**Implementing Data Galaxy at a Major French Insurance Company**

In order to identify ownership and understanding of data transformation, data indicators, and algorithms, a major French insurance company implemented data Galaxy. The company was facing challenges with data ownership and traceability, and it was clear that they needed a solution to address these issues. The implementation of data Galaxy provided the organization with a unique functional and technical desk, which became the central point of information and a common language for their different use cases.

The definition of an indicator was standardized across the entire organization, ensuring clear identification of ownership and stewardship. Furthermore, the system implemented end-to-end data lineage, tracing the origin and transformation of data across all systems. This allowed for better understanding of where data came from, how it was transformed, and who owned it. The platform also helped subject matter experts share their knowledge and document information, valuing their expertise.

**A Solution for the Insurance Sector**

The implementation of data Galaxy at the French insurance company provided a solution to their challenges with data ownership and traceability. This approach is particularly relevant to the insurance sector, where accurate data is crucial for informed decision-making. The use of data Galaxy enabled the organization to build a common language around data assets, improve collaboration, and make data-driven decisions.

**Key Benefits of Data Governance**

The implementation of data governance at the French insurance company demonstrated several key benefits, including improved control over data, understanding of data transformation, and identification of ownership and stewardship. The use of data Galaxy also enabled better traceability of all systems and transformations applied to the data. Furthermore, the platform helped in gathering information, sharing knowledge among subject matter experts, and documenting data.

**Trends in Data Governance**

In 2023, several trends are expected to shape the field of data governance. One of the key trends is data democracy, which refers to how data governance, management, and cataloging will improve decision-making. To achieve a data-driven mindset, organizations need to ensure that their data is accurate and reliable, and that decisions based on data are not foolproof.

Increased collaboration and sharing of data among departments and business lines is also expected to become more prevalent in 2023. This trend highlights the importance of building an enterprise-wide data strategy and adopting a common language for data assets. Furthermore, transparency will play a critical role in data governance, with organizations striving to provide access to relevant data for customers and employees.

**Reducing Data Silos**

Another trend expected to shape the field of data governance in 2023 is reducing data silos. Breaking down barriers across departments and adopting an overall company-wide data strategy will become more common. This approach will enable organizations to build a comprehensive understanding of their data assets and make informed decisions.

**Final Words**

The market can benefit from starting small when implementing data governance and data catalog initiatives. Even if it's in Excel, taking the first step is essential for building a foundation for future data management efforts. Communicating with stakeholders and having a clear data vision and strategy are also crucial for success. By embracing data governance and harnessing its power, organizations can build competitive advantage and drive business success.

**Conclusion**

The implementation of data Galaxy at a major French insurance company demonstrated the importance of data governance in achieving accurate and reliable insights. The trends expected to shape the field of data governance in 2023 highlight the need for increased transparency, collaboration, and reduced data silos. By embracing these trends and adopting best practices, organizations can build a comprehensive understanding of their data assets and make informed decisions that drive business success.

**About the Author**

Lauren is a data management expert who has shared her knowledge on data governance, data cataloging, and other related topics through various platforms. She is passionate about helping organizations build strong data management strategies and leveraging data to drive business success.

