned are so important in this uh 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 so um and 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 so how that person needs to receive the insights in that area it it the tool comes always later it's about the people it's about the process the business outcomes that Tiff anyy mentioned are so important so those are the you know just my two cents on that okay absolutely there with you that um the people side of things are very important it seems like um non-technical people are then going to need some kind of data skills but this often be quite daunting for them so do you have any advice on how to improve the um the data skills for these workers um maybe Omar do you want to go again we'll do reverse order this time yeah sure uh I can take a stab at that and how about I reverse the order of the question as well Richie okay uh it's it's not about the business people only learning tools and the tech uh I think with the with this Modern Age people are becoming Tech sevy the data traditional data skills are no longer locked in the IT department or only in the data Department uh we have more and more citizen data roles evolving while that is happening uh for each of those personas uh there should be be a targeted data literacy program AI analytics literacy program as well but I want us to step back I want us that the teams that are more Tech saavy the teams that are more data savy they also needs to understand the business as well to make an impact an organization is there for the purpose they have their Vision their ad Mission they are there to achieve that Target no matter which industry they are 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 they needs to make so we already can see that there is a nice balance it's not just oneway education about tools and Technologies and making people Tech heavy 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 uh interesting so um you're going to have the some some technical skills and some business skills and hopefully uh that's going to help people communicate um all right um Tiffany do you want to expand on this like um do you have any advice on how uh you can improve communication between like your technical employees and your business employees or other non-technical yeah yeah so yes thank you Richie so I can't stress this enough um 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 one on that right but people have been talking about it more but it's really about like training on storytelling techniques to help the technical teams convey insights in a compelling and relatable manner right 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 like no one knows right 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 in 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 love that I wanted go I wanted to add to that you know what Tiffany was talking about is we all have to be speaking the same language and know the language of our stakeholders and to end so um 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 goto 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 right 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 like agree more and more on that and uh just wanted to add to that Richie no this wonderful I like that you're both talking about storytelling here because we had a session on storytelling earli today so for anyone who missed that you're GNA have to catch up on the recording for anyone who intended I'm hoping uh you're you're aligned on this that storytelling is a really good idea in the in the data World um all right so from storytelling I think we can talk more generally about uh culture and what constitutes like a good data culture so in order to change how you work with data you gonna have to change the data culture at your organization um what are some go good goals around this like what what should you be aiming for if you say okay we want to change the culture to get better at data um oh my you've not spoken for a while you want to talk us through this how might you change your data culture I'll I'll give it a go Richie and then Laura and Tiffany please feel to join in um so there are two ways to looking at it right uh we have this is one of the misused terms a lot data driven culture data driven culture and then we recently also come up with is it is your company data driven or AI driven or data informed and all of these things out there I think companies have a culture with without data without AI there is this is how people live tell stories they talk to each other they uh they react to each other they work with each other that's a company culture is existing already in a any company where does data and AI play a role in that that's 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 and then you know we have a culture s maybe so we need to as a data leader I think it's important to go to that point understand how the companies operating what is the company's culture and Infuse this data thinking over there this means understanding the people a lot this means U building upon earlier storytelling comment how can I tell the story to a salesperson versus to an executive versus to a engineer in a in a factory for example or versus to to a planner sitting in supply chain will be very different uh uh and you need to uh really make a point that how what's in it for them so as a leader who's talking about data analytics AI they need to really understand what the how the people will use uh 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 uh they get insights in a very different way rather than re reviewing 20 reports and then doing something and then doing something with it this this is how you you can understand that uh 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 uh 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 we instead of protecting it and shackling it how can we unlock the potential of the data how can we uh give it the same love to improve data quality to maintain the data pipelines its freshness its completeness so that people can make informed decisions right so I think this aspect is very good this is hashtag free the data free the data free the data I love it I love it Tiffany yes excellent I I always called it being a data shopkeeper versus a data gatekeeper because 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 right um and I think that's going to help unlock a data culture uh to empower the user but also having those right governance right 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 you check out so that is uh one analogy as Omar was talking about it and Tiffany said free the data I was like keep that data shop nice and clean all right so we got lots of great ideas 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 defitely free the data and then Laura oh I've forgotten already oh uh make sure that your data you're governing it well actually so we've got a session later on today are all