Session: Breaking Silos – Data Democracy To Maximise Teams Effort - Alla Tytarenko, devtodev.

[Music] up next uh we have a session from IL ilano um from Dev to Dev uh who's going to be talking about breaking silos and using data to De democratize and maximize team efforts uh so please warm welcome on on board hi everyone let's start uh my name is Allah I from De to Def and we're a product analytics platform for game developers and we're helping maximize g games LTV and provide tools to understand user behavior and drive product growth through data driven insight and first thanks for being here I know it's the second day of the conference it's after lunch I know every

one is uh very exhausted and those two days have been very active so thanks for being here and I'll try to keep it light bright and very informative so quick intro about myself uh I've been in um gaming industry for the last eight years and U here's uh a snapshot at our awesome de to death team and uh as you may see by the Christmas trees uh those pictures have been pretty recent and and we have offices in Cyprus in vus liania so we are used to working remotely from each other and sharing the same data language and to be able to communicate the same data language is vital for us and uh that's why this topic is very important for us as for any gaming company as well so uh let's break data Silas together and let's try to unveil why data Silas actually exist and why they

mean well sometimes teams are working isolated from each other and they sometimes look at the same metrics but they are working isolated from each other and they have different sources of the same data and sometimes different departments have separate goals for themselves that are not necessarily aligned with the whole company's goals and sometimes they operate different kpis and metrics and therefore uh those Silas are created and teams are working working in them so it's important to break them to be able to grow products together so today's topic is actually how to break the data Silas and uh we'll start with creating a unified data strategy then we will move forward to uh shared qpi and metrics and uh finally inent we'll Empower all teams and find out how to do that uh with accessible users for the whole team and hopefully in that we'll get to um letting us uh

and letting data Guide our data during decisions and move products forward so why data silus are a problem well from our experience and from experience from our clients as well uh sometimes users within the same company might have limited access to data and uh they have inconsistent matrics and keep eyes because sometimes for you this metrix means one thing and for the other team it means completely other thing and you have to move slack messages back and forth all the time or wait for reports uh for a very long time and um and in the end you're duplicating your team's efforts and sometimes you're waiting for such a long time for for example the report that is necessary to make some of the decisions or on implementing a new feature and you end up waiting for it for a very long time and then you're like okay fine I'm just going to skip it and download the report or just create

an SQL query and in the end you get the same report from your data team and it's actually a manitous nightmare because you're duplicating team's efforts and you're doing someone else's job and in then um you could have spent this time doing something else and probably more efficiently so and it all comes from being isolated from the common data language uh sometimes it's unclear who owns the data who's responsible for updating the data well it actually is a nightmare and uh it causes delays in making decisions um the con uh the priorities are conflicted uh teams is uh teams are irritated nobody is happy and sometimes data that you operated is already outdated well it's not nice and it ends up in slowing in the product growth and in the end well of course nobody wants that so a bit of therapy here uh first

step in solving any problem is actually recognizing that you have one and uh from my experience and from experience from our clients as well uh the main causes of operating in isolated data Silas are having no clear data strategy uh sometimes um metrics are um I inconsistent to through the teams and definitions are not the same for the every team member um sometimes could be that uh there is a very high data access threshold especially if on a technical users it is pretty pretty complicated to get access to the data and actually make data driven decisions out of it so what could be a solution well let's start with creating a unified data strategy now I know this may sound a bit overwhelming but even if you're moving slowly in the direction of having this

common strategy and unified data strategy even small steps count in that direction you could simply start with making an inventory list of all the tools that all the teams are using well it could be this exam uh it could be like a small spreadsheet a spreadsheet uh divided by the product by the teams uh and just simply list out all the tools that all those teams are using uh those could be uh then start looking into um overlapping tools or maybe the gaps in the tools in the gaps that you see that uh some somebody is actually using maybe not the most sophisticated tools uh that they should and it slows down their decision process after you've done that you need to define the metrics and shared kpis that you team is focusing

on and uh for that you're just agreeing on Cor metrics for example you can agree that your whole team is focuses on retention rpu and LTV The Next Step would be just figuring out what those metrics mean for each team and with the case uh if we're talking about retention for example uh for the C Level the retention could mean rolling retention for product team the retention would be a classic retention and for marketing team the retention would be a 24-hour retention and if you don't align on those uh terms you can end up with three different numbers and uh well then you'll spend hours and days figuring out which one you should use which one you're operating and why your data does not align so once you align you can for example agree that okay we're calculating retention as a percentage of users who is coming back within the 24-hour period okay you have it set and

in stone and it could be a Confluence page it could be an oce page it could be a dock entry but the main thing is to have this uh shared uh access to this uh entry and have those standardized definitions uh similar like accessible easily accessible for the whole team uh then you're syncing teams on those main metrics and uh then using the source as a single source of Truth when making data during decisions The Next Step would be empowering all teams with accessible tools and the first step uh could be um having a a data governance having someone in your team to overlook data governance governance so for example if you have a very large team it could be a whole department if your team is smaller uh it could be one person who is responsible or even half a person maybe somebody will be share those responsibilities uh with everyday job but there is one person person who is responsible for uh creating dashboard

