Effective Data Governance: A Collaborative Approach

In order to establish effective data governance, it is essential to work closely with business users and stakeholders from various areas of the organization. This was a key takeaway from a recent workshop where experts discussed best practices for data governance. By engaging with leaders from different departments, such as legal, compliance, risk management, and financial management, organizations can gain a more comprehensive understanding of their data needs.

One approach that was highlighted during the workshop is to visualize data metrics in a way that makes them accessible to decision-makers. This involves not only presenting data in a clear and concise manner but also adding layers of quality control, such as data quality metrics. By doing so, organizations can ensure that the data being used is accurate, reliable, and trustworthy.

Another important aspect of effective data governance is starting small and delivering value early on. This approach allows organizations to build momentum and create a sense of urgency around adopting new practices. Additionally, using business users as word-of-mouth advocates can help spread awareness about the importance of data governance within an organization.

Data governance should be approached with three core principles in mind: value, equity, and trust. By looking at data through a value lens, organizations can unlock its full potential by reusing and repurposing it. Equity is also crucial, as data should be used to benefit everyone, rather than just certain groups of people. Finally, building trust with stakeholders and maintaining the security of data is essential in today's digital landscape.

Stefan emphasized the importance of formulating purpose, questions, and governance frameworks for data use. By asking the right questions and understanding their limitations, organizations can create a culture of accountability around data use. This involves not only ensuring data quality but also being transparent about how data is processed and used.

Finally, Esther stressed the need to assess one's current state and maturity before embarking on a journey of data governance improvement. Understanding where an organization stands in terms of its data capabilities and limitations is essential for creating a roadmap for success.

Effective data governance requires a collaborative approach that involves stakeholders from various departments. By starting small, delivering value early on, and adopting a framework that prioritizes equity and trust, organizations can create a culture of accountability around their data assets. With the right mindset and approach, data governance can become a key driver of business success.

"WEBVTTKind: captionsLanguage: enhello everyone welcome to the final panel of today I am pretty excited about this I hope you all are too uh for everyone in the audience please do let us know where you're joining from let us know if there's anything that you want us to talk about in this next session I say oh and M saying no not the final panel sorry we there's there's a bonus sort of Afterparty session with Joe our CEO uh uh later on after this but this is going to be good uh I'm yeah I enjoy all our sessions but we got four speakers rather than three for you so it's G to be an extra exciting discussion so uh let's see who we got uh chat's scrolling very fast I have to pause this um okay uh we got Anna from Canada we've got uh mark from Philadelphia we've got aanish from India we've got Carolina from Toronto Noah from Florida uh oh it's going too fast too many people here H Andrea from bogatar we got Gustavo from Washington and we got Adam from Romania um all sorts of people there I'm very very excited by the global audience well done for everyone joining from around the world I know some of you in pretty weird time zones for this so very late for you all very early glad you all made it all right so with that I think it's time that we got started so over the last few years a lot of organizations have been getting really excited about generative AI they've been building things that think are going to solve all their problems and then they discover that actually the AI is generating garbage um and so when they dig deeper they discover the old truth that AI is only as good as the data that you feed into it and that their data quality control is non-existent so on a personal level as a data scientist I've had way too many experiences where I've been like I've had to present my results and then say well I know my analysis is technically technically correct but I really don't believe the results of my own work because the data set was pretty sketchy to begin with and this is a terrible experience both like the data scientist and the audience like no one wants to be like well this is analysis nonsense we just been wasting our time so uh today we're going to learn about how to improve data quality and more generally data governance across and organization and so for this uh last panel session we got four of the finest Minds in the data governance space so first up uh Stefan verol is the chief research and development officer and the director of the data program at NYU governance lab he's also a co-founder and principal Scientific Advisor at the data tank and a senior advisor at marle Foundation Esther money is the chief data officer at at sasin she was recognized as the CDO of the year in 2023 at the finex South Africa Awards and she's also on the list of uh global data power women in 2023 Amy Grace is the director of military engines digital strategy at pratton Whitney she spent much of the last few decades running teams working on analytics for predicting the health of aircraft engines fascinating stuff uh and this is uh something has got pretty terrible consequences if you get your data wrong so she's no stranger to worrying about data governance and rounding out our Fe and forsome is uh Mala veran uh a program manager and Senior data scientist at the World Bank she's part of the task team that launched the world bank's open data initiative and was instrumental in creating the world bank's First Data Council so uh four real experts here and uh with that let's learn about how to improve our data governance so um I think it's worth just defining what we're talking about here so uh can you explain what you mean by data quality and what are the business impacts of having better data quality so uh Esther do you want to go first on this sure um thank you Richie and I'm really happy to be here um so what is data quality data quality is really about having the right data that you need in the right format and ready for use for purpose at the right time um I use the analogy of data is like data quality is like baking a cake you know you need to have the right ingredients it needs to be in the right amounts it needs to be available and I mean if you're baking a cake and the baking powder or the sugar is missing or you don't have the right quantity then the cake will most likely not come out right so it's it's really about you know the having data that is accurate relevant complete and consistent and you know striving for better data quality means that Business Leaders are able to make better decisions because the reality is if you base a decision on faulty data you will most likely make the wrong calls which can cost the organization um and also having better data quality improves the client experience and by improving the client experience you can increase revenues um I work for and one of the things that we put a lot of effort in is improving the quality of our client data and in order to achieve one of our strategic objectives of client of being client Centric is we must first understand our clients and we want to understand their their behaviors but to do that we need to study and and and and explore and understand the data that we have around our clients and the one thing we've also realized is that you know um clients can get very frustrated when we have the wrong information um of them I'm sure most of us are have are banking um with with an organization or financial institution and I'm sure you get frustrated if they have the wrong information like the wrong name or they send you um a birthday message on the wrong date or they send you communication and you don't receive it because they have the wrong address so it's important always always have the right information when it comes to clients and I think that other thing is is is it's important to have good data quality um so that you don't miss opportunities um you know by perhaps not seeing the chances and opportunity to gain more customers and to improve your product offering um and that is really around being getting um you know having a competitive advantage and um so if if you don't have the right data and the data is not not correct you're not able to to explore those those opportunities I really like that analogy of it being like a cake and you mess up the uh the proportions of the ingredients and yeah it's going to be a disgusting mess rather than something edible um all right so I don't think I've ever heard anyone say wow I really love the quality of data of my company so um why does data quality never seemed to be a solved problem um Stefan do you want to go first on this yes thanks uh Richie and a pleasure to be on this panel this great panel here uh focusing on data governance and data quality and as relates to your question I think there are a variety of reasons why data quality is never really uh an objective that is always or ever uh fully met and and I think the first one is really about kind of the dynamic nature uh of data itself data is not a static kind of thing it evolves and especially I would say in the current environment where we have moved to new kinds of instrumentation of collecting data especially the data that has some kind of a realtime quality there is more opportunity also to really um have challenges with some of the data that might not be fully captured or might not be fully um qualitative as well and that also relates to then the dynamic context in which data is being collected which also means if the context changes of course the quality might change or the expectations and the requirements as uh Esther was saying if the cake changes then the expectations and the requirements for the cake and the ingredients uh might change as well and that is especially the case when you start reusing data that was collected for one purpose for another purpose and then you have different kinds of requirements that also means that the quality requirements might be different and as a result never been seen uh perfect the other reason uh is actually that indeed data is not static but also it's not a thing that uh is uh not a a result of a process and so data typically uh evolves during the data life cycle and uh at every point of the data life cycle there are opportunities to