around like data quality data governance so if you're interested in that please do come back for that uh final session there we're going to discuss that in detail um all right 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 like which teams need to be involved which roles what do they actually do um yeah go on to Tiffany do you want to talk us through like what's the process here like who needs to do what yeah so I think that this can happen in in one of two ways right 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 ground swell there's interest and it's not of bubles 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 um the executive team because you need the executives at at the top to Champion the initiatives to set the tone right for the data culture transformation and obviously to allocate the resources so let's not forget that um then you also need need the data and analytics teams obviously right they you need to empower them all of the scientists the analysts the engineers they 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 the organization in this space um and then obviously the partners are the business units and operations right you have to get them to integrate the data into the daily operations into the decision Mak making processes and the performance metrics um 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 right the people who are 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 does that pulls that together but regardless I think those five roles or those five teams are really critical in making a lasting um long-term data culture change um okay lot lots and lots of teams involved there so we got Executives we've got data practitioners HR business functions all sorts mostly business GNA be involved in this um I'm can I just say Richie really quickly that when you when people I think um one problem that a lot of organizations face is that they try to do it with one or two of those teams like 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 right 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 all right so um it's going to have to end up being pretty broadscale with lots of people involved I'm wondering how do you get started do you have to mobilize the whole business at once or there are a few people who you can begin with um Laura do you want to take this sure I mean I start with a small cohort of people right and I can't agree more with Tiffany making sure the right infrastructure essentially is uh 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 uh the data culture at the organization but I always try to find a cohort of Advocates within the business um to kind of start with right and then they actually will speak the 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 um and because not everybody knows everything that we can do across data and we don't have to do data science a day one right nor would I recommend that but at that with that said like 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 all right so you get the people who want to be involved involved first and then hopefully things can grow from there then that snowball it's like 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 it go down the mountain right um and so I I really believe that if you get a few strategic partners and you really Market it uh accordingly the people that might not have been as like excited about a data informed culture going towards uh more data organization they'll then say Oh that's in it for me too I was to Laura oh I'm sorry just to add Laura we call those people in the business friendlies like go find some friendlies Who Will Champion your idea right and then you take that small cohort 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 because then people will start to see how it's impacting different initiatives and they'll start to you know it'll have a little bit of a Snowball Effect like Laura said I like that uh yeah uh building things up one separate time um okay uh so Omar go on uh you was 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 companies 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 uh doing these lunch and learn sessions coffee chats virtual or or in person this really creates that Snowball Effect it triggers that Snowball Effect cannot stress enough to do that I like the idea of having just some uh regular sort of training sessions just to make sure that people are on board one thing um that's been mentioned a couple of times is that Executives need to involved we don't really talked 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 like who's going to be in charge of um changing data culture uh do you all want to just say say who you think should be in charge so I think a lot of Executives will need to be to sign off on it but clearly you can't have everybody at the table being the owner it feels like a strategic initiative right so like a chief strategy officer but in part in Partnership as we've all said here with the data owners and the and the business owners right but in because it is a companywide um 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 um with like 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 and I think it depends on the organization the size of the company the culture that already exists but I mean I think the CEO a lot of times does set the president right on the culture of the company and stuff but depending on where processes lie it could be this CFO the CIO chief chief sta strategy officer it really depends on your organization but the CEO probably wouldn't own the initiative but they'd be that you know Advocate to set the tone and then one of those Chief officers would actually own it through and through right okay agreed um all right so whenever you're trying to change culture there's always some in fact trying to change anything at all that work there's always someone who complains uh and so uh I think we need to talk a little bit about change management so if you have employees who are kind of pushing back going well this is the way I've been doing things for like three decades now I'm not going to change my practices how do you get them to how do you encourage them to uh make more use of data or change your tools and things like that um okay so best practices for change management uh who wants to go first um uh I can can start okay Co only because I have a funny analogy um when I first started out like forget like way before um Tom Davenport came out with the article that said here's the sexy new career data scientist so a decade before that right I would go I started in Investment Banking and so I would go into in the the company and I would say yeah we're GNA use data and analytics to talk to your clients and they would say to me what what are you talking about I've had these relationships with these clients for 20 years 25 years like what on Earth are you going to tell me using data and analytics that I don't already know