"WEBVTTKind: captionsLanguage: enyou're listening to data framed a podcast by datacamp in this show you'll hear all the latest trends and insights in data science whether you're just getting started in your data career or you're a data leader looking to scale data-driven decisions in your organization join us for in-depth discussions with data and analytics leaders at the Forefront of the data Revolution Let's Dive Right In hello everyone this is Adele data science educator and evangelist at data Camp arguably one of the biggest obstacles for organizations in building a data culture today is trust and data and Trust ultimately boils down to data quality and an organization's ability to govern its data well this is where data governance comes in and this is why it's so crucial so how do you make gains on data governance how do you bridge the gap between functional teams and Technical teams to create a common data governance Vision these are all questions that are going to be answered by today's guest laurentress Lauren is a data governance evangelist and director of Professional Services at data Galaxy he is a seasoned professional and expert with a proven track record of successful implementation of data governance related projects across the world Lauren is skilled in data management data governance data quality Master data management and a lot more he has a strong Consulting background with prince2 practitioner certification and has been focused on pragmatic project management around data management throughout his career he acted as the perfect middleman between I.T and business regarding federating efforts in developing a unified view of an organization's data landscape throughout the episode we spoke about the state of data governance today how data leaders and organizations can start their data governance Journey how to evangelize the importance of data governance and gain buy-in from everyone within the organization the state of data governance tooling today and a lot more if you enjoyed this episode make sure to let us know and now on to today's episode Laurence wait to have you on the show hello there thank you very much for the invitation and the opportunity to share around data topics I'm very excited to discuss with you all things data governance and quality how a culture of data trusts promotes higher use of data how data leaders can succeed at data governance and more but before can you please give us a bit of a background about yourself sure I started in the data governance and data World about 20 years ago prior to data was a project manager and doing the liaison between I.T and business which helped me a lot in my data career then what I did is in fact I was working for a company where they implemented Master data management it was my first experience with data where I presented my business unit and then at the end took over the overall program management then I worked for a major Consulting companies and implemented data governance Frameworks organizations and tools internationally so I traveled a lot around the world which is also nice because you're confronted to different cultures and opens your mind and then the three year back I joined data Galaxy to set up Professional Services and help our customers with the implementation and the return on investment on the data platform recently I moved to the Evangelist position so I'm spreading the good words around data governance I'm also participating in podcasts like yours but also in exhibition and events in Keynotes bottom line making sure that our product fits the market needs that we also voice the market in terms of features and expectations and also making sure that data Galaxy is a known brand across emea and now also across the US on the personal side I live in Belgium I'm passionate about food it's probably also the reason why I called my daily LinkedIn posts to the data governance kitchen so do not hesitate to subscribe to my profile and you will get daily hints on data governance data management data catalog that's really great we definitely have a lot of comment I also live in Belgium and I also am passionate about food but I'm really excited to chat with you about all things data governance you know I'd love to set the stage for today's chat by trying to understand where the data governance and quality space are at today I think we can all agree that the past few years have been you know marked by the March for organizations to become data driven organizations have been making massive investments in data infrastructure Talent data collection data culture and more naturally the data quality space has evolved quite a lot to accommodate the growing needs of organizations so maybe starting off I'd love to First understand how have you seen the importance of data governance evolve over the past decade and what makes it such an important area of investment for data leaders today that's a great question and then it's the Genesis of data culture in many organizations what I've seen is that historically anything related to data was managed by the IT department in a very siled way it was a kind of Kingdom approach around the data and the same was applicable to business departments very little information was documented they had no common language and they were working in a very closed way and not sharing information across a line of businesses now things have changed the mindset have progressed or evolved data is recognized as an important asset in many organizations so it's let's say three to five years back we saw the rise of data self-service in the API world so we were giving the power back to the business they were provided with data sets and able to render personal queries and reports but to be able to understand your data set you also need to have discovered language definition of kpis definition of business terms so the rise of data catalog really started there and then it really helped building this common language I mean we are both European based if you put a German a friendship Belgian a Dutch a Danish and Italian and a Spanish guy around the table they will have no common language it's very difficult to understand each other same goes for the data and the data catalog brought this kind of Esperanto type of language around the table and people were able to communicate and understand and look at the same thing with the same definition so data governance helped a lot it creates created the need for data catalogs and it was the rise of some of our competitors back in these days I mean Colibri started probably 15 years back and really identified these issues and data Galaxy we really had the same we were facing many feedback from our customers that they were lacking business and Global language and then last but not least is also the understanding from many organizations that data is a variable asset it's not just information on the server but it can really make a huge difference in the competitive landscape I would say or it's a competitive advantage and the ones leveraging this information to the max are quieter leaders on the market if we simply take examples such as Amazon or Uber I mean these guys they know you in and out when you book a ride and they come with the right offer at the right moment and this is because they are leveraging the available data that's really great and there's definitely a lot to unpack here you know one thing I want to kind of Deep dive in is how is data governance evolving given the rise and complexity of the different data Landscapes organizations deal with right now you know we mentioned that organizations are making more and more investments in data there is a bigger recognition that data is recognized as an asset data is given back to the people it's given back to the business and this in turn creates an increasingly complex data value chain so given how complex the data value chain is becoming across an organization today how have the challenges with data quality and governance evolved what do you think are the top obstacles in terms of data governance for organizations today that's right many companies are evolving in terms of technical stock big organization they have this Legacy of I would say old-fashioned systems it's not old-fashioned but the old ways of doing things with relational on-prem databases like you know record NES 400 but also they start understanding the importance of modern data stack in terms of data lakes and modern Technologies so they really have this struggle of where do we go do we keep the Legacy do we move to the full access for instance and full cloud or do we have this hybrid approach and what we see is many companies for now are at this stage of having this hybrid approach because they will never the commission Old Stock which is a total different story for startups because they are Cloud native they started with Cloud tools so it's a bit easier in terms of data landscape however they all face the same challenges and I would say if we need to outline some of them is traceability is where the data is coming from what are the system it goes through and what are in fact as well the transformation applies and the more complex is your it landscape or data landscape the more complex it is to understand the Journey of your data and the transformation because it goes through so many systems with replication with aggregation with transformation that people are totally lost they don't know which transformation and when they have a data quality issue at the end they don't know where it happened so it's very important to have this traceability and transformation understanding and now also another type of challenge is indeed identifying the real source of error with let's say your transactional system your name Adder is on an invoice but where is the issue with your let's say address is it coming from the initial transaction is it coming from accounting is it coming from marketing so it's really important to be able to really find the source of error it means that if you want to find this source of error and take some remediation action you need to be able to contact the right person or the right people which is a big challenge around data stewardship what I hear from many organizations when we start discussing about data catalog is of course we know where the ever happened but we never know who to contact is It Adele at accounting is it local at the marketing or is it someone else in another department that's where data cataloging is playing a very important role and the last part is something which is not always understood by organization is your data context because data quality issues can happen of course but depending on the content it has more or less impact I mean for instance a