improve or to decline uh the quality of the data as well and I think that's why data governance really and data quality uh really needs to take an endtoend kind of approach when anyway from when you start creating or collecting data to ultimately when you start using uh the data uh and the insights that is generated from the data there is a quality uh component to every step of the data life cycle and so that also means that um given the fact that it's Dynamic given the fact that it's also uh the result of decisions made across uh the data life cycle means that we not just have to look at data quality from a policy perspective but really from a cultural perspective because I've been advocating on many occasions that right data quality is actually the result of a culture of data quality that exist within uh an organization or within a corporation for that matter and that really it has to be about a cultural shift towards making sure that data is qualitative it's not dirty or faulty for that matter and that's really what matters and then the other shift from my point of view is that we really need to start thinking about data stewardship on how we actually Ste data in a way that is aligned with the purpose uh and that is also then uh aligned with the requirements that are needed uh from a quality perspective so a longwinded answer to your question Richie but uh uh it's uh it's of course go a complex matter and uh data quality is the result of many decisions not just one at the point of collection okay uh I like the idea that um this uh cake that we're making might want to change over time you on different cakes on different occasions um but yeah uh it seems like you need that kind of broader idea of data governance and data stewardship if you want data qual um Amy do you want to add to this like do you have any ideas on like um how um how data governance is feeding into this into um like the the idea of uh data quality staying good over time or getting better over time yeah I I agree with everything Stefan says in addition I just think it a lot of us are data consumers and we don't always know where the data comes from or who the real producers are of the data that we pick up in different places and I think we also kind of have a tools first mentality we usually Express um our needs in the way of the data we want to see so a lot of lot of times we end up with uh people making local tools to aggregate data and look at it the way they want to but all of the aggregations and everything are really happening behind the scenes of what they're looking at and I think a lot of times just the visibility across the Enterprise of who has what data and what data is available has been a challenge so I I do think that some of the the Technologies are helping us to be more aware and and Concepts like cataloging um I I think are really important just to make people aware of the data behind the dashboards um I I also think that um we're learning uh to evolve our our requests of data to be more in the form of questions we want answered and you know maybe the generative AI culture is helping us to be a as we get experience it's a lot of what you get is how you frame the questions and uh I I think that's been helping us to get better at framing the questions we want to answer to support the decisions we need to make and the actions we need to take and if then you consider what data do I need to be able to make those decisions um I think we're all evolving in our awareness of the data beyond the dashboard culture okay yeah I I do like the idea that uh if you're just consuming data like you're looking at a dashboard you should have an understanding of what the data is underneath the the pretty visuals uh excellent uh so um it seems like uh maybe we need to have some um areas of innovation here so uh Mala can you talk me through like what of the main areas organizations need to innovate in terms of data governance thanks R and hello to everyone I really like all the flying hearts and smilees it's uh it's super nice and also the many many pictures of cakes uh it's quite distracting I have to say but but uh thanks for the question um Richie I mean I want to kind of take a step back a little bit uh to just sort of paint the picture of you know data governance happens at different levels uh you know at an organizational level it happens at the national level uh you know at the country at the highest level it happens at the international level because data now flows uh you know it's not that data is just used by only a few people or by a few communities or organizations data is now everyone uses dat everyone generates data everyone uses data actively or passively so the first thing I think we really have to change the way we're thinking about data governance um is that it it is it it must involve all stakeholders uh you know whether governments who are using data to improve services or policies private sector who are creating new Innovative uh you know products out of the data that they have uh or opening new markets uh or uh you know just individuals and civil societies who can really use the data more effectively to hold uh government's private sector account accountable um so you know with this sort of uh you know interventions that are going to happen at different levels across multiple stakeholders maybe I will focus on four or five areas where we think we really need to innovate in data governance uh the first I think is really Shifting the mindset of uh collecting generating data to really use and reuse of data you know I don't want to get into this debate on how much data that is being generated I mean I think we've lost count now Zab bytes and whatever new terms we are using but there is a lot of data granted there are gaps of course but there's also lots of data uh and the question to ask is whether we're using that data effectively are we enabling flows of that data across different stakeholders uh are we putting in standards to improve the interoperability of all of all of this information so really shifting that mind mindset towards uh use and reuse I think is really critical uh and then the second is about to stop I mean I don't know how many people from the technology team are here and and I'm not saying this in a sort of negative manner but really looking at data governance is is not as a technology initiative because the first thing when somebody says I'm thinking about data is is a tool that manifests in their mind uh I think now data governance goes beyond creating a technology product I want to give an example in Kenya where Kenya is doing by the way many great things but this is just uh based off of a study uh that they did and and this is kind of the situation in many countries uh you know where in Kenya particularly this study where they found in 58 hospitals they had um across different hospitals they found 58 different applications that was collecting data on different diseases on different uh different type of health services that was provided and none of them talked to each other so you want to put this scenario in your mind you know all of us go to the hospital right I I speak about health because I'm I'm currently working in the health sector so maybe many of my examples are going to be there but you go to a doctor you know they they take your vitals that's recorded somewhere you may have some kind of a you know accident you fall you go to Radiology you get a scan you know all of this information is getting recorded the question to ask is is that being used actively is that being used uh is is that information being uh connected and for for all of that to happen you can't think of this is a technology issue uh the data governance needs to sit outside of a technology initiative where really focusing on new rules of how all of this new data that's emerging can talk to each other um you know what kind of skills and Workforce need you know Stefon talked about people I think the people Dimension is really key here do you have people who are setting standards new rules of the game you have Regulators who are thinking about the broader implications of regulations of protecting information because some of the information we're talking about are really really uh personal data and uh important to protect so the point being there is yeah Thinking Beyond uh this is being an IT Tool uh and then of course creating a balance of uh you know reforms which is is enabling use but also really important to safeguard information really protecting really thinking about cyber security data protection some of the things that are you know quite boring and and people don't really often talk about talk about those things um and uh having this uh having having a really good leadership uh which is really creates that U uh culture of data use because often leadership teams fail to visualize the tangible benefits from data governance uh I think it's important to advocate for that and create that culture of data use and incentives for for people to use uh data more actively a lot to think about that I think the tricky part is like you say nothing's connected your colleagues need to talk to talking to people in other teams sounds very dangerous to me uh okay so uh there's a lot to do I think we need to get into like getting started but before that I want some motivation um so let's talk about some success stories I'd like to know if there are any examples of organization where they've made an effort to improve their data governance and then they've seen some real benefit from it um Esther do you want to talk us through some examples absolutely um so obviously I work for a bank and I mentioned that earlier and one of the things that that tends to happen to Banks is that we are under stringent regulatory requirements which demands that we meet certain regulations and legislations and and part of it is ensureing that you have proper governance over over your data but I think the the the thing that tends to happen is that is that data governance tends to be seen as this oversight function that's that's there to come with you know sort of like you know a stick to come and see that everybody is doing what they need to be doing instead of seeing it as something that's an enabler or a strategic driver for the business um so one of the things I can say that for us was a success story was shift that that that view and that notion that one that data is isn't is owned by it it's not owned by it it's owned by business um that that shift really created um the the the the idea of accountability responsibility and also the the by owning the data from a business perspective it means that they can leverage it I love us saying um I forget the person that said it but you know when it comes to data management and and adopting to data analytics it's it's there's there needs to be an element of change management and um the idea that you know for business that they own the data that's sitting in a system