about these clients because you know it's a very um relationship driven business in that space right 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 um but I think one of the things that you do to help adopt change I would say two things actually one is I've learned over the years that Pilots are really helpful like poc's proof of Concepts like let's not we're not going to boil the ocean we're not going to change all your processes today and start doing things new tomorrow we're gonna 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 right 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 right 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 I love the idea of a proof of concept um Lauren seems like you might have some ideas around this 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 right is it because they don't see value in what you're doing right um and so really understanding that why uh with folks that kind of fear that change you can then address it accordingly right and that's been how I've kind of approached it in the past and it's really been successful okay actually speaking to colleagues from finding out why they don't want to do something El actually sounds like a very uh useful advice um okay uh Omar um do you have any advice on like what a good sort of um first step might be for like getting people like encouraging people to make more use of data or do data driven decision making I think uh stepping into their shoes understanding their pain points and the opportunities that they have with data is the the starting point for me then you can relate to whether it's a initiative in terms of tools introduction or an in I ative 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 uh you choose the Right audience and the wrong tool you are ending up in a blank faces and meeting room right what's in it for me so you have the best data warehouse in front of a end business executive he's like uh so what do I do with it do you want me to become a data engineer now so we need to definitely match the audiences we are speaking to and uh 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 paino which is happening today we need to find tactical 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 RS excellent I like that um start small and then build things up um we're going to go to audience questions in two minutes so very very quick answer on the last question um so we've talked about getting started but as data sets keep getting bigger a lot of organizations struggle to keep up keep up so one of them L blockers to scaling up your analytics capabilities you got something already you want to go bigger more data sets bigger data sets faster analysis things like that um Tiffany do you want to take this yeah sure um 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 you know 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 sto you know data storage processing power scalability of existing infrastructure to kind of handle increasing volumes and complexity um and then I think as we move into the this data space We Have Skills skill gaps right 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 uh we also have lots of integration challenges right we're migrating we Mo we're moving from the lake to the cloud you know we have Silo data sources we have disparate data formats right so I know all of this sounds familiar and then ultimately we have Regulatory Compliance issues right 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 are in their Journey really right to how quickly they can scale the analytics function and to package that all up it's identifying the bottleneck in the process right and the issue and so Tiffany laid out all the issues um really well and so that's where I can kind of see is like 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 infastructure that and how do you make sure there's funding towards that bottleneck right excellent thank you for wrapping that up so beautifully Laura that's wonderful yeah so data quality in governance and then identifying bottlenecks excellent um okay we're gonna have to go to audience questions now because we've got a lot of great questions so uh thank you all for your Insight so far and uh this question comes from aan saying how do we reconcile an analytics L culture with gut feeling decision making maybe entrenching the organization's history uh is this a battle of change management or do we need uh proof points I'm not sure what proof points are but I like that idea like you get some points for providing proof um yeah I know we talked a bit about change management do you want to um talk about like what is there still a role for gutfield decision making so I yeah you go go Laura you go I mean I think that there there can be right not everything's predictable but there should be like there Still Human Le decision making that should be backed by data right and so being able to change the culture it just depends again going back to that AB advocacy or friendly as Tiffany was talking about which I really like that I might start using that term Tiffany um yeah I think Laura's absolutely right that you want to think about how do you human we can't lose the the like the value of human intuition right like you know 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 um Pilots you know 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 um I like that and um Omar do you have anything you wanted to add on that 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 uh 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 if I need to make a decision today and I'm getting something in five days or five weeks what good good is it that for me so we need to provide that you know 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 so that people who are the decision makers can trust that data to make that decision uh and you combine that with your supporters you combine that with communication you combine with role modeling from the top Executives that that will initiate that change that we need it is about change management okay I like the point that yeah if you can't trust your data you're probably not going to use it for decision making that's or if you are it's it's not going to go down very well um okay we've got time for one or two more questions so um all right let's go for this one so uh anus asks um do you know any differences how data is leveraged in the private sector versus public sector so um maybe you can uh talk about this in the context of like how is it going to change how your data culture whether you're in private sector or public sector um yeah let me let me know your thoughts actually since we're supposed to talk about Roi is is what you're going to measure as like your return on investment is that going to differ if you're like in a nonprofit sort of place does anyone want to answer this I don't I I take let me let me do that in two aspects right I have seen that there are