birth date of someone when it comes to generating the invoice it has very few importance I mean it's not important that your birth date is correct when you receive the invoice however if you're for marketing and initiate a reward plan for your birthday then it's very important because you need to receive this offer right on time on your birthday so to summarize I would say traceability transformation source of error data stewardship and ownership and data context are are very very big challenges for organizations today that's really great and let's dive deep maybe into the stewardship component and the overall ownership of the data governance program this increasingly complex value chain also puts stress on the ownership when it comes to data quality and data governance data has produced Upstream by various functional teams collected from various sources then transformed Downstream and is used by a variety of stakeholders within the organization who do you think is the owner of a data governance program or the data governance agenda in general and who should be the main stakeholders when it comes to the data store trip program well this is one million dollar question who is this is this is quite a difficult topic and it's a complex question I think there are many many stakeholders in the data Journey which make things complex either the only thing the responsibility resides with someone else or they all think they are the owner of the data and the others can't touch it so we really need to set the boundaries there what I would say and I tend to tell my customer is first of all there can only be one data owner in the entire value chain I mean it's like cooking if you have 10 chefs in the kitchen things will run very very bad so you only have one Chef in the kitchen you only have one data owner in your data value chain it means that the exercise is quite complex to identify this ultimate owner one approach is to say Department the line of business who created this data the first time is the owner because he or she has the ability to modify the data if there is a data quality issue so that's one approach what I also think is important is to in fact draw your data value chain because you have all the steps all the systems and it will definitely help your data governance organization to find out the ultimate ownership again it's a value chain so value chain mean you have many links and each link needs to understand that he or she is receiving a data item altering the data item doing something with this data and passing it on to another person so it's everyone's responsibility to make sure that when you pass the data to someone else the quality of that data is very good and then I would say that you need once you've drawn this value chain you need to define the chain of commands so knowing exactly if you have a data issue if you need to update a data item who are the person in charge and who has the ultimate power to alter this definition and this is where data governance and data catalogs are playing big role because the data catalog is the centralized information around your data items in terms of ownership stewardship traceability definition and so and if we want to kind of dive deeper there you know in an organizational Enterprise organization that's still identifying it's data governance strategy who are the type of profiles that you think are suitable owners of the data governance agenda and what are the type of stakeholders that support them there I would say based on the implementation I made it you have various profiles but where companies are successful is where you have a CDO because the CTO is really the person at senior leadership level with the authority and the mandate to make sure that data is valued and data governance is implemented and this makes a big difference because otherwise if it's just you and I for instance in a day-to-day life your marketing and accounting trying to do things around data governance it will be very complex because you don't have the resource you don't have the budget you don't have the Mandate so you really need to have sponsorship at a leadership level with this video quite often you also see that you have head of data governance for instance who are playing a big role or you have a CD office with a bunch of people managing the data governance but it's really a separate function in the organization that's really great and I think this really marks a great segue into what actually makes the data governance strategy and approach successful as data becomes even more critical for decision making and product development data governance can have massive implications for the organization as we've discussed so far so you've had quite a lot of experience in implementing successful data governance programs can you outline what you think are key components of a successful data governance strategy yes that's as well an important aspect because the word strategy is very important I've implemented data Galaxy with around 80 customers in the past three years so what I've seen with successful customer are having data vision and strategy at executive board level that's very important because these guys will have to Define what they want to do with data can be monetization it can be data driven it can be whatever they want but at least they have the vision on what they want to do and this is the very start and then you have the CDO role which is in fact empowered and in charge of making this strategy life and also having this frequent communication between the rest of the data organization and the management board so here's the guide of person relaying this information and then they also have to Define their why is why do we want to be data driven why do we want to Value data because at the end there should be a business outcome and then once this part is defined and clarified they pass it on to the business and I really say to the business because data governance and data cataloging and data valuation in general is the responsibility of the business it's the end of the it Kingdom doing everything around Data Business takes the lead and then it comes there to support in fact to access the data to trace the data but it's really a business matter and then once you have this strategy business understands the importance now it's also to identify what are the daily pains challenges with the data once you have these challenges you will know how your data governance initiative and data cataloging will relieve the pain and help the business getting better I also strongly suggest to have a data governance by Design so don't try to make it a big bang in the organization by building complex structure stay simple be a child be iterative so try with one line of business with one use case and then two use cases and gradually expanding the organization it will be way more efficient than trying to have this big Bank and then another success factor is how you identify the indicators around your data governance initiative not that you necessarily have to identify the return on investment in terms of dollars but the indicators which will make sure that you're on the right track and remember we identified paints if you identify pains you identify the benefits and you monitor these benefits and once you have these benefits reported you need to communicate across the organization it's a key component of your data governance initiative the way you will communicate every time I mean every touch point in the organization turn all's quarterly business reviews see your speech of the week or newsletter whatever data should be embedded in there because this is the way management shows the importance of data in the organization and the last part is really the human part data governance initiative is based on human it's a change management courses and is the same for many other projects you can buy any tool but the tool will only do what you tell him to do and if you don't focus on human that you understand that people are reluctant to change they need to understand the value they need to be informed they need to communicate once you have this under control then you have more chances to be successful in your data governance initiative that's really great and there's a few things I want to really focus on here so you mentioned you know data governance having macro benefits that are made clear there is a why that is attached to it that is communicated to the entire organization CDO needs to act as an evangelist for example and the organization outside when discussing the data governance program and finally it's a change management program right it's also a cultural program it's a way of work that needs to change and I think one thing that we've seen across you know data framed episodes regardless of analytics Investments whether it's the illiteracy or data culture investment a new machine learning project the importance of evangelism and buy-in right by the executive team and also by The Wider organization is extremely important for these types of Investments I think one thing standing in the way of investments in data governance and data quality is that it's not necessarily a shiny use case you can show in a quarterly earnings report if you're a leader or something you can add to your CV like a machine learning project or you know a dashboard if you're a data practitioner how do you convince stakeholders within the organization to invest in data governance especially if they don't yet view it as a strategically important project Adele you're asking tricky questions in fact it's complex and I don't have the magic source to make things happen from one day to another but if senior leadership does not understand the importance of data and the value of data and data governance my first reaction would be to tell them do nothing until you hit the wall and understand the importance of data governance that's a total waste of time trying convincing someone who's totally reluctant to the information of course it's a bit on the job site that I'm telling the sentence but if they don't understand I mean it's a long effort and most likely you need to have this external view or external people or external I would say for instance a consultancy firm coming and explaining why it is important how it works within other companies to show the importance what I've seen is that most of the leaders and stakeholders they have a vague feeling that the data is important but in fact they don't know where to start that's more this aspect of being aware of data governance it's not that they don't understand the importance is they don't know where to start so that's where again you need to seek for help there are many consultancy companies expert companies