somewhere is is very difficult to Fathom and and to decipher but through the process of change management um and and back to the quotes that I wanted to say is somebody said change is a threat when done to you but it's an opportunity you when done by you um I forget the person that said that but it's about taking people through the Journey and let it let letting the business users understand why um you know um having governance over their data is important so that's that's a huge I think Plus for us the second thing is the the idea that not all data is equal you know there's this idea that you need to go and govern all the data and that's not necessarily true because some data um that you might have or data elements that you might have in your organization is actually not useful or fit for for purpose it's it's really it could just be unusable really um so it's about identifying what are those key data elements that you need to focus on what are your crown jewels and then focus on that so one of the things we've done with data quality is create that that road map around what data should we be overseeing what data should we be managing and what data should we be monitoring and maintaining from a data quality perspective the other thing around when we talk about not all data is equal is also true to data quality right if you take the example of the cake um you might have a scenario where you put in a little bit of sugar not enough but it's still edible right but if you do not put any baking powder or you do not put any flour into the cake it's unedible it's it's it's not useful so what we've done is we've also realized that there's a level of Tolerance around around data quality and that's what we've we've applied in our data quality framework where we've tried to understand based on the different data quality rules and data quality metrics what is the tolerance for the business because that way we when business is making decisions based on a certain tolerance levels they know that um when they make that decision it's based on a certain standard which they've defined um the other thing that um I think that has been very successful is realizing that the human element around around governance is is of often overlooked um we tend to to stick to the technology to the data itself and not really looking at the people aspect um so we we've really also started to shift that that that that frame the way we we we look at data governance but focusing on the people and that means ensuring that the data affluency or data literacy of key people is elevated in order to improve our data quality and to ensure that data governance is is embedded in a way that's useful for our business um lot to think about there I like the idea that um you need to decide which things uh are the most important which which data sets the most important and like what your tolerance for equality is um for those um because there's a lot to go on I'm trying to work out what's the first step uh Amy do you want us talk through like how you like when you right at the start how do you begin improving your data quality so I think some of the um most important first steps is to have a burning platform there has to be you you know a need for change people have to say this can't go on um because their experience with the data is just not working another thing in a in a company is that's invaluable is to have strong executive Championship to there's no no support to having a courageous leader at the top who will Empower people who want to change um I think another thing that's important is to have a data governance professional so somebody who can help teach us the ins and outs of data governance um I also think it's equally important to have case studies that'll help to teach the the people in the workforce especially the executives that are going to have to drive some of these things down through the organization case studies that'll teach them um why we should care about data governance and what the consequence is uh and how it's holding us back and then lastly we when we started our um data governance Council um I think somebody else mentioned the importance of change management we actually have a change management specialist working side by side with our data governance um lead and the most important part of this are um engaged committed forward-thinking business partners uh because like Esther says they own the data or they they are most intimately familiar with the data we're asking them to take on new roles and um to have those people come and be you know committed as opposed to just compliant um is is the key I think to to um really take off and and start our journey um I like that and you mentioned there should be um some sort of executive um leadership uh involved in this um maybe we should talk a bit more about like which teams and which roles uh need to be involved in any sort of data governance program uh Mala do you want to take this sure Richi so um I often um you know say that we want to think about it more in terms of the functions because each organization creates its own team or uh I guess the role Remains the Same but it's often difficult to create new teams depends on the fiscal constraints of the organization again at different levels at the national level or at an organizational level but importantly I think Amy uh touched upon some of those roles already uh in terms of and I think Esther also in the sense that having first the data governance having that sort of leadership from the top is important you kind of need both you don't want it to be a very compliance oriented sort of tone that you set for data governance so you have a leadership that really that really shows that this is beneficial for everyone and you're kind of recruiting everybody to this agenda so and you so you need that executive sort of committee that is uh sort of owning this uh so it's sustainable in the longer run then you have to have different um also dedicated roles for people who are going to be framing standards uh around uh for data governance you need business uh domain experts who understands the data and actually so it's not data for data's sake but really how at the end of the day how are you going to use that data to improve any any type of business in uh outcome uh you know it could be from the government side improving policies or reducing poverty or uh you know providing better service business from a private sector perspective it could be improving their own business outcomes um so even from an individual's point of view if you had access to your own health record you can take better decisions for example or on your health or on your financial outcomes um so it's it's about um really having those business domain experts as part of as part of the committee uh I mean I think Stefan uh already talked about this new sets of roles that are getting created in organizations called Data stewards whose role is really to look um at data and see how the data can be used in the organization how data can flow across different departments often you know you have siloed use of the data you know data from say a finance department is not really being used could be used for some other purpose that's you know would be the responsibility of the data Steward and and um another group of people is the legal team in an organization or Regulators or data protection officials at the at the national level who are deciding on these regulations and policies uh that is really standardized um I know we all love lawyers but as much as we love them I think it's really important to still engage them um uh and sort of really bring them along as well because they they have like Esther said sometimes you really have strong compliance requirements but I think somewhere you have to see that see the balance to see how you can bring them along to be able to use this data efficiently and then of course people who are very looking at measurement and uh very technical uh issues like anonymization of data that is still you know some of these areas are still being explored now that we're bringing in very many different types of information like geospatial cell phone records so having a team that's technically aware of how you bring some of these anonymization techniques or data integration techniques uh and continuously thinking about that in a systematic manner is is also important this is inter CU I was kind of expecting the answer okay we've got Executives we've got the kind of technical data people we've got business people but it's actually it goes beyond that because you need like legal people as well and then even like people outside your organization like uh governments creating regulations so it's a it's very much a a team effort there all right so I feel like a lot of the ideas around data governs are going to be the same for one organization to the next and you shouldn't be having to reinvent the whe from scratch are there any principles or Frameworks around data governance uh that you can leverage um Stefan do you want to take this yeah sure and and again I this is a a wonderful panel and and so and also by the way a wonderful chat mean it's a a great set of uh uh lessons even learned from just looking at the the chat and so I'm not sure how much I have to add here but one of the Frameworks that we have developed um in order to really kind of De mystifying um data governance is something what I call the five pce uh of data governance which really is about uh purposes principles uh processes um uh practices and positions and uh and I think we have discussed a few of them already because to a large extent from my point of view data governance is actually a set of practices positions and processes to meet a purpose that is aligned with a set of principles and I think if you think in terms of those kinds of 5ps then you basically have kind of all the ingredients for the cake that uh Esther has been baking here um um and it also means that we really have to a be crystal clear and it goes back to your question uh rich on where where would you start and I always anyway recommend organizations uh or uh anyone who wants to develop a data governance uh structure to really start with the purpose because that's really where uh uh it all uh comes down to because otherwise why do you need governance if you have no purpose that you seek to establish on meet and so a crystal clear purpose but then in order to achieve that purpose you will have to make decisions and so then it's going to be very important to have a set of principles that will align those uh decisions uh um in a way that meets the purpose that it's also principle based and so here of course I'm not going to go into the full-fledged uh kind of set of principles that you can apply and there are of course wellestablished principles such as the fair information practice principles which anyway were developed 30 years ago but are still anyway some of them are still pretty sound