public private Partnerships are also 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 how can I leverage something which is outside and that also means that there needs to be a fun fundamental shift in the culture of the organizations both on private and public side on data sharing and collaboration that's a quite a big industrywide change we are talking about right it's it's huge uh at the same time whether the organization is let's say public or private or they are not in profit for example right there are still there are goals that that organization still needs to achieve that organization still needs to work efficiently for example they needs to make the Big Bang on their investment still they need to measure whether they are initiatives in terms of for data and AI are they being used even or not so those principles are still common uh of course the organization goals itself might be very different all right I feel like we've come full circle back to the very first question where was talk about well it might not be Revenue that you're measuring it's going to be like some sort of Team specific goal but you just want to make sure you're working towards something you can measure um okay uh all right let's do one more question we got two minutes left uh so uh 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 and ensuring every speaking the same language uh so I think that's how do you make sure data is discoverable and accessible but also make sure that different teams are understand each other um it's a tricky this is a big topic we need not to put Laura on the spot but it seems like a mongod DB kind of question sure um 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 uh educate stakeholders through and through across the board right um so if we democratize data you know 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 um that's where part of the data literacy comes into play making sure there 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 I will answer it with I will just add a hasht Tiffany free the data and that's that will lead you to the place you want to go go ahead Tiffany sorry thank you om I was just going to add that I also think it's important for um sort of meta 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 num and I need metat tags right to help me understand so that everyone knows what they're pulling and looking at so sometimes even when you don't have the infastructure 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 build outs can be um expensive then then you get to a place where people can start kind of utilizing the same information and singing from the same song book telling similar stories Etc about success it's all about keeping that Shop clean going back to my shopkeeper analogy that's right yeah uh no rats in the basement hopefully not in law's basement absolutely not no absolutely not all right wonderful uh we got 30 seconds left so 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 uh what's your advice 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 all right 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 uh Laura do you want to go next have h 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 I like that yeah uh no waiting for years before you see some kind of uh benefit and Tiffany uh would you like to wrap us up yeah I would say um align with business objectives so make sure your data initiatives are closely aligned with business goals strategic priorities and key performance indicator so that you can drive tangible value and outcomes I love that align with whatever metrics or uh business goals you've got that sounds like very useful advice okay with that we are done so uh you got to hold on for the next session I I from my board is it's been a long day uh oh we're doing building a learning culture for analytics functions uh so if you're interested in having uh companywide training then that's the session that you need to go to i' like to thank all of our speakers again that was absolutely amazing oh we're having a break first just mared me 15 minute or so break and then uh come go to the next session youve got time to go and grab yourself a drink all right uh thank you uh Omar thank you Laura thank you Tiffany that was amazing thank you guys all the bestalrighty hello everyone welcome to the uh latest session please let us know where you're joining from in the chat let us know what you're excited to hear about in this session and as ever you can ask questions for the audience uh not the audience questions for the speakers uh throughout the session and we'll get to your questions at the end see lot of people joining all right we got it's scrolling very fast to keep up we got Troy from India we got IAM from Serbia we've got uh David from France Javier calling from I don't jav is calling from uh Matas from Poland we've got Aldo from Peru we've got who else we got um Sandra from somewhere scrolling too fast me I can't keep there too many of you uh anyway nice to see you all we give it uh just a few more seconds for everyone else to join us and then we're going to kick off all right so uh 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 what 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 got three fantastic guests to guide you through the process so uh first up is Laura gent fuler she's the go to market analytics lead at mongodb and she's previously the senior director of insight and scalability at Salesforce so welcome Laura and secondly we have Omar kaaja he's the chief data and analytics officer and also Global head of data and analytics at Shodan and pry who's the head of business intelligence at rash Diagnostics and he's also a founding member of the data public leadership community and last but not least we have Tiffany Perkins man she's the managing director and head of data and Analytics at uh for marketing at JP Morgan Chase and was previously the managing director and Global head of research uh for uh analytics and data at Black Rock uh so yeah welcome all three of you uh now uh 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 even thought of yet so we only bring the best of the best here at radar uh so let's hear what they have to say now to begin with since we are talking about returnal investment it be ni to know what that actually means so just in practice what does return on investment mean for data initiatives uh Tiffany do you want to leave this one yeah sure so first of all thank you Richie and radar for having us this is um I 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 um to be here so uh 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 like a specific business goal right 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 you know how are you acquiring customers are there cross- selling opportunities Etc 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 is there are there operational efficiencies are we optimizing processes are we allocating