for having tool stack and uh thinking about sh sharable dashboard think automated dashboards uh that are simplifying uh usage of analytics and access to data for uh non analytics and um non uh Tech users think automated reporting and share dashboards for example at defto def it could be a shared dashboard uh that you can have user activity uh in-game balance retention metrics with a comments with a des clear description that is easily accessible for the whole team and you can actually take users over there and uh have maybe a small discussion or and to go through all those metrics and uh that's pretty convenient and uh pretty easily maintain maintained you can set it up once and then just go with it once you get there you can actually use data to guide decisions and resolve

your and resolve conflicts if you have some um well because data is neutral and you know that this is the same data for the whole team and it's not someone else's opinion over someone else's opinion and you're figuring out okay whose opinion is more important it's actually data and metrics and uh you set a clear success metric and you know that these criteria are the same for the whole team and uh for this project and then you can actually uh test those ideas if for example you decide to uh have the button green or red and you can actually test those assumptions with AB experiments and not rely on someone else's uh thought and opinion in that so let's move to the case study from one of our clients uh this is a gaming studio uh they have a few mat three projects and when they came to us they had a real clear problem which they

described as data chaos they didn't know um how to implement how to use the data for example they were implementing a feature and they couldn't really use that experience through across different teams because it took a lot of slack messages and huddles to go back and forth with the metrics and once they implemented Dev to Dev uh they um centralized their data and uh they de toep helped them align their product marketing and Tech teams uh for the outcome uh they gave us actually recent feedback a few a few weeks ago that uh they said that they reduced decision time and communication became so much easier with u within the teams and it was actually pretty easy well it was easier to use other teams experiences for example when they implemented new features they could rely on those metrics and actually use them as Bank benchmarks and well as a nice

bonus uh they reduced the amount of tools that they were having because they were able to use de to Dev Sab tests and push notifications which they used as a third from the third parties and they all integrated in De to Dev so that's pretty awesome of course that didn't happen in one day but it's definitely simplified their life um remember this tool stack it's uh actually a another case study from one of our clients before they implemented Dev to Dev they had pretty chaotic and overlapping uh tools uh different teams used uh different tools for basically the same thing and um once they were figuring out okay what they need to change and they decided they will be moving forward and integrated de to death uh they cleared out the tool stack make it Slicker and actually more efficient and uh well of course um that

really helped and that makes us really happy it didn't happen of course in one day as well uh but in the end the whole integration of De to Dev took them only four hours and another case study from um from our clients well it's not actually a case study uh it's it's a small feedback on uh how um dep toep helped Mobile gaming studio clear out their communication and uh make better collaboration through sharing the same data language uh those are one of our clients and of course it's always it's always nice to hear this uh feedback so let's move to the uh recap of what we've heard today and uh okay data Silas are are an issue and once you realize that you have them uh once you realize that your team are

operating different numbers uh when they should be operating the same data language uh you should start building a shared data culture and creating a shared and creating a clear data strategy um then uh The Next Step would be aligning on the same Matrix and um reduce confusion of course uh reducing uh once you implement that you'll reduce the amount of irritation and amount of within the team members um think accessible tools and uh that will be easy to understand even for non-technical users and even the most complicated tools uh even within the most complicated tools you need to find access uh you need to find a way to give access for non technical users because uh it will give them more more access to data and they will be Freer making their decisions um also data driven decisions uh of course are fostering uh and collaboration and

outcomes uh and we are all interested in our games and our products to be growing and of course speaking same data language improves those data driving decisions and as a special bonus here is our free ebook that de to Def have compiled on how to improve LTV in games it also uh shares some ways to calculate LTV and uh that's it from me um let's chat about analytics if you're around where booth G14 um let's make 2025 a year of growth and data driving Insight with no data silis and everybody speaking the same datadriven language and shared [Applause] culture thank you very much um now we've got a couple minutes left does anyone have any questions in the audience or feels like sharing some of their stories of Team massive miscommunications uh from working with different datas I hear some people chuckling anyone a story they're brave

enough to share well just in case we're over there at boo j4 and I didn't even speak about this yeah of course about about all of the case studies uh but yeah it it really is a problem and I like during during the uh these two days at the booth I the people are coming and they're like okay we don't understand what our marketing team is like looking into and product team have different metrics and uh we're we're always going back and forth with fighting and reten actually that retention example it's it's a real life example from our clients and of course like once you figuring out your data stack and once you're just trying to structure all those uh all those tools that you're having it's easier to to move forward with with the decisions 100% And as you're as you're scaling up your team as well like making sure this is fixed early on is probably going to be the best solution yeah it's of course if you have a good Common Ground if you have the good base it's easier to scale

forward and it's even easier to onboard new team members because okay you have this reference you have this notion page or we have this gbook entry and as a part of on boarding process it's easier to align the new members and the whole team and it actually does look nicer and more professional okay we're we're calculating this the same way and that's actually what def to Dev helps with uh we if you speaking about retention we have a bunch of ways to calculate it so you have a you have the whole team aligned on that definitely yeah aming no um yeah so be sure to go check out their Booth um they've got a lot of really insightful information um and as I'm sure everyone has experience those kind of where you talking about one thing and your marketing person is just on a different planet or vice versa um I'm usually the coming from the marketing side I'm very aware of the discrepancies that can happen so um definitely definitely be sure um to reach out can we get another uh Round of Applause please thank you thank you very much for