and uh and actually should be uh uh retained uh but you also have a set of new principles from my point of view that have entered the space one of them is actually equity and inclusion which I think uh needs to be uh uh more included in data governance meaning that you how do you make sure that the data benefits everyone uh to a large extent in a way that is also inclusive but also uh the principle that we have worked on which is kind of digital self-determination which is of course specifically uh more relevant for um personal data where at at the same time you not just rely on content but you also rely on a kind of additional areas of agency where individuals can also actually provide their preferences and expectations on how the data is being used to serve them and to serve society as a whole and so these are a set of principles uh that um can be used to then inform the processes that need to be in place to make decisions and I think malar was referring to all the kind of uh ingredient uh and the the positions uh that need to be in place but you also need to have decision processes Because by the end of the day you need to make decisions on how you actually go go go about uh the purpose that is aligned with the the principles and here I think it's super important to also make sure that those processes are seen as legitimate and at the same time effective and I think that's another kind of element of the framework as well so and some Richie uh I think there are kind of five PS that one may want to address the purposes the principles in order to make decisions via processes that ultimately then need to be implemented through practices and then dedicated positions that can oversee whether those practices align with the decisions and the principles as well oh man purposes principles something practice processes I think I got four I gave you a neonic to make it easy okay all right uh everyone has to watch the recording back and repeat that phrase over and over until they got all five PS um thank you very much we're out of uh time for my questions already this has gone by so fast um all right we've got some great questions from the audience though so uh let's uh dive over to those now the first question comes from aanish saying how do you balance the necessary data governance with agility and accessibility can we avoid creating processes that stifle Innovation make data difficult to use all right so um yeah how do you keep yourself Nimble and agile while having good data governance who wants to go first on this no takers this is a possible question to answer um I can maybe take a stab okay I I think it's a very very pertinent question I think that's the struggle for everyone to kind of balance what we say how do you balance enablers which is about enabling use while safeguarding and protecting information uh but also you're looking at it more from you know let's not make it too compliance oriented that it's just so hard to uh you know innovate based on that I think it's a process in the sense that um I think it's I I just want to go back to what Esther said uh you know maybe I I I'll just connect Esther's point with what we did at the World Bank as well uh you know we wanted to show I think you need to you can do that by illustrating value for example at the World Bank about five or six years ago before we really ramped up our data governance uh initiative a simple question would be what you know as staff if I join the organization uh you know what data I had access to you know I you know the way we would do that is someone would find data is they call someone and call someone you know there's a phone that you play you don't know where to go you don't know what data you had access to you didn't know how you can access it uh you don't know what are the terms under which you can you can actually access that data uh people were afraid to share information uh you know this this famous phrase by Hans rosling that everybody must have heard called a database hugging disorder I think that was a serious disease for us at the bank but I think we've I haven't sorry I haven't heard this phrase database hugging disorder hugging disorder yes uh so it's it's it's when you decide to hug your database and you don't want to release it or share it uh so so it's by it's Hans rosling it's not it's I didn't coin the name but but uh again going back to the the point is that I think you want to show value I mean there are some things that organizations do like the World Bank did as well as the first step is we we tried to understand what data we had you know and that manifests itself in different ways sometimes it's in the form of data catalog uh where you then understand what are your high value data sets and then you really focus on governing those and you're able to to show what kind of innovation and value that you you could bring um in some other cases I will give you another example where these are real deliberations right you might get frustrated about why you're not able to use certain data uh but uh you know it's just because of the nature of regulatory environment we are in or we just don't have standards so we just have to proactively start setting them up in the case of covid for example I think everybody saw that how we really struggled to use information even High income countries that had very strong Data Systems uh really struggled to use information from uh you know their health systems or you know from mobile phone operators because we didn't have a regulatory environment to access that information or we didn't have technical standards that talk to each other uh so you know by focusing on them I think and bringing in change management is something then you you know we hope to reach that balance between that enablers and while protecting and having stringent processes that protect information AB um so that Co example where like you've got a ton of data but actually making use of that data is very difficult that seems like uh a problem everywhere it's like okay yeah you're not actually having an impact unless you can make use of your data okay so uh next question comes from Lawrence so Lawrence asks how do you actually measure the quality of data how do you know if your organization's data is good or bad um so yeah what what's the scoring system here um I can take that sure um Lawrence I think what I can say is is the approach that we've taken is we we look at data quality based on three c one is um industry standards so for example when you have quality when you want to measure your data for example currency data obviously there's industry standards around how currencies should look like how they're named there's the iso um standard so we look we we try and get um industry standard type of um um rules into into the way we measure our data the second category is regulatory so I think also I saw in the chat somebody talked about GP gdpr there's poia in South Africa where where I am and it's basically taking those standards from a regulatory perspective and and applying that to our data quality rules then the third one which is the most important one is the business context and the business rules um is are is your data meeting the rules of your business uh processes um and then some of the ways we measure that is is is really um and the approach can be can vary we've adopted the dmbok Dharma dmbok approach we've taken the different um data quality dimensions and we've tried to create rules around those different um um dimensions and then the other thing that we we we had to do is make sure that we workshopped with our business users and we also engaged with different uh leaders from from different areas of the business like um Mala mentioned um legal um compliance risk teams to kind of give us also a different perspective and lens to the data because also what tends to happen is that when you look at it only from a business context and you ignore the other factors for example what is needed for financial um management and Reporting what's needed from a risk management perspective you miss out on some of those um pertinent rules that you should be measuring and obviously it's for us it's also visualizing those those metrics and making sure that it's accessible to people and one thing that really worked for us is even when we build reports we add on a layer of of having a data quality metric for that report so you're measuring the data that's been used in that report so that the decision maker knows the level of quality of the data that's in that report in order to make decisions so there's different approaches we've just adopted the dhma dambach approach oh man um I do like the idea of showing data quality in the dashboard so the person who's sort of down stream you can see that that's kind of terrifying though like uh if you get a low score they give you some funny questions ask like why is this dashboard existing if uh it's not very good quality um all right we are basically uh at time so before we finish 10 seconds each on how do you do data governance better so final advice uh Amy uh would you like to go first uh I would just say start small deliver value Drive awareness and I liked the thing I saw in chat use the um business as your word of mouth and bassadors and it'll spread I like that one a lot excellent yeah that's very cool uh Mala uh what's your final advice I mean I really want to Second what Amy said in terms of start small and then you really become AG Gile and improve I do want to add three principles that we talk about when we talk about data governance which is value getting value out of data look at it through a value lens of how you can unlock that value by reuse and use and the second is uh Equity um a lot of data that we use today is is really used only for very spe you know to the benefit of certain groups of people so in in how you deliver services using the data I think it's really important to have the equity Dimension that everyone benefits from the use of the data uh that there is um and the last is building Trust with all of the exposure to data that I'm sure you're all reading in the news about um you know data security issues data breaches I think it it's really important to safeguard data and which will build further build trust in in the data that we are producing along with being very transparent about how we are doing how we are processing data and how we are using data and around the data quality Dimensions good luck to everyone go like good luck you'll need it uh all right uh Stefan um what's your final advice yeah well meaning it's always hard to narrow it down but um uh I would just pick up on something that Amy actually mentioned earlier as well is that it's super important to formulate the purpose uh well uh which also includes formulating the questions well for which you then need data and then you also know to what extent does it need to be governed to what extent can it be made Equitable to what extent can it actually