resources appropriately um 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 um decisions F faster are we building business intelligence tools to help us make more accurate speed to Market kind of decisions right and then in my space just to give an example um one of the things that really matters in terms of value for to a specific business goal is risk management right are we detecting fraud you know 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 uring ensuring that we are compliant with regulations are how are we actually using um algorithms predictive models Etc to protect our customers assets to mitigate Financial losses and basically to build that trust with the consumer so when we when I talk about Roi and value I'm talking along sort of that Continuum of metrics that matter I think it's really important that it's not always going to be directly tieable back to like the revenue of your company but actually there might be some Department specific metric that you're working towards on that excellent um and yeah really comprehensive set of metrics there I like it um all right so uh Beyond simply buying tools at a high level like what do organizations need to do to improve their data their data capabilities um so Laura you work for like a tool vendor I'm G to get you to talk about what you do Beyond tools do you want to go first on this one yeah absolutely so beyond tools I think process is one of the most imperative elements of data so really making sure that we identify in to end on what I call the data supply chain to identify where are the bottlenecks so we are able to get um a very fast and efficient set of data and metrics so we can measure that quickly um 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 illiterate 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 excellent yeah like you got have the the right supporting infrastructure um Omar do you have anything to add to that uh yes R thanks and uh please first allow me to uh show my appreciation for inviting us and glad to be here today sharing this panel discussion with Laura and Tiffany uh let me complete the triangle right Laura talked about processes I'll talk about people and uh people in my view are the key pillar of any data initiative they make and break everything so uh and when it comes to people you can uh range from Top executive Committees of the company all the top 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 the Frontline managers the sales teams the people on the shop floors and these peoples have job to do uh they know what they are 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 uh I mean process examples that Laura mentioned are so important in this uh 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 so um and 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 so how that person needs to receive the insights in that area it it the tool comes always later it's about the people it's about the process the business outcomes that Tiff anyy mentioned are so important so those are the you know just my two cents on that okay absolutely there with you that um the people side of things are very important it seems like um non-technical people are then going to need some kind of data skills but this often be quite daunting for them so do you have any advice on how to improve the um the data skills for these workers um maybe Omar do you want to go again we'll do reverse order this time yeah sure uh I can take a stab at that and how about I reverse the order of the question as well Richie okay uh it's it's not about the business people only learning tools and the tech uh I think with the with this Modern Age people are becoming Tech sevy the data traditional data skills are no longer locked in the IT department or only in the data Department uh we have more and more citizen data roles evolving while that is happening uh for each of those personas uh there should be be a targeted data literacy program AI analytics literacy program as well but I want us to step back I want us that the teams that are more Tech saavy the teams that are more data savy they also needs to understand the business as well to make an impact an organization is there for the purpose they have their Vision their ad Mission they are there to achieve that Target no matter which industry they are 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 they needs to make so we already can see that there is a nice balance it's not just oneway education about tools and Technologies and making people Tech heavy 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 uh interesting so um you're going to have the some some technical skills and some business skills and hopefully uh that's going to help people communicate um all right um Tiffany do you want to expand on this like um do you have any advice on how uh you can improve communication between like your technical employees and your business employees or other non-technical yeah yeah so yes thank you Richie so I can't stress this enough um 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 one on that right but people have been talking about it more but it's really about like training on storytelling techniques to help the technical teams convey insights in a compelling and relatable manner right 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 like no one knows right 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 in 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 love that I wanted go I wanted to add to that you know what Tiffany was talking about is we all have to be speaking the same language and know the language of our stakeholders and to end so um 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 goto 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 right 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 like agree more and more on that and uh just wanted to add to that Richie no this wonderful I like that you're both talking about storytelling here because we had a session on storytelling earli today so for anyone who missed that you're GNA have to catch up on the recording for anyone who intended I'm hoping uh you're you're aligned on this that storytelling is a really good idea in the in the data World um all right so from storytelling I think we can talk more generally about uh culture and what constitutes like a good data culture so in order to change how you work with data you gonna have to change the data culture at your organization um what are some go good goals around this like what what should you be aiming for if you say okay we want to change the culture to get better at data um oh my you've not spoken for a while you want to talk us through this how might you change your data culture I'll I'll give it a go Richie and then Laura and Tiffany please feel to join in um so there are two ways to looking at it right uh we have this is one of the misused terms a lot data driven culture data driven culture