Hello, everyone. Welcome to my talk. Hope you guys are doing good. Today we'll be talking about empowering non profiting with enterprise data warehousing. We'll be talking about what is data warehousing, how we can empower non profits with when you build the data warehousing. And we'll also focus off, solution delivery, right? seamless donation and program management data. Some of the predictive analytics with the machine learning capabilities, how we can automate data processing for consistent results, and other important pieces, secure role based access management. Let's dive into our session. We'll be talking about overview of enterprise data warehousing, followed by some of the benefits of your organization. What is enterprise data warehousing? A powerful centralized data ecosystem that unifies all your organization's information in one secure location. Essentially, a foundation of tracking long term trends, measuring impact, and making data driven decisions. That is our end goal when you build the enterprise data warehousing. How we can make sure our data is so meaningful. And it will impact the business. Strategic tools that revolutionize how nonprofits harness data to amplify their social mission and create lasting change. Let's talk about some benefits for your organization. Transform manual processing to automated workflows. Saving a valuable staff time and the resources. Make confident data backed decisions by accessing comprehensive insight across all programs and operations. Strengthen donor relationship through data driven personalization and targeted engagement strategies. Demonstrate your organization impact with the precise measurement and compelling impact to your reports. Now let's talk about unlock the potential of data for nonprofits. So what are the stages we need if you wanted to, unlock the potential of data for your organization, right? So we talk about the three steps here, right? One is strategic decision making, the data automation, and empower your teams, right? So when you say strategy, Strategic decision making turn complex data into compiling insights that guide resource allocation, program development, and impact measurements to advance your mission with confidence and the clarity. And the data automation eliminates repetitive tasks and manual data entry by building an integrated data ecosystem. That connects all your system, saving hundred of step hours while improving accuracy, right? So how best you are doing the automation of your data, right? Like now, you are bringing some data into some system, and some other person is bringing the same data from the same system. But a different, end goal of it, right? instead of that, make sure you have the streamlined process that will, avoid all this, repetitive tasks, right? and empower the teams. Oh, this is one of the key area which we need to talk about. It's not only impact, while building the enterprise data warehouse, but it will impact overall organization. The best you empower your team, you will get to the more benefits out of from the team. Breakdown information barriers. And equip your teams with real time data access, enabling them to make informed decision faster and focus their energy on creating lasting social impact. So if you focus on these three, that definitely will help us, to better shape our data. In our organization now, let's talk about some core technical capabilities when you build the enterprise data warehousing, right? So let's talk about cloud based infrastructure And the etl processing advanced analytics engine Data governance framework, so I am talking of these four. the cloud based infrastructure i'm talking now Because like now the whole world is moving into the cloud based system, right? And again, this is just a suggestion, right? Like now based on your, in your organizations and the budgets, we can choose whatever the infrastructure best works for you. just wanted to make sure that's scalable. Secure and ensuring 24 by 7 data availability and also you have the disaster recovery plan in place. So this is some of the organizations I like know, tend to miss the discovery, disaster recovery plan. But this is one of the, one of the key element we need to consider. Like when you have the disaster recovery plan, so you can, sleep, You can sleep that, you, if something goes wrong, you got all is a plan B in place, right? And when you have the data, disaster recovery plan, make sure it's updated. whatever the schedule you have from the main product system to the, recovery, instances, right? And then, let's talk about the ETL processing. we're not talking about the, any specific tools. There are some tools in the market, but whatever works for you guys, you can then definitely choose those tools. but ETL processing will help us. The data from the different sources and, it will cleanse your data and it'll make sure it's proper properly transformed with the right rules and, eliminating all the junk data. and it's also helps to set the, the load the data, With the right time, right? What are the time frame, you and your organizations, works for you guys, right? So this will work in both ways. one is to bring the data, right? And to update the data into the periodical, right? So that's when the ETL processing plays a major role, right? And we'll also talk about SCD2, how that will impact, right? And then we'll talk about that space as well. And then advanced analytics engine, built in statistical analysis and machine learning capabilities for predictive insights here. So that's what I wanted to focus here. so since everyone is talking about the AI and machine learning, to make that AI works better, right? Our, the warehouse and the architecture and our data should be good enough so that way you will get the, good value out of it, right? if you have the bad data and if you apply on the, the AI analytics on the top of it, Technically we will get, we will end up with, different results, right? to avoid those things. Like we need to make sure our data is, qualified and quality and other meet other aspects as well. And, and another