be done in a trusted way and so I would as some of you might know I've been advocating for actually question science to complement data science because we really need to do better in how we go about formulating questions because that's where it all starts all right wonderful yeah I get better uh asking and answering questions um Esther uh final final piece of advice from you um I think everybody said great points I think for me is also just start Where You Are um assess your current state and maturity I think it's important to know where you are what the gaps are what where your shortcomings are what strengths you have already that's in your organization it it it doesn't make sense starting a journey where you don't even know where you are in that Journey um so it's important to understand where you are and where what what your Readiness is what your organizational Readiness is and what risk tolerance you have in the organization oh yes understanding your data maturity very important all right we are well over time now and everyone needs to jump to the final session so I will have to wrap up quickly uh just thank you uh to all four of our speakers that was just magnificent stuff really really informative uh yeah thank you all thank you for having us all right and for everyone in the audience please do jump to the final session uh it's going to be a good one all right byehello everyone welcome to the final panel of today I am pretty excited about this I hope you all are too uh for everyone in the audience please do let us know where you're joining from let us know if there's anything that you want us to talk about in this next session I say oh and M saying no not the final panel sorry we there's there's a bonus sort of Afterparty session with Joe our CEO uh uh later on after this but this is going to be good uh I'm yeah I enjoy all our sessions but we got four speakers rather than three for you so it's G to be an extra exciting discussion so uh let's see who we got uh chat's scrolling very fast I have to pause this um okay uh we got Anna from Canada we've got uh mark from Philadelphia we've got aanish from India we've got Carolina from Toronto Noah from Florida uh oh it's going too fast too many people here H Andrea from bogatar we got Gustavo from Washington and we got Adam from Romania um all sorts of people there I'm very very excited by the global audience well done for everyone joining from around the world I know some of you in pretty weird time zones for this so very late for you all very early glad you all made it all right so with that I think it's time that we got started so over the last few years a lot of organizations have been getting really excited about generative AI they've been building things that think are going to solve all their problems and then they discover that actually the AI is generating garbage um and so when they dig deeper they discover the old truth that AI is only as good as the data that you feed into it and that their data quality control is non-existent so on a personal level as a data scientist I've had way too many experiences where I've been like I've had to present my results and then say well I know my analysis is technically technically correct but I really don't believe the results of my own work because the data set was pretty sketchy to begin with and this is a terrible experience both like the data scientist and the audience like no one wants to be like well this is analysis nonsense we just been wasting our time so uh today we're going to learn about how to improve data quality and more generally data governance across and organization and so for this uh last panel session we got four of the finest Minds in the data governance space so first up uh Stefan verol is the chief research and development officer and the director of the data program at NYU governance lab he's also a co-founder and principal Scientific Advisor at the data tank and a senior advisor at marle Foundation Esther money is the chief data officer at at sasin she was recognized as the CDO of the year in 2023 at the finex South Africa Awards and she's also on the list of uh global data power women in 2023 Amy Grace is the director of military engines digital strategy at pratton Whitney she spent much of the last few decades running teams working on analytics for predicting the health of aircraft engines fascinating stuff uh and this is uh something has got pretty terrible consequences if you get your data wrong so she's no stranger to worrying about data governance and rounding out our Fe and forsome is uh Mala veran uh a program manager and Senior data scientist at the World Bank she's part of the task team that launched the world bank's open data initiative and was instrumental in creating the world bank's First Data Council so uh four real experts here and uh with that let's learn about how to improve our data governance so um I think it's worth just defining what we're talking about here so uh can you explain what you mean by data quality and what are the business impacts of having better data quality so uh Esther do you want to go first on this sure um thank you Richie and I'm really happy to be here um so what is data quality data quality is really about having the right data that you need in the right format and ready for use for purpose at the right time um I use the analogy of data is like data quality is like baking a cake you know you need to have the right ingredients it needs to be in the right amounts it needs to be available and I mean if you're baking a cake and the baking powder or the sugar is missing or you don't have the right quantity then the cake will most likely not come out right so it's it's really about you know the having data that is accurate relevant complete and consistent and you know striving for better data quality means that Business Leaders are able to make better decisions because the reality is if you base a decision on faulty data you will most likely make the wrong calls which can cost the organization um and also having better data quality improves the client experience and by improving the client experience you can increase revenues um I work for and one of the things that we put a lot of effort in is improving the quality of our client data and in order to achieve one of our strategic objectives of client of being client Centric is we must first understand our clients and we want to understand their their behaviors but to do that we need to study and and and and explore and understand the data that we have around our clients and the one thing we've also realized is that you know um clients can get very frustrated when we have the wrong information um of them I'm sure most of us are have are banking um with with an organization or financial institution and I'm sure you get frustrated if they have the wrong information like the wrong name or they send you um a birthday message on the wrong date or they send you communication and you don't receive it because they have the wrong address so it's important always always have the right information when it comes to clients and I think that other thing is is is it's important to have good data quality um so that you don't miss opportunities um you know by perhaps not seeing the chances and opportunity to gain more customers and to improve your product offering um and that is really around being getting um you know having a competitive advantage and um so if if you don't have the right data and the data is not not correct you're not able to to explore those those opportunities I really like that analogy of it being like a cake and you mess up the uh the proportions of the ingredients and yeah it's going to be a disgusting mess rather than something edible um all right so I don't think I've ever heard anyone say wow I really love the quality of data of my company so um why does data quality never seemed to be a solved problem um Stefan do you want to go first on this yes thanks uh Richie and a pleasure to be on this panel this great panel here uh focusing on data governance and data quality and as relates to your question I think there are a variety of reasons why data quality is never really uh an objective that is always or ever uh fully met and and I think the first one is really about kind of the dynamic nature uh of data itself data is not a static kind of thing it evolves and especially I would say in the current environment where we have moved to new kinds of instrumentation of collecting data especially the data that has some kind of a realtime quality there is more opportunity also to really um have challenges with some of the data that might not be fully captured or might not be fully um qualitative as well and that also relates to then the dynamic context in which data is being collected which also means if the context changes of course the quality might change or the expectations and the requirements as uh Esther was saying if the cake changes then the expectations and the requirements for the cake and the ingredients uh might change as well and that is especially the case when you start reusing data that was collected for one purpose for another purpose and then you have different kinds of requirements that also means that the quality requirements might be different and as a result never been seen uh perfect the other reason uh is actually that indeed data is not static but also it's not a thing that uh is uh not a a result of a process and so data typically uh evolves during the data life cycle and uh at every point of the data life cycle there are opportunities to improve or to decline uh the quality of the data as well and I think that's why data governance really and data quality uh really needs to take an endtoend kind of approach when anyway from when you start creating or collecting data to ultimately when you start using uh the data uh and the insights that is generated from the data there is a quality uh component to every step of the data life cycle and so that also means that um given the fact that it's Dynamic given the fact that it's also uh the result of decisions made across uh the data life cycle means that we not just have to look at data quality from a policy perspective but really from a cultural perspective because I've been advocating on many occasions that right data quality is actually the result of a culture of data quality that exist within uh an organization or within a corporation for that matter and that really it has to be about a cultural shift towards making sure that data is qualitative it's not dirty or faulty for that matter and that's really what matters and then the other shift from my point of view is that we really need to start thinking about data stewardship on how we actually Ste data in a way that is aligned with the purpose uh and that is also then uh aligned with the requirements that are needed uh from a quality perspective so a longwinded answer to your question Richie but uh uh it's uh it's of