and then we recently also come up with is it is your company data driven or AI driven or data informed and all of these things out there I think companies have a culture with without data without AI there is this is how people live tell stories they talk to each other they uh they react to each other they work with each other that's a company culture is existing already in a any company where does data and AI play a role in that that's 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 and then you know we have a culture s maybe so we need to as a data leader I think it's important to go to that point understand how the companies operating what is the company's culture and Infuse this data thinking over there this means understanding the people a lot this means U building upon earlier storytelling comment how can I tell the story to a salesperson versus to an executive versus to a engineer in a in a factory for example or versus to to a planner sitting in supply chain will be very different uh uh and you need to uh really make a point that how what's in it for them so as a leader who's talking about data analytics AI they need to really understand what the how the people will use uh 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 uh they get insights in a very different way rather than re reviewing 20 reports and then doing something and then doing something with it this this is how you you can understand that uh 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 uh 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 we instead of protecting it and shackling it how can we unlock the potential of the data how can we uh give it the same love to improve data quality to maintain the data pipelines its freshness its completeness so that people can make informed decisions right so I think this aspect is very good this is hashtag free the data free the data free the data I love it I love it Tiffany yes excellent I I always called it being a data shopkeeper versus a data gatekeeper because 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 right um and I think that's going to help unlock a data culture uh to empower the user but also having those right governance right 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 you check out so that is uh one analogy as Omar was talking about it and Tiffany said free the data I was like keep that data shop nice and clean all right so we got lots of great ideas 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 defitely free the data and then Laura oh I've forgotten already oh uh make sure that your data you're governing it well actually so we've got a session later on today are all around like data quality data governance so if you're interested in that please do come back for that uh final session there we're going to discuss that in detail um all right 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 like which teams need to be involved which roles what do they actually do um yeah go on to Tiffany do you want to talk us through like what's the process here like who needs to do what yeah so I think that this can happen in in one of two ways right 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 ground swell there's interest and it's not of bubles 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 um the executive team because you need the executives at at the top to Champion the initiatives to set the tone right for the data culture transformation and obviously to allocate the resources so let's not forget that um then you also need need the data and analytics teams obviously right they you need to empower them all of the scientists the analysts the engineers they 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 the organization in this space um and then obviously the partners are the business units and operations right you have to get them to integrate the data into the daily operations into the decision Mak making processes and the performance metrics um 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 right the people who are 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 does that pulls that together but regardless I think those five roles or those five teams are really critical in making a lasting um long-term data culture change um okay lot lots and lots of teams involved there so we got Executives we've got data practitioners HR business functions all sorts mostly business GNA be involved in this um I'm can I just say Richie really quickly that when you when people I think um one problem that a lot of organizations face is that they try to do it with one or two of those teams like 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 right 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 all right so um it's going to have to end up being pretty broadscale with lots of people involved I'm wondering how do you get started do you have to mobilize the whole business at once or there are a few people who you can begin with um Laura do you want to take this sure I mean I start with a small cohort of people right and I can't agree more with Tiffany making sure the right infrastructure essentially is uh 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 uh the data culture at the organization but I always try to find a cohort of Advocates within the business um to kind of start with right and then they actually will speak the 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 um and because not everybody knows everything that we can do across data and we don't have to do data science a day one right nor would I recommend that but at that with that said like 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 all right so you get the people who want to be involved involved first and then hopefully things can grow from there then that snowball it's like 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 it go down the mountain right um and so I I really believe that if you get a few strategic partners and you really Market it uh accordingly the people that might not have been as like excited about a data informed culture going towards uh more data organization they'll then say Oh that's in it for me too I was to Laura oh I'm sorry just to add Laura we call those people in the business friendlies like go find some friendlies Who Will Champion your idea right and then you take that small cohort 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 because then people will start to see how it's impacting different initiatives and they'll start to you know it'll have a little bit of a Snowball Effect like Laura said I like that uh yeah uh building things up one separate time um okay uh so Omar go on uh you was 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 companies 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 uh doing these lunch and learn sessions coffee chats virtual or or in person this really creates that Snowball Effect it triggers that Snowball Effect cannot stress