I'm not talking about the big four I will don't give any names because they are also partner of us but I work for some but you have really small companies with high level of expertise around data management data governance framework data governance strategy which will educate the stakeholders of where to start and how to make it a success in their organization that's really great but maybe kind of diving a bit deeper here if I am a you know data governance stakeholder manager I'm owning the data governance program within my organization how do I evangelize the importance what are the kind of key tactics I can have to evangelize the import corners of data governance within the organization to The Wider organization as it is indeed a change management program that requires a cultural adoption of data governance methodologies and techniques indeed a change management and the educational part is really important so you have to equip your people your teams with not only the right tools but most importantly with the right skills and knowledge it's very important that not only people will understand the importance of data but they will know what to do with the data they have and also reinforce soft skills development around Collective mindset because data governance is really a collective and organizational program also making sure that your people are able to listen it's a lot of listening to the others and the challenges of the others having also a certain dose of pragmatism and Agility it's the end of two years program with the waterfall project plan which will never be respected I mean in fact just be iterative try to understand your long-term goals but be iterative on the day-to-day or week weekly basis and then also make sure that people can communicate easily with their peers communication is very important explaining why you are doing things trying to communicate the benefits of the organization so it's really around the human skills and not technical skills but soft skills which you have to invest and then of course once you decide to equip yourself with a specific platform then you train the people on how to use this platform from a system of standpoint but I would say that focus on soft skills focus on understanding the world of data they don't have to be data scientists or become data scientists or data experts but they need to understand the overall picture of the editable that's really great I couldn't agree more especially on the importance of skills here because the evangelism of why you need to acquire these skills but also clarifying hey once the data is of great quality you'll be able to leverage your skills to be able to derive value for the organization is extremely important now of course one thing that's also really interesting given your role at data Galaxy is as an outsider it's been really interesting to watch the data governance space you know especially the rise of many different categories of tools and new startups in this space last three years we saw for example the data observability space growing importance quite a lot we're seeing more and more data catalog system grow into the mainstream walk us through the technical ecosystem for data governance tool how it has evolved and what are now the must-have tools for any organization that is mindful of data governance data governance I would say application landscape has evolved tremendously in the past years what we saw a couple of years back was big players having one platform doing everything ETI Air Quality Storage data warehouse and everything so now we see more and more specialized players but still focusing a lot on the technical side so data sources data transformation etls data visualization code parsing there are plenty of players there but very little business focus very few are really focusing on how I can serve the business part of the organization what is the value I bring to them and I truly think that this approach is I would say over to only have the technical side we really need to focus on the business value and this is where we will from data Galaxy standpoint we are making the difference and the new players will also make a difference that's one contextual introduction now what we see is that many organizations are moving towards the best of breed applications to be integrated in their ecosystem through apis for instance because each application each platform is expert in what it's doing and not doing everything in average it's really being specialized it's really providing quality on the arena Road problem or challenge now in terms of what should be your application landscape around the data in your organization I would say first of all focusing on the technical part you need to have your modern data stack which makes things way easier so data Lake data warehouse but really on the cloud which helps in with the integration and once you have this modern data stack having data observability so piloting from the finops standpoint your data stack understanding the cost of storage the cost of computation the cost of operation is very important then you have of course your ETL elt stack to converge the data into one central location within your modern data stack accessible to your organization then what we tend to see now is the data mesh or data product architecture and related application so we in fact have there the Gateway between Technical and functional between it and business where you bring back the ownership at the business domain level which makes things I think from a data governance way easier then you have your data quality which is also important because if you want to be data driven you need to base your decision on qualitative data then you have data visualization NBI plenty of players there but they tend now to aggregate so probably in a couple of years we'll only have four or five big players in that area and then also your conceptual data model platform which will help Architects and business defining what should be a data model the Enterprise data model and how to roll it out in your organization and the last one which in fact will seal everything together is your data category because your data catalog should infect and within the data governance World We tend to use the hands of data governance data catalog is the foundation of your house so if you don't have your solid foundation with the information that can be shared across your organization it will be very difficult to enable the rest of your data stack or data modern stack in your company that's really great and one thing that you mentioned here I'm really excited to Deep dive into the data catalog side of things but before that you mentioned as well the rise of the data mesh architecture system where are we today in data mesh adoption there is quite a lot of discussion in the data space about the transition to data mesh how far along are we in the adoption of data mesh and do you see that that is something that is common across organizations or is that something that is only adopted by relatively High data mature organizations today what I see around data mesh first of all it has been a hype in 2022 with many books being written but in term of implementation we are still at the stage where many organizations are still investigating the feasibility of data mesh on the narrowed scope or perimeter it's not that they are fully rolled out with data mesh it's still a conceptual architectural approach because it gives guidance but it doesn't really give instructions on how to implement it it's really based on each company and each organization structure so we've seen that companies where they implemented partially data mesh on some parameters and we have companies where they are still trying to understand the architecture understand the data mesh principles before investigating because it means that you will have to rethink your entire architecture and identify different type of profiles and responsibilities so we are not there with companies fully data meshed it will probably come in 2023-24. that's exciting to see now of course given your worker data Galaxy I'd also be remiss not to talk about data catalogs walk us through some of the most important aspects of the modern data catalog and what should data leaders think about when they consider a data catalog solution data Galaxy in fact yeah we're indeed a data catalog solution and our vision is that data Galaxy solution should act as data knowledge workplace with one goal is bringing data to the people meaning that we are business driven in fact you need to have this information available within your organization and and act like your Google or Wikipedia around the data for the Enterprise and there are some key aspects to consider when opting for a modern data catalog is first the user experience it's very important because in terms of adoption this is one big enabler so for me this is the end of the technical platforms finding information about your data it should be as easy as Googling something I mean very simple interface ergonomic user-friendly few keywords and then you get the results that's really important then you have integration API is a given modern data catalog should have apis for integration and also it will ease the connection with your other modern data stack layers in a very simple way you don't have to rebuild complex mechanism I mean API will really free the data world and then we have artificial intelligence back in let's say five or six years stewardship was a very intense manual activity and now ai should support so a lot of artificial intelligence running in your data catalog to in fact bring augmented stewardship helping data storage with classification with organization with relationship okay and not having to discover everything by yourself but having the tools supporting this and suggesting you relations classifications tagging and so on and then a very important last aspect is the business value at the tip of your fingers it means that in fact the data catalog should be part of the user digital environment it's one of the key enabler in many organization and we've seen since we rolled out your browser Chrome and Edge extension that you can browse the data catalog straight from your working environment that adoption has increased I mean you don't have to connect to a third-party platform you really just have to query something and it's displayed in your working environment and this is really how we will ensure from a holistic view that business and common people in the organization are leveraging the data catalog content that's really great and maybe I'd love to learn some examples of data galaxy in the real world how it has helped real world data teams address some of the