course go a complex matter and uh data quality is the result of many decisions not just one at the point of collection okay uh I like the idea that um this uh cake that we're making might want to change over time you on different cakes on different occasions um but yeah uh it seems like you need that kind of broader idea of data governance and data stewardship if you want data qual um Amy do you want to add to this like do you have any ideas on like um how um how data governance is feeding into this into um like the the idea of uh data quality staying good over time or getting better over time yeah I I agree with everything Stefan says in addition I just think it a lot of us are data consumers and we don't always know where the data comes from or who the real producers are of the data that we pick up in different places and I think we also kind of have a tools first mentality we usually Express um our needs in the way of the data we want to see so a lot of lot of times we end up with uh people making local tools to aggregate data and look at it the way they want to but all of the aggregations and everything are really happening behind the scenes of what they're looking at and I think a lot of times just the visibility across the Enterprise of who has what data and what data is available has been a challenge so I I do think that some of the the Technologies are helping us to be more aware and and Concepts like cataloging um I I think are really important just to make people aware of the data behind the dashboards um I I also think that um we're learning uh to evolve our our requests of data to be more in the form of questions we want answered and you know maybe the generative AI culture is helping us to be a as we get experience it's a lot of what you get is how you frame the questions and uh I I think that's been helping us to get better at framing the questions we want to answer to support the decisions we need to make and the actions we need to take and if then you consider what data do I need to be able to make those decisions um I think we're all evolving in our awareness of the data beyond the dashboard culture okay yeah I I do like the idea that uh if you're just consuming data like you're looking at a dashboard you should have an understanding of what the data is underneath the the pretty visuals uh excellent uh so um it seems like uh maybe we need to have some um areas of innovation here so uh Mala can you talk me through like what of the main areas organizations need to innovate in terms of data governance thanks R and hello to everyone I really like all the flying hearts and smilees it's uh it's super nice and also the many many pictures of cakes uh it's quite distracting I have to say but but uh thanks for the question um Richie I mean I want to kind of take a step back a little bit uh to just sort of paint the picture of you know data governance happens at different levels uh you know at an organizational level it happens at the national level uh you know at the country at the highest level it happens at the international level because data now flows uh you know it's not that data is just used by only a few people or by a few communities or organizations data is now everyone uses dat everyone generates data everyone uses data actively or passively so the first thing I think we really have to change the way we're thinking about data governance um is that it it is it it must involve all stakeholders uh you know whether governments who are using data to improve services or policies private sector who are creating new Innovative uh you know products out of the data that they have uh or opening new markets uh or uh you know just individuals and civil societies who can really use the data more effectively to hold uh government's private sector account accountable um so you know with this sort of uh you know interventions that are going to happen at different levels across multiple stakeholders maybe I will focus on four or five areas where we think we really need to innovate in data governance uh the first I think is really Shifting the mindset of uh collecting generating data to really use and reuse of data you know I don't want to get into this debate on how much data that is being generated I mean I think we've lost count now Zab bytes and whatever new terms we are using but there is a lot of data granted there are gaps of course but there's also lots of data uh and the question to ask is whether we're using that data effectively are we enabling flows of that data across different stakeholders uh are we putting in standards to improve the interoperability of all of all of this information so really shifting that mind mindset towards uh use and reuse I think is really critical uh and then the second is about to stop I mean I don't know how many people from the technology team are here and and I'm not saying this in a sort of negative manner but really looking at data governance is is not as a technology initiative because the first thing when somebody says I'm thinking about data is is a tool that manifests in their mind uh I think now data governance goes beyond creating a technology product I want to give an example in Kenya where Kenya is doing by the way many great things but this is just uh based off of a study uh that they did and and this is kind of the situation in many countries uh you know where in Kenya particularly this study where they found in 58 hospitals they had um across different hospitals they found 58 different applications that was collecting data on different diseases on different uh different type of health services that was provided and none of them talked to each other so you want to put this scenario in your mind you know all of us go to the hospital right I I speak about health because I'm I'm currently working in the health sector so maybe many of my examples are going to be there but you go to a doctor you know they they take your vitals that's recorded somewhere you may have some kind of a you know accident you fall you go to Radiology you get a scan you know all of this information is getting recorded the question to ask is is that being used actively is that being used uh is is that information being uh connected and for for all of that to happen you can't think of this is a technology issue uh the data governance needs to sit outside of a technology initiative where really focusing on new rules of how all of this new data that's emerging can talk to each other um you know what kind of skills and Workforce need you know Stefon talked about people I think the people Dimension is really key here do you have people who are setting standards new rules of the game you have Regulators who are thinking about the broader implications of regulations of protecting information because some of the information we're talking about are really really uh personal data and uh important to protect so the point being there is yeah Thinking Beyond uh this is being an IT Tool uh and then of course creating a balance of uh you know reforms which is is enabling use but also really important to safeguard information really protecting really thinking about cyber security data protection some of the things that are you know quite boring and and people don't really often talk about talk about those things um and uh having this uh having having a really good leadership uh which is really creates that U uh culture of data use because often leadership teams fail to visualize the tangible benefits from data governance uh I think it's important to advocate for that and create that culture of data use and incentives for for people to use uh data more actively a lot to think about that I think the tricky part is like you say nothing's connected your colleagues need to talk to talking to people in other teams sounds very dangerous to me uh okay so uh there's a lot to do I think we need to get into like getting started but before that I want some motivation um so let's talk about some success stories I'd like to know if there are any examples of organization where they've made an effort to improve their data governance and then they've seen some real benefit from it um Esther do you want to talk us through some examples absolutely um so obviously I work for a bank and I mentioned that earlier and one of the things that that tends to happen to Banks is that we are under stringent regulatory requirements which demands that we meet certain regulations and legislations and and part of it is ensureing that you have proper governance over over your data but I think the the the thing that tends to happen is that is that data governance tends to be seen as this oversight function that's that's there to come with you know sort of like you know a stick to come and see that everybody is doing what they need to be doing instead of seeing it as something that's an enabler or a strategic driver for the business um so one of the things I can say that for us was a success story was shift that that that view and that notion that one that data is isn't is owned by it it's not owned by it it's owned by business um that that shift really created um the the the the idea of accountability responsibility and also the the by owning the data from a business perspective it means that they can leverage it I love us saying um I forget the person that said it but you know when it comes to data management and and adopting to data analytics it's it's there's there needs to be an element of change management and um the idea that you know for business that they own the data that's sitting in a system somewhere is is very difficult to Fathom and and to decipher but through the process of change management um and and back to the quotes that I wanted to say is somebody said change is a threat when done to you but it's an opportunity you when done by you um I forget the person that said that but it's about taking people through the Journey and let it let letting the business users understand why um you know um having governance over their data is important so that's that's a huge I think Plus for us the second thing is the the idea that not all data is equal you know there's this idea that you need to go and govern all the data and that's not necessarily true because some data um that you might have or data elements that you might have in your organization is actually not useful or fit for for purpose it's it's really it could just be unusable really um so it's about identifying what are those key data elements that you need to focus on what are your crown jewels and then focus on that so one of the things we've done with data quality is create that that road map around what data should we be overseeing what data should we be managing and what data should we be monitoring and maintaining from a data quality perspective the other thing around when we talk about not all data is equal is also true to data quality right if you take the example of the cake um you might have a scenario