enough to do that I like the idea of having just some uh regular sort of training sessions just to make sure that people are on board one thing um that's been mentioned a couple of times is that Executives need to involved we don't really talked 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 like who's going to be in charge of um changing data culture uh do you all want to just say say who you think should be in charge so I think a lot of Executives will need to be to sign off on it but clearly you can't have everybody at the table being the owner it feels like a strategic initiative right so like a chief strategy officer but in part in Partnership as we've all said here with the data owners and the and the business owners right but in because it is a companywide um 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 um with like 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 and I think it depends on the organization the size of the company the culture that already exists but I mean I think the CEO a lot of times does set the president right on the culture of the company and stuff but depending on where processes lie it could be this CFO the CIO chief chief sta strategy officer it really depends on your organization but the CEO probably wouldn't own the initiative but they'd be that you know Advocate to set the tone and then one of those Chief officers would actually own it through and through right okay agreed um all right so whenever you're trying to change culture there's always some in fact trying to change anything at all that work there's always someone who complains uh and so uh I think we need to talk a little bit about change management so if you have employees who are kind of pushing back going well this is the way I've been doing things for like three decades now I'm not going to change my practices how do you get them to how do you encourage them to uh make more use of data or change your tools and things like that um okay so best practices for change management uh who wants to go first um uh I can can start okay Co only because I have a funny analogy um when I first started out like forget like way before um Tom Davenport came out with the article that said here's the sexy new career data scientist so a decade before that right I would go I started in Investment Banking and so I would go into in the the company and I would say yeah we're GNA use data and analytics to talk to your clients and they would say to me what what are you talking about I've had these relationships with these clients for 20 years 25 years like what on Earth are you going to tell me using data and analytics that I don't already know about these clients because you know it's a very um relationship driven business in that space right 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 um but I think one of the things that you do to help adopt change I would say two things actually one is I've learned over the years that Pilots are really helpful like poc's proof of Concepts like let's not we're not going to boil the ocean we're not going to change all your processes today and start doing things new tomorrow we're gonna 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 right 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 right 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 I love the idea of a proof of concept um Lauren seems like you might have some ideas around this 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 right is it because they don't see value in what you're doing right um and so really understanding that why uh with folks that kind of fear that change you can then address it accordingly right and that's been how I've kind of approached it in the past and it's really been successful okay actually speaking to colleagues from finding out why they don't want to do something El actually sounds like a very uh useful advice um okay uh Omar um do you have any advice on like what a good sort of um first step might be for like getting people like encouraging people to make more use of data or do data driven decision making I think uh stepping into their shoes understanding their pain points and the opportunities that they have with data is the the starting point for me then you can relate to whether it's a initiative in terms of tools introduction or an in I ative 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 uh you choose the Right audience and the wrong tool you are ending up in a blank faces and meeting room right what's in it for me so you have the best data warehouse in front of a end business executive he's like uh so what do I do with it do you want me to become a data engineer now so we need to definitely match the audiences we are speaking to and uh 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 paino which is happening today we need to find tactical 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 RS excellent I like that um start small and then build things up um we're going to go to audience questions in two minutes so very very quick answer on the last question um so we've talked about getting started but as data sets keep getting bigger a lot of organizations struggle to keep up keep up so one of them L blockers to scaling up your analytics capabilities you got something already you want to go bigger more data sets bigger data sets faster analysis things like that um Tiffany do you want to take this yeah sure um 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 you know 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 sto you know data storage processing power scalability of existing infrastructure to kind of handle increasing volumes and complexity um and then I think as we move into the this data space We Have Skills skill gaps right 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 uh we also have lots of integration challenges right we're migrating we Mo we're moving from the lake to the cloud you know we have Silo data sources we have disparate data formats right so I know all of this sounds familiar and then ultimately we have Regulatory Compliance issues right 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 are in their Journey really right to how quickly they can scale the analytics function and to package that all up it's identifying the bottleneck in the process right and the issue and so Tiffany laid out all the issues um really well and so that's where I can kind of see is like 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 infastructure that and how do you make sure there's funding towards that bottleneck right excellent thank you for wrapping that up so beautifully Laura that's wonderful yeah so data quality in governance and then identifying bottlenecks excellent um okay we're gonna have to go to audience questions now because we've got a lot of great questions so uh thank you all for your Insight so far and uh this question comes from aan saying how do we reconcile an