one is the data governance framework. This is also very important. the part I would say when you're building the enterprise data warehousing. So if I put it in the simple term. Why we need the data governance, right? Yes, you can build the enterprise data warehousing without a data governance framework, but having the data governance in place, which means you are making sure the entire organization is talking the same language. Which means, for example, if you talk about, A RIE example, let's say country, right? So maybe one source bringing data, a country, say it's like in the United States. From other source, you may see it's U. S. Another country, you can see U. S. A., right? All these three are valid, right? And again, the departments also use a different terminology. But when the data governance in place, so they'll make sure. You wanted to use the USA or United States of America or just US. So they will make the decision that they'll discuss with stakeholders and make sure they understand why it is important. And they will help us. Okay. From now on, if country comes in, let's use as a USA. All right. the organization is, finalized the naming convention, but it's just an example, but we can talk a number of things, which falls under the data governancy. Yeah. So I would say this is also one of the important steps we need to consider when you are building the enterprise data warehouse. Now let's talk about the, the power of data integration. so why you wanted to integrate your data, right? it's really important. We'll talk about the step by step, but I'd like to give you, high level overview before we dive into the more in detail. So let's say in your organization. You have data is coming from the CRM system, right? Most of the nonprofit organizations in rely on a majority of the transactions on the CRM platform. And you have some other fundraising platforms. The data is pulling from some other, the sources, right? And definitely you will have the finance system in your, in every organization. And besides that, you will also have like HR and other security and whatnot, right? Different, the teams, right? And, I would say business units and different business units, and they maintain the data into the different, source systems. Now. If you don't integrate your data into the single platform, so you got isolated with your specific views, right? Let's say if you're, If you are only getting the CRM data into your, any one of the database, right? So you always end up with providing the analytics out of that CRM data. But some, in some scenario, right? You want it to know the analysis based on the CRM data along with the financial data. So then, you don't have, Option to create or provide analytics to the management system Combination of data is coming from the CRM and the financial right and vice versa or maybe you know if you wanted to provide The high level summarize over you to a senior management system from combination of all the different business units So if you don't have the data integrated In a single platform, and it's hard to, provide, provide the analytics to the senior, that's, I would say, one of the major benefit having that. And then if you're looking about the another aspect, like the maintenance wise, or, so the single team can maintain everything and same template on the same platform. And the maintenance is easy and you'll have that in a single tune to maintain it. And you'll have structured data, right? Yeah, like that kind of, we have many other, the benefits when you integrated data from the multiple systems, right? With that process, what we need, right? okay, we were talking about the data integration, but how are you going to do it, right? that is a key aspect here. So what we need, right? Like advanced data pipeline architecture. The architecture is the key role here, right? The more strong your architecture and when you build the architecture It's worth spending More time when you're building the architecture to consider the 360 degree angles, right? So that will help you to accommodate the existing, some of the known issues. And, the future, like now, what are the issues we're going to end up, right? this is the important thing, right? that's what my another point saying it's scalable architecture is also one of the key here. And secure integration protocols, right? Like now, how secure you are bringing the data and making sure they're integrating, right? So that's also one of the key aspects here. and, okay, you have, established architecture, which consider all the pros and cons and the feature, error free system, right? And you have also making sure like all the, all the secure security integration in place, right? But how your data, Is going to be the syncing in the process, right? Like now, what time you wanted to sync your data. So are you wanted to go with the 6am or the 7am or 8am when no business coming to the desk, like now your data is happening. So this is what we need to discuss a little bit more. when you set up the synchronization, it's not just a synchronization, right? Let's say business is asking for, okay, I need the data every 6am, right? So we can set it up 6am, but we need to focus a couple of things here. When exactly source system is updating, right? If the source system is updating nine o'clock, right? But if you set up your synchronization in 6 a. m So obviously, we will not have the updated data, right? So we need to make sure and consider both angles And explain to the business saying that come up with a common decision and providing the pros and cons, right? So that is, one of the, the synchronization, area which we need to focus. And, the data quality and the monitoring, right? I will talk about in detail each and every, point here. But yeah, so the data quality. Scalable architecture and compliance management, right? So I would say, if you follow all these steps So that will give us, you know help us to get the a place where we have the Data, we integrated data in a well formed manner Okay. So now let's talk about the building a robust data warehouse, right? So I