where you put in a little bit of sugar not enough but it's still edible right but if you do not put any baking powder or you do not put any flour into the cake it's unedible it's it's it's not useful so what we've done is we've also realized that there's a level of Tolerance around around data quality and that's what we've we've applied in our data quality framework where we've tried to understand based on the different data quality rules and data quality metrics what is the tolerance for the business because that way we when business is making decisions based on a certain tolerance levels they know that um when they make that decision it's based on a certain standard which they've defined um the other thing that um I think that has been very successful is realizing that the human element around around governance is is of often overlooked um we tend to to stick to the technology to the data itself and not really looking at the people aspect um so we we've really also started to shift that that that that frame the way we we we look at data governance but focusing on the people and that means ensuring that the data affluency or data literacy of key people is elevated in order to improve our data quality and to ensure that data governance is is embedded in a way that's useful for our business um lot to think about there I like the idea that um you need to decide which things uh are the most important which which data sets the most important and like what your tolerance for equality is um for those um because there's a lot to go on I'm trying to work out what's the first step uh Amy do you want us talk through like how you like when you right at the start how do you begin improving your data quality so I think some of the um most important first steps is to have a burning platform there has to be you you know a need for change people have to say this can't go on um because their experience with the data is just not working another thing in a in a company is that's invaluable is to have strong executive Championship to there's no no support to having a courageous leader at the top who will Empower people who want to change um I think another thing that's important is to have a data governance professional so somebody who can help teach us the ins and outs of data governance um I also think it's equally important to have case studies that'll help to teach the the people in the workforce especially the executives that are going to have to drive some of these things down through the organization case studies that'll teach them um why we should care about data governance and what the consequence is uh and how it's holding us back and then lastly we when we started our um data governance Council um I think somebody else mentioned the importance of change management we actually have a change management specialist working side by side with our data governance um lead and the most important part of this are um engaged committed forward-thinking business partners uh because like Esther says they own the data or they they are most intimately familiar with the data we're asking them to take on new roles and um to have those people come and be you know committed as opposed to just compliant um is is the key I think to to um really take off and and start our journey um I like that and you mentioned there should be um some sort of executive um leadership uh involved in this um maybe we should talk a bit more about like which teams and which roles uh need to be involved in any sort of data governance program uh Mala do you want to take this sure Richi so um I often um you know say that we want to think about it more in terms of the functions because each organization creates its own team or uh I guess the role Remains the Same but it's often difficult to create new teams depends on the fiscal constraints of the organization again at different levels at the national level or at an organizational level but importantly I think Amy uh touched upon some of those roles already uh in terms of and I think Esther also in the sense that having first the data governance having that sort of leadership from the top is important you kind of need both you don't want it to be a very compliance oriented sort of tone that you set for data governance so you have a leadership that really that really shows that this is beneficial for everyone and you're kind of recruiting everybody to this agenda so and you so you need that executive sort of committee that is uh sort of owning this uh so it's sustainable in the longer run then you have to have different um also dedicated roles for people who are going to be framing standards uh around uh for data governance you need business uh domain experts who understands the data and actually so it's not data for data's sake but really how at the end of the day how are you going to use that data to improve any any type of business in uh outcome uh you know it could be from the government side improving policies or reducing poverty or uh you know providing better service business from a private sector perspective it could be improving their own business outcomes um so even from an individual's point of view if you had access to your own health record you can take better decisions for example or on your health or on your financial outcomes um so it's it's about um really having those business domain experts as part of as part of the committee uh I mean I think Stefan uh already talked about this new sets of roles that are getting created in organizations called Data stewards whose role is really to look um at data and see how the data can be used in the organization how data can flow across different departments often you know you have siloed use of the data you know data from say a finance department is not really being used could be used for some other purpose that's you know would be the responsibility of the data Steward and and um another group of people is the legal team in an organization or Regulators or data protection officials at the at the national level who are deciding on these regulations and policies uh that is really standardized um I know we all love lawyers but as much as we love them I think it's really important to still engage them um uh and sort of really bring them along as well because they they have like Esther said sometimes you really have strong compliance requirements but I think somewhere you have to see that see the balance to see how you can bring them along to be able to use this data efficiently and then of course people who are very looking at measurement and uh very technical uh issues like anonymization of data that is still you know some of these areas are still being explored now that we're bringing in very many different types of information like geospatial cell phone records so having a team that's technically aware of how you bring some of these anonymization techniques or data integration techniques uh and continuously thinking about that in a systematic manner is is also important this is inter CU I was kind of expecting the answer okay we've got Executives we've got the kind of technical data people we've got business people but it's actually it goes beyond that because you need like legal people as well and then even like people outside your organization like uh governments creating regulations so it's a it's very much a a team effort there all right so I feel like a lot of the ideas around data governs are going to be the same for one organization to the next and you shouldn't be having to reinvent the whe from scratch are there any principles or Frameworks around data governance uh that you can leverage um Stefan do you want to take this yeah sure and and again I this is a a wonderful panel and and so and also by the way a wonderful chat mean it's a a great set of uh uh lessons even learned from just looking at the the chat and so I'm not sure how much I have to add here but one of the Frameworks that we have developed um in order to really kind of De mystifying um data governance is something what I call the five pce uh of data governance which really is about uh purposes principles uh processes um uh practices and positions and uh and I think we have discussed a few of them already because to a large extent from my point of view data governance is actually a set of practices positions and processes to meet a purpose that is aligned with a set of principles and I think if you think in terms of those kinds of 5ps then you basically have kind of all the ingredients for the cake that uh Esther has been baking here um um and it also means that we really have to a be crystal clear and it goes back to your question uh rich on where where would you start and I always anyway recommend organizations uh or uh anyone who wants to develop a data governance uh structure to really start with the purpose because that's really where uh uh it all uh comes down to because otherwise why do you need governance if you have no purpose that you seek to establish on meet and so a crystal clear purpose but then in order to achieve that purpose you will have to make decisions and so then it's going to be very important to have a set of principles that will align those uh decisions uh um in a way that meets the purpose that it's also principle based and so here of course I'm not going to go into the full-fledged uh kind of set of principles that you can apply and there are of course wellestablished principles such as the fair information practice principles which anyway were developed 30 years ago but are still anyway some of them are still pretty sound and uh and actually should be uh uh retained uh but you also have a set of new principles from my point of view that have entered the space one of them is actually equity and inclusion which I think uh needs to be uh uh more included in data governance meaning that you how do you make sure that the data benefits everyone uh to a large extent in a way that is also inclusive but also uh the principle that we have worked on which is kind of digital self-determination which is of course specifically uh more relevant for um personal data where at at the same time you not just rely on content but you also rely on a kind of additional areas of agency where individuals can also actually provide their preferences and expectations on how the data is being used to serve them and to serve society as a whole and so these are a set of principles uh that um can be used to then inform the processes that need to be in place to make decisions and I think malar was referring to all the kind of uh ingredient uh and the the positions uh that need to be in place but you also need to have decision processes Because by the end of the day you need to make decisions on how you actually go go go about uh the purpose that is aligned with the the principles and here I think it's super important to also make sure that those processes are seen as legitimate and at the same time effective and I think that's