considerations you just mentioned like I told you in the in the introduction I've implemented data Galaxy with roughly 80 different customers so each industry has its own priorities each company has its own structure but if I could pick one I would pick the exam of a major French insurance company who implemented data Galaxy they were having a lot of challenges with data ownership and traceability and understanding of data transformation data indicators and algorithm so it was clear scope at the beginning we need to have a solution which will help us identifying ownership and understanding our indicators and algorithms for your specific scope or perimeter so what they did is they implemented data Galaxy to provide the organization with a unique functional and Technical desk I mean it's really the central point of information common language of their different use cases so the definition of an indicator was the same across the entire organization of course clear identification of ownership and stewardship what they also brought is the end-to-end data lineage so traceability of all the systems and transformation applied this system they also build this platform not only to spread information but also to gather information and help subject matter experts sharing their knowledge documenting the information valuing their knowledge and last but not least is how the data catalog is able to support the Regulatory and compliance requirements here is a solution C2 for insurance sector as the evidence showing that here we are in control of our data we know where it's coming from we know how it's transform we know who's the owner so it supports really their initiative and that's what we see in many organizations is building a current language having ownership and stewardship identified having traceability and building this common language around the data assets it's really awesome to hear and I highly recommend to the audience to check out the site now of course as we wrap up I'd be remiss not to ask you what do you think are the main Trends data leaders need to be mindful of in 2023 regarding data governance and data catalogs there are many Trends out there on the data governance or data management market and depending on who's publishing you have different Trends but I would say one of the common theme in 2023 would be data democracy is in fact how data governance data management data cataloging will improve the decision making so if you want to move to a data-driven mindset you need to make sure that your data is accurate then when you make the decision based on data you're not fooling yourself then it's increased collaboration that you share the data you in fact have more and more would say decision process around your data I mean not only that your data division but data is part of your day-to-day discussion and whenever you think you need to mitigate something you based your mitigation decision on data and then is also in terms of Trends greater transparency I mean it's giving people access to the data they need and we are back to the beginning of this discussion where many organizations it's still siled either it versus business or even business lines against business lines so here it's really having transparency and building enterprise-wide data sets and sharing it with the people what we can see also is improved customer service through data in today's competitive world we roughly offer the same service and same pricing in terms of business but the customer relationship will play a big role and if as a customer facing employee you don't have the relevant data to bring the right offer to bring the right Services it will become a competitive disadvantage so really having this in mind will help many organizations and then the last one which is a trend but which is also a trend since many years is reducing the data silos I mean breaking barriers across Department having this overall entire company-wide data strategy and data language is a very important aspect that's really great very exciting to see the year ahead in data governance now law as we close out Today's Show do you have any final words for the audience I would give the market one simple advice it's never to earn to start your data governance and data catalog initiatives I mean even if it's in Excel just do it it will be the foundation of your future data caterer then make sure that you have a data vision and strategy drafted not necessarily ready to be rolled out but at least drafted and then also talk to the business make communication key in your organization and then jump I mean go ahead harness the power of data governance and change you will value your data and build your competitive Advantage so do it now and then the last word is Thank You Adele for the opportunity I really like to share around data governance data management data cataloging topics and for the one listening to this podcast do not hesitate to subscribe to my LinkedIn profile I'm sharing day-to-day tips on data management and data governance thank you so much Lauren for coming on the podcast really appreciate it been listening to data framed a podcast by datacamp keep connected with us by subscribing to the show in your favorite podcast player please give us a rating leave a comment and share episodes you love that helps us keep delivering insights into all things data thanks for listening until next timeyou're listening to data framed a podcast by datacamp in this show you'll hear all the latest trends and insights in data science whether you're just getting started in your data career or you're a data leader looking to scale data-driven decisions in your organization join us for in-depth discussions with data and analytics leaders at the Forefront of the data Revolution Let's Dive Right In hello everyone this is Adele data science educator and evangelist at data Camp arguably one of the biggest obstacles for organizations in building a data culture today is trust and data and Trust ultimately boils down to data quality and an organization's ability to govern its data well this is where data governance comes in and this is why it's so crucial so how do you make gains on data governance how do you bridge the gap between functional teams and Technical teams to create a common data governance Vision these are all questions that are going to be answered by today's guest laurentress Lauren is a data governance evangelist and director of Professional Services at data Galaxy he is a seasoned professional and expert with a proven track record of successful implementation of data governance related projects across the world Lauren is skilled in data management data governance data quality Master data management and a lot more he has a strong Consulting background with prince2 practitioner certification and has been focused on pragmatic project management around data management throughout his career he acted as the perfect middleman between I.T and business regarding federating efforts in developing a unified view of an organization's data landscape throughout the episode we spoke about the state of data governance today how data leaders and organizations can start their data governance Journey how to evangelize the importance of data governance and gain buy-in from everyone within the organization the state of data governance tooling today and a lot more if you enjoyed this episode make sure to let us know and now on to today's episode Laurence wait to have you on the show hello there thank you very much for the invitation and the opportunity to share around data topics I'm very excited to discuss with you all things data governance and quality how a culture of data trusts promotes higher use of data how data leaders can succeed at data governance and more but before can you please give us a bit of a background about yourself sure I started in the data governance and data World about 20 years ago prior to data was a project manager and doing the liaison between I.T and business which helped me a lot in my data career then what I did is in fact I was working for a company where they implemented Master data management it was my first experience with data where I presented my business unit and then at the end took over the overall program management then I worked for a major Consulting companies and implemented data governance Frameworks organizations and tools internationally so I traveled a lot around the world which is also nice because you're confronted to different cultures and opens your mind and then the three year back I joined data Galaxy to set up Professional Services and help our customers with the implementation and the return on investment on the data platform recently I moved to the Evangelist position so I'm spreading the good words around data governance I'm also participating in podcasts like yours but also in exhibition and events in Keynotes bottom line making sure that our product fits the market needs that we also voice the market in terms of features and expectations and also making sure that data Galaxy is a known brand across emea and now also across the US on the personal side I live in Belgium I'm passionate about food it's probably also the reason why I called my daily LinkedIn posts to the data governance kitchen so do not hesitate to subscribe to my profile and you will get daily hints on data governance data management data catalog that's really great we definitely have a lot of comment I also live in Belgium and I also am passionate about food but I'm really excited to chat with you about all things data governance you know I'd love to set the stage for today's chat by trying to understand where the data governance and quality space are at today I think we can all agree that the past few years have been you know marked by the March for organizations to become data driven organizations have been making massive investments in data infrastructure Talent data collection data culture and more naturally the data quality space has evolved quite a lot to accommodate the growing needs of organizations so maybe starting off I'd love to First understand how have you seen the importance of data governance evolve over the past decade and what makes it such an important area of investment for data leaders today that's a great question and then it's the Genesis of data culture in many organizations what I've seen is that historically anything related to data was managed by the IT department in a very siled way it was a kind of Kingdom approach around the data and the same was applicable to business departments very little information was documented they had no common language and they were working in a very closed way and not sharing information across a line of businesses now things have