analytics L culture with gut feeling decision making maybe entrenching the organization's history uh is this a battle of change management or do we need uh proof points I'm not sure what proof points are but I like that idea like you get some points for providing proof um yeah I know we talked a bit about change management do you want to um talk about like what is there still a role for gutfield decision making so I yeah you go go Laura you go I mean I think that there there can be right not everything's predictable but there should be like there Still Human Le decision making that should be backed by data right and so being able to change the culture it just depends again going back to that AB advocacy or friendly as Tiffany was talking about which I really like that I might start using that term Tiffany um yeah I think Laura's absolutely right that you want to think about how do you human we can't lose the the like the value of human intuition right like you know 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 um Pilots you know 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 um I like that and um Omar do you have anything you wanted to add on that 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 uh 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 if I need to make a decision today and I'm getting something in five days or five weeks what good good is it that for me so we need to provide that you know 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 so that people who are the decision makers can trust that data to make that decision uh and you combine that with your supporters you combine that with communication you combine with role modeling from the top Executives that that will initiate that change that we need it is about change management okay I like the point that yeah if you can't trust your data you're probably not going to use it for decision making that's or if you are it's it's not going to go down very well um okay we've got time for one or two more questions so um all right let's go for this one so uh anus asks um do you know any differences how data is leveraged in the private sector versus public sector so um maybe you can uh talk about this in the context of like how is it going to change how your data culture whether you're in private sector or public sector um yeah let me let me know your thoughts actually since we're supposed to talk about Roi is is what you're going to measure as like your return on investment is that going to differ if you're like in a nonprofit sort of place does anyone want to answer this I don't I I take let me let me do that in two aspects right I have seen that there are public private Partnerships are also 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 how can I leverage something which is outside and that also means that there needs to be a fun fundamental shift in the culture of the organizations both on private and public side on data sharing and collaboration that's a quite a big industrywide change we are talking about right it's it's huge uh at the same time whether the organization is let's say public or private or they are not in profit for example right there are still there are goals that that organization still needs to achieve that organization still needs to work efficiently for example they needs to make the Big Bang on their investment still they need to measure whether they are initiatives in terms of for data and AI are they being used even or not so those principles are still common uh of course the organization goals itself might be very different all right I feel like we've come full circle back to the very first question where was talk about well it might not be Revenue that you're measuring it's going to be like some sort of Team specific goal but you just want to make sure you're working towards something you can measure um okay uh all right let's do one more question we got two minutes left uh so uh 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 and ensuring every speaking the same language uh so I think that's how do you make sure data is discoverable and accessible but also make sure that different teams are understand each other um it's a tricky this is a big topic we need not to put Laura on the spot but it seems like a mongod DB kind of question sure um 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 uh educate stakeholders through and through across the board right um so if we democratize data you know 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 um that's where part of the data literacy comes into play making sure there 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 I will answer it with I will just add a hasht Tiffany free the data and that's that will lead you to the place you want to go go ahead Tiffany sorry thank you om I was just going to add that I also think it's important for um sort of meta 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 num and I need metat tags right to help me understand so that everyone knows what they're pulling and looking at so sometimes even when you don't have the infastructure 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 build outs can be um expensive then then you get to a place where people can start kind of utilizing the same information and singing from the same song book telling similar stories Etc about success it's all about keeping that Shop clean going back to my shopkeeper analogy that's right yeah uh no rats in the basement hopefully not in law's basement absolutely not no absolutely not all right wonderful uh we got 30 seconds left so 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 uh what's your advice 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 all right 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 uh Laura do you want to go next have h 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 I like that yeah uh no waiting for years before you see some kind of uh benefit and Tiffany uh would you like to wrap us up yeah I would say um align with business objectives so make sure your data initiatives are closely aligned with business goals strategic priorities and key performance indicator so that you can drive tangible value and outcomes I love that align with whatever metrics or uh business goals you've got that sounds like very useful advice okay with that we are done so uh you got to hold on for the next session I I from my board is it's been a long day uh oh we're doing building a learning culture for analytics functions uh so if you're interested in having uh companywide training then that's the session that you need to go to i' like to thank all of our speakers again that was absolutely amazing oh we're having a break first just mared me 15 minute or so break and then uh come go to the next session youve got time to go and grab yourself a drink all right uh thank you uh Omar thank you Laura thank you Tiffany that was amazing thank you guys all the best\n"