another kind of element of the framework as well so and some Richie uh I think there are kind of five PS that one may want to address the purposes the principles in order to make decisions via processes that ultimately then need to be implemented through practices and then dedicated positions that can oversee whether those practices align with the decisions and the principles as well oh man purposes principles something practice processes I think I got four I gave you a neonic to make it easy okay all right uh everyone has to watch the recording back and repeat that phrase over and over until they got all five PS um thank you very much we're out of uh time for my questions already this has gone by so fast um all right we've got some great questions from the audience though so uh let's uh dive over to those now the first question comes from aanish saying how do you balance the necessary data governance with agility and accessibility can we avoid creating processes that stifle Innovation make data difficult to use all right so um yeah how do you keep yourself Nimble and agile while having good data governance who wants to go first on this no takers this is a possible question to answer um I can maybe take a stab okay I I think it's a very very pertinent question I think that's the struggle for everyone to kind of balance what we say how do you balance enablers which is about enabling use while safeguarding and protecting information uh but also you're looking at it more from you know let's not make it too compliance oriented that it's just so hard to uh you know innovate based on that I think it's a process in the sense that um I think it's I I just want to go back to what Esther said uh you know maybe I I I'll just connect Esther's point with what we did at the World Bank as well uh you know we wanted to show I think you need to you can do that by illustrating value for example at the World Bank about five or six years ago before we really ramped up our data governance uh initiative a simple question would be what you know as staff if I join the organization uh you know what data I had access to you know I you know the way we would do that is someone would find data is they call someone and call someone you know there's a phone that you play you don't know where to go you don't know what data you had access to you didn't know how you can access it uh you don't know what are the terms under which you can you can actually access that data uh people were afraid to share information uh you know this this famous phrase by Hans rosling that everybody must have heard called a database hugging disorder I think that was a serious disease for us at the bank but I think we've I haven't sorry I haven't heard this phrase database hugging disorder hugging disorder yes uh so it's it's it's when you decide to hug your database and you don't want to release it or share it uh so so it's by it's Hans rosling it's not it's I didn't coin the name but but uh again going back to the the point is that I think you want to show value I mean there are some things that organizations do like the World Bank did as well as the first step is we we tried to understand what data we had you know and that manifests itself in different ways sometimes it's in the form of data catalog uh where you then understand what are your high value data sets and then you really focus on governing those and you're able to to show what kind of innovation and value that you you could bring um in some other cases I will give you another example where these are real deliberations right you might get frustrated about why you're not able to use certain data uh but uh you know it's just because of the nature of regulatory environment we are in or we just don't have standards so we just have to proactively start setting them up in the case of covid for example I think everybody saw that how we really struggled to use information even High income countries that had very strong Data Systems uh really struggled to use information from uh you know their health systems or you know from mobile phone operators because we didn't have a regulatory environment to access that information or we didn't have technical standards that talk to each other uh so you know by focusing on them I think and bringing in change management is something then you you know we hope to reach that balance between that enablers and while protecting and having stringent processes that protect information AB um so that Co example where like you've got a ton of data but actually making use of that data is very difficult that seems like uh a problem everywhere it's like okay yeah you're not actually having an impact unless you can make use of your data okay so uh next question comes from Lawrence so Lawrence asks how do you actually measure the quality of data how do you know if your organization's data is good or bad um so yeah what what's the scoring system here um I can take that sure um Lawrence I think what I can say is is the approach that we've taken is we we look at data quality based on three c one is um industry standards so for example when you have quality when you want to measure your data for example currency data obviously there's industry standards around how currencies should look like how they're named there's the iso um standard so we look we we try and get um industry standard type of um um rules into into the way we measure our data the second category is regulatory so I think also I saw in the chat somebody talked about GP gdpr there's poia in South Africa where where I am and it's basically taking those standards from a regulatory perspective and and applying that to our data quality rules then the third one which is the most important one is the business context and the business rules um is are is your data meeting the rules of your business uh processes um and then some of the ways we measure that is is is really um and the approach can be can vary we've adopted the dmbok Dharma dmbok approach we've taken the different um data quality dimensions and we've tried to create rules around those different um um dimensions and then the other thing that we we we had to do is make sure that we workshopped with our business users and we also engaged with different uh leaders from from different areas of the business like um Mala mentioned um legal um compliance risk teams to kind of give us also a different perspective and lens to the data because also what tends to happen is that when you look at it only from a business context and you ignore the other factors for example what is needed for financial um management and Reporting what's needed from a risk management perspective you miss out on some of those um pertinent rules that you should be measuring and obviously it's for us it's also visualizing those those metrics and making sure that it's accessible to people and one thing that really worked for us is even when we build reports we add on a layer of of having a data quality metric for that report so you're measuring the data that's been used in that report so that the decision maker knows the level of quality of the data that's in that report in order to make decisions so there's different approaches we've just adopted the dhma dambach approach oh man um I do like the idea of showing data quality in the dashboard so the person who's sort of down stream you can see that that's kind of terrifying though like uh if you get a low score they give you some funny questions ask like why is this dashboard existing if uh it's not very good quality um all right we are basically uh at time so before we finish 10 seconds each on how do you do data governance better so final advice uh Amy uh would you like to go first uh I would just say start small deliver value Drive awareness and I liked the thing I saw in chat use the um business as your word of mouth and bassadors and it'll spread I like that one a lot excellent yeah that's very cool uh Mala uh what's your final advice I mean I really want to Second what Amy said in terms of start small and then you really become AG Gile and improve I do want to add three principles that we talk about when we talk about data governance which is value getting value out of data look at it through a value lens of how you can unlock that value by reuse and use and the second is uh Equity um a lot of data that we use today is is really used only for very spe you know to the benefit of certain groups of people so in in how you deliver services using the data I think it's really important to have the equity Dimension that everyone benefits from the use of the data uh that there is um and the last is building Trust with all of the exposure to data that I'm sure you're all reading in the news about um you know data security issues data breaches I think it it's really important to safeguard data and which will build further build trust in in the data that we are producing along with being very transparent about how we are doing how we are processing data and how we are using data and around the data quality Dimensions good luck to everyone go like good luck you'll need it uh all right uh Stefan um what's your final advice yeah well meaning it's always hard to narrow it down but um uh I would just pick up on something that Amy actually mentioned earlier as well is that it's super important to formulate the purpose uh well uh which also includes formulating the questions well for which you then need data and then you also know to what extent does it need to be governed to what extent can it be made Equitable to what extent can it actually be done in a trusted way and so I would as some of you might know I've been advocating for actually question science to complement data science because we really need to do better in how we go about formulating questions because that's where it all starts all right wonderful yeah I get better uh asking and answering questions um Esther uh final final piece of advice from you um I think everybody said great points I think for me is also just start Where You Are um assess your current state and maturity I think it's important to know where you are what the gaps are what where your shortcomings are what strengths you have already that's in your organization it it it doesn't make sense starting a journey where you don't even know where you are in that Journey um so it's important to understand where you are and where what what your Readiness is what your organizational Readiness is and what risk tolerance you have in the organization oh yes understanding your data maturity very important all right we are well over time now and everyone needs to jump to the final session so I will have to wrap up quickly uh just thank you uh to all four of our speakers that was just magnificent stuff really really informative uh yeah thank you all thank you for having us all right and for everyone in the audience please do jump to the final session uh it's going to be a good one all right bye\n"