changed the mindset have progressed or evolved data is recognized as an important asset in many organizations so it's let's say three to five years back we saw the rise of data self-service in the API world so we were giving the power back to the business they were provided with data sets and able to render personal queries and reports but to be able to understand your data set you also need to have discovered language definition of kpis definition of business terms so the rise of data catalog really started there and then it really helped building this common language I mean we are both European based if you put a German a friendship Belgian a Dutch a Danish and Italian and a Spanish guy around the table they will have no common language it's very difficult to understand each other same goes for the data and the data catalog brought this kind of Esperanto type of language around the table and people were able to communicate and understand and look at the same thing with the same definition so data governance helped a lot it creates created the need for data catalogs and it was the rise of some of our competitors back in these days I mean Colibri started probably 15 years back and really identified these issues and data Galaxy we really had the same we were facing many feedback from our customers that they were lacking business and Global language and then last but not least is also the understanding from many organizations that data is a variable asset it's not just information on the server but it can really make a huge difference in the competitive landscape I would say or it's a competitive advantage and the ones leveraging this information to the max are quieter leaders on the market if we simply take examples such as Amazon or Uber I mean these guys they know you in and out when you book a ride and they come with the right offer at the right moment and this is because they are leveraging the available data that's really great and there's definitely a lot to unpack here you know one thing I want to kind of Deep dive in is how is data governance evolving given the rise and complexity of the different data Landscapes organizations deal with right now you know we mentioned that organizations are making more and more investments in data there is a bigger recognition that data is recognized as an asset data is given back to the people it's given back to the business and this in turn creates an increasingly complex data value chain so given how complex the data value chain is becoming across an organization today how have the challenges with data quality and governance evolved what do you think are the top obstacles in terms of data governance for organizations today that's right many companies are evolving in terms of technical stock big organization they have this Legacy of I would say old-fashioned systems it's not old-fashioned but the old ways of doing things with relational on-prem databases like you know record NES 400 but also they start understanding the importance of modern data stack in terms of data lakes and modern Technologies so they really have this struggle of where do we go do we keep the Legacy do we move to the full access for instance and full cloud or do we have this hybrid approach and what we see is many companies for now are at this stage of having this hybrid approach because they will never the commission Old Stock which is a total different story for startups because they are Cloud native they started with Cloud tools so it's a bit easier in terms of data landscape however they all face the same challenges and I would say if we need to outline some of them is traceability is where the data is coming from what are the system it goes through and what are in fact as well the transformation applies and the more complex is your it landscape or data landscape the more complex it is to understand the Journey of your data and the transformation because it goes through so many systems with replication with aggregation with transformation that people are totally lost they don't know which transformation and when they have a data quality issue at the end they don't know where it happened so it's very important to have this traceability and transformation understanding and now also another type of challenge is indeed identifying the real source of error with let's say your transactional system your name Adder is on an invoice but where is the issue with your let's say address is it coming from the initial transaction is it coming from accounting is it coming from marketing so it's really important to be able to really find the source of error it means that if you want to find this source of error and take some remediation action you need to be able to contact the right person or the right people which is a big challenge around data stewardship what I hear from many organizations when we start discussing about data catalog is of course we know where the ever happened but we never know who to contact is It Adele at accounting is it local at the marketing or is it someone else in another department that's where data cataloging is playing a very important role and the last part is something which is not always understood by organization is your data context because data quality issues can happen of course but depending on the content it has more or less impact I mean for instance a birth date of someone when it comes to generating the invoice it has very few importance I mean it's not important that your birth date is correct when you receive the invoice however if you're for marketing and initiate a reward plan for your birthday then it's very important because you need to receive this offer right on time on your birthday so to summarize I would say traceability transformation source of error data stewardship and ownership and data context are are very very big challenges for organizations today that's really great and let's dive deep maybe into the stewardship component and the overall ownership of the data governance program this increasingly complex value chain also puts stress on the ownership when it comes to data quality and data governance data has produced Upstream by various functional teams collected from various sources then transformed Downstream and is used by a variety of stakeholders within the organization who do you think is the owner of a data governance program or the data governance agenda in general and who should be the main stakeholders when it comes to the data store trip program well this is one million dollar question who is this is this is quite a difficult topic and it's a complex question I think there are many many stakeholders in the data Journey which make things complex either the only thing the responsibility resides with someone else or they all think they are the owner of the data and the others can't touch it so we really need to set the boundaries there what I would say and I tend to tell my customer is first of all there can only be one data owner in the entire value chain I mean it's like cooking if you have 10 chefs in the kitchen things will run very very bad so you only have one Chef in the kitchen you only have one data owner in your data value chain it means that the exercise is quite complex to identify this ultimate owner one approach is to say Department the line of business who created this data the first time is the owner because he or she has the ability to modify the data if there is a data quality issue so that's one approach what I also think is important is to in fact draw your data value chain because you have all the steps all the systems and it will definitely help your data governance organization to find out the ultimate ownership again it's a value chain so value chain mean you have many links and each link needs to understand that he or she is receiving a data item altering the data item doing something with this data and passing it on to another person so it's everyone's responsibility to make sure that when you pass the data to someone else the quality of that data is very good and then I would say that you need once you've drawn this value chain you need to define the chain of commands so knowing exactly if you have a data issue if you need to update a data item who are the person in charge and who has the ultimate power to alter this definition and this is where data governance and data catalogs are playing big role because the data catalog is the centralized information around your data items in terms of ownership stewardship traceability definition and so and if we want to kind of dive deeper there you know in an organizational Enterprise organization that's still identifying it's data governance strategy who are the type of profiles that you think are suitable owners of the data governance agenda and what are the type of stakeholders that support them there I would say based on the implementation I made it you have various profiles but where companies are successful is where you have a CDO because the CTO is really the person at senior leadership level with the authority and the mandate to make sure that data is valued and data governance is implemented and this makes a big difference because otherwise if it's just you and I for instance in a day-to-day life your marketing and accounting trying to do things around data governance it will be very complex because you don't have the resource you don't have the budget you don't have the Mandate so you really need to have sponsorship at a leadership level with this video quite often you also see that you have head of data governance for instance who are playing a big role or you have a CD office with a bunch of people managing the data governance but it's really a separate function in the organization that's really great and I think this really marks a great segue into what actually makes the data governance strategy and approach successful as data becomes even more critical for decision making and product development data governance can have massive implications for the organization as we've discussed so far so you've had quite a lot of experience in implementing successful data governance programs can you outline what you think are key components of a successful data governance strategy yes that's as well an important aspect because the word strategy is very important I've implemented data Galaxy with around 80 customers in the past three years so what I've seen with successful customer are having data vision and strategy at executive board level that's very important because these guys will have to Define what they want to do with data can be monetization it can be data driven it can be whatever they want but at least they have the vision on what they want to do and this is the very start and then you have the CDO role which is in fact empowered and in charge of making this strategy life and also having this frequent communication between the rest of the data organization and the management board so here's the guide of person relaying this information and then they also have to Define their why is why do we want to be data driven why do we want to Value data because at the end there should be a business outcome and then once this part is defined and clarified they pass it on to the business and I really say to the business because data governance and data cataloging and data valuation in general is the responsibility of the business it's the end of the it Kingdom doing everything around Data Business takes the lead and then it comes there to support in fact to access the data to trace the data but it's really a business matter and then once you have this strategy business understands the importance now it's also to identify what are the daily pains challenges with the data once you have these challenges you will know how your data governance initiative and data cataloging will relieve the pain and help the business getting better I also strongly suggest to have a data governance by Design so don't try to make it a big bang in the organization by building complex structure stay simple be a child be iterative so try with one line of business with one use case and then two use cases and gradually expanding the organization it will be way more efficient than trying to have this big Bank and then another success factor is how you identify the indicators around your data governance initiative not that you necessarily have to identify the return on investment in terms of dollars but the indicators which will make sure that you're on the right track and remember we identified paints if you identify pains you identify the benefits and you monitor these benefits and once you have these benefits reported you need to communicate across the organization it's a key component of your data governance initiative the way you will communicate every time I mean every touch point in the organization turn all's quarterly business reviews see your speech of the week or newsletter whatever data should be embedded in there because this is the way management shows the importance of data in the organization and the last part is really the human part data governance initiative is based on human it's a change management courses and is the same for many other projects you can buy any tool but the tool will only do what you tell him to do and if you don't focus on human that you understand that people are reluctant to change they need to understand the value they need to be informed they need to communicate once you have this under control then you have more chances to be successful in your data governance initiative that's really great and there's a few things I want to really focus on here so you mentioned you know data governance having macro benefits that are made clear there is a why that is attached to it that is communicated to the entire organization CDO needs to act as an evangelist for example and the organization outside when discussing the data governance program and finally it's a change management program right it's also a cultural program it's a way of work that needs to change and I think one thing that we've seen across you know data framed episodes regardless of analytics Investments whether it's the illiteracy or data culture investment a new machine learning project the importance of evangelism and buy-in right by the executive team and also by The Wider organization is extremely important for these types of Investments I think one thing standing in the way of investments in data governance and data quality is that it's not necessarily a shiny use case you can show in a quarterly earnings report if you're a leader or something you can add to your CV like a machine learning project or you know a dashboard if you're a data practitioner how do you convince stakeholders within the organization to invest in data governance especially if they don't yet view it as a strategically important project Adele you're asking tricky questions in fact it's complex and I don't have the magic source to make things happen from one day to another but if senior leadership does not understand the importance of data and the value of data and data governance my first reaction would be to tell them do nothing until you hit the wall and understand the importance of data governance that's a total waste of time trying convincing someone who's totally reluctant to the information of course it's a bit on the job site that I'm telling the sentence but if they don't understand I mean it's a long effort and most likely you need to have this external view or external people or external I would say for instance a consultancy firm coming and explaining why it is important how it works within other companies to show the importance what I've seen is that most of the leaders and stakeholders they have a vague feeling that the data is important but in fact they don't know where to start that's more this aspect of being aware of data governance it's not that they don't understand the importance is they don't know where to start so that's where again you need to seek for help there are many consultancy companies expert companies I'm not talking about the big four I will don't give any names because they are also partner of us but I work for some but you have really small companies with high level of expertise around data management data governance framework data governance strategy which will educate the stakeholders of where to start and how to make it a success in their organization that's really great but maybe kind of diving a bit deeper here if I am a you know data governance stakeholder manager I'm owning the data governance program within my organization how do I evangelize the importance what are the kind of key tactics I can have to evangelize the import corners of data governance within the organization to The Wider organization as it is indeed a change management program that requires a cultural adoption of data governance methodologies and techniques indeed a change management and the educational part is really important so you have to equip your people your teams with not only the right tools but most importantly with the right skills and knowledge it's very important that not only people will understand the importance of data but they will know what to do with the data they have and also reinforce soft skills development around Collective mindset because data governance is really a collective and organizational program also making sure that your people are able to listen it's a lot of listening to the others and the challenges of the others having also a certain dose of pragmatism and Agility it's the end of two years program with the waterfall project plan which will never be respected I mean in fact just be iterative try to understand your long-term goals but be iterative on the day-to-day or week weekly basis and then also make sure that people can communicate easily with their peers communication is very important explaining why you are doing things trying to communicate the benefits of the organization so it's really around the human skills and not technical skills but soft skills which you have to invest and then of course once you decide to equip yourself with a specific platform then you train the people on how to use this platform from a system of standpoint but I would say that focus on soft skills focus on understanding the world of data they don't have to be data scientists or become data scientists or data experts but they need to understand the overall picture of the editable that's really great I couldn't agree more especially on the importance of skills here because the evangelism of why you need to acquire these skills but also clarifying hey once the data is of great quality you'll be able to leverage your skills to be able to derive value for the organization is extremely important now of course one thing that's also really interesting given your role at data Galaxy is as an outsider it's been really interesting to watch the data governance space you know especially the rise of many different categories of tools and new startups in this space last three years we saw for example the data observability space growing importance quite a lot we're seeing more and more data catalog system grow into the mainstream walk us through the technical ecosystem for data governance tool how it has evolved and what are now the must-have tools for any organization that is mindful of data governance data governance I would say application landscape has evolved tremendously in the past years what we saw a couple of years back was big players having one platform doing everything ETI Air Quality Storage data warehouse and everything so now we see more and more specialized players but still focusing a lot on the technical side so data sources data transformation etls data visualization code parsing there are plenty of players there but very little business focus very few are really focusing on how I can serve the business part of the organization what is the value I bring to them and I truly think that this approach is I would say over to only have the technical side we really need to focus on the business value and this is where we will from data Galaxy standpoint we are making the difference and the new players will also make a difference that's one contextual introduction now what we see is that many organizations are moving towards the best of breed applications to be integrated in their ecosystem through apis for instance because each application each platform is expert in what it's doing and not doing everything in average it's really being specialized it's really providing quality on the arena Road problem or challenge now in terms of what should be your application landscape around the data in your organization I would say first of all focusing on the technical part you need to have your modern data stack which makes things way easier so data Lake data warehouse but really on the cloud which helps in with the integration and once you have this modern data stack having data observability so piloting from the finops standpoint your data stack understanding the cost of storage the cost of computation the cost of operation is very important then you have of course your ETL elt stack to converge the data into one central location within your modern data stack accessible to your organization then what we tend to see now is the data mesh or data product architecture and related application so we in fact have there the Gateway between Technical and functional between it and business where you bring back the ownership at the business domain level which makes things I think from a data governance way easier then you have your data quality which is also important because if you want to be data driven you need to base your decision on qualitative data then you have data visualization NBI plenty of players there but they tend now to aggregate so probably in a couple of years we'll only have four or five big players in that area and then also your conceptual data model platform which will help Architects and business defining what should be a data model the Enterprise data model and how to roll it out in your organization and the last one which in fact will seal everything together is your data category because your data catalog should infect and within the data governance World We tend to use the hands of data governance data catalog is the foundation of your house so if you don't have your solid foundation with the information that can be shared across your organization it will be very difficult to enable the rest of your data stack or data modern stack in your company that's really great and one thing that you mentioned here I'm really excited to Deep dive into the data catalog side of things but before that you mentioned as well the rise of the data mesh architecture system where are we today in data mesh adoption there is quite a lot of discussion in the data space about the transition to data mesh how far along are we in the adoption of data mesh and do you see that that is something that is common across organizations or is that something that is only adopted by relatively High data mature organizations today what I see around data mesh first of all it has been a hype in 2022 with many books being written but in term of implementation we are still at the stage where many organizations are still investigating the feasibility of data mesh on the narrowed scope or perimeter it's not that they are fully rolled out with data mesh it's still a conceptual architectural approach because it gives guidance but it doesn't really give instructions on how to implement it it's really based on each company and each organization structure so we've seen that companies where they implemented partially data mesh on some parameters and we have companies where they are still trying to understand the architecture understand the data mesh principles before investigating because it means that you will have to rethink your entire architecture and identify different type of profiles and responsibilities so we are not there with companies fully data meshed it will probably come in 2023-24. that's exciting to see now of course given your worker data Galaxy I'd also be remiss not to talk about data catalogs walk us through some of the most important aspects of the modern data catalog and what should data leaders think about when they consider a data catalog solution data Galaxy in fact yeah we're indeed a data catalog solution and our vision is that data Galaxy solution should act as data knowledge workplace with one goal is bringing data to the people meaning that we are business driven in fact you need to have this information available within your organization and and act like your Google or Wikipedia around the data for the Enterprise and there are some key aspects to consider when opting for a modern data catalog is first the user experience it's very important because in terms of adoption this is one big enabler so for me this is the end of the technical platforms finding information about your data it should be as easy as Googling something I mean very simple interface ergonomic user-friendly few keywords and then you get the results that's really important then you have integration API is a given modern data catalog should have apis for integration and also it will ease the connection with your other modern data stack layers in a very simple way you don't have to rebuild complex mechanism I mean API will really free the data world and then we have artificial intelligence back in let's say five or six years stewardship was a very intense manual activity and now ai should support so a lot of artificial intelligence running in your data catalog to in fact bring augmented stewardship helping data storage with classification with organization with relationship okay and not having to discover everything by yourself but having the tools supporting this and suggesting you relations classifications tagging and so on and then a very important last aspect is the business value at the tip of your fingers it means that in fact the data catalog should be part of the user digital environment it's one of the key enabler in many organization and we've seen since we rolled out your browser Chrome and Edge extension that you can browse the data catalog straight from your working environment that adoption has increased I mean you don't have to connect to a third-party platform you really just have to query something and it's displayed in your working environment and this is really how we will ensure from a holistic view that business and common people in the organization are leveraging the data catalog content that's really great and maybe I'd love to learn some examples of data galaxy in the real world how it has helped real world data teams address some of the considerations you just mentioned like I told you in the in the introduction I've implemented data Galaxy with roughly 80 different customers so each industry has its own priorities each company has its own structure but if I could pick one I would pick the exam of a major French insurance company who implemented data Galaxy they were having a lot of challenges with data ownership and traceability and understanding of data transformation data indicators and algorithm so it was clear scope at the beginning we need to have a solution which will help us identifying ownership and understanding our indicators and algorithms for your specific scope or perimeter so what they did is they implemented data Galaxy to provide the organization with a unique functional and Technical desk I mean it's really the central point of information common language of their different use cases so the definition of an indicator was the same across the entire organization of course clear identification of ownership and stewardship what they also brought is the end-to-end data lineage so traceability of all the systems and transformation applied this system they also build this platform not only to spread information but also to gather information and help subject matter experts sharing their knowledge documenting the information valuing their knowledge and last but not least is how the data catalog is able to support the Regulatory and compliance requirements here is a solution C2 for insurance sector as the evidence showing that here we are in control of our data we know where it's coming from we know how it's transform we know who's the owner so it supports really their initiative and that's what we see in many organizations is building a current language having ownership and stewardship identified having traceability and building this common language around the data assets it's really awesome to hear and I highly recommend to the audience to check out the site now of course as we wrap up I'd be remiss not to ask you what do you think are the main Trends data leaders need to be mindful of in 2023 regarding data governance and data catalogs there are many Trends out there on the data governance or data management market and depending on who's publishing you have different Trends but I would say one of the common theme in 2023 would be data democracy is in fact how data governance data management data cataloging will improve the decision making so if you want to move to a data-driven mindset you need to make sure that your data is accurate then when you make the decision based on data you're not fooling yourself then it's increased collaboration that you share the data you in fact have more and more would say decision process around your data I mean not only that your data division but data is part of your day-to-day discussion and whenever you think you need to mitigate something you based your mitigation decision on data and then is also in terms of Trends greater transparency I mean it's giving people access to the data they need and we are back to the beginning of this discussion where many organizations it's still siled either it versus business or even business lines against business lines so here it's really having transparency and building enterprise-wide data sets and sharing it with the people what we can see also is improved customer service through data in today's competitive world we roughly offer the same service and same pricing in terms of business but the customer relationship will play a big role and if as a customer facing employee you don't have the relevant data to bring the right offer to bring the right Services it will become a competitive disadvantage so really having this in mind will help many organizations and then the last one which is a trend but which is also a trend since many years is reducing the data silos I mean breaking barriers across Department having this overall entire company-wide data strategy and data language is a very important aspect that's really great very exciting to see the year ahead in data governance now law as we close out Today's Show do you have any final words for the audience I would give the market one simple advice it's never to earn to start your data governance and data catalog initiatives I mean even if it's in Excel just do it it will be the foundation of your future data caterer then make sure that you have a data vision and strategy drafted not necessarily ready to be rolled out but at least drafted and then also talk to the business make communication key in your organization and then jump I mean go ahead harness the power of data governance and change you will value your data and build your competitive Advantage so do it now and then the last word is Thank You Adele for the opportunity I really like to share around data governance data management data cataloging topics and for the one listening to this podcast do not hesitate to subscribe to my LinkedIn profile I'm sharing day-to-day tips on data management and data governance thank you so much Lauren for coming on the podcast really appreciate it been listening to data framed a podcast by datacamp keep connected with us by subscribing to the show in your favorite podcast player please give us a rating leave a comment and share episodes you love that helps us keep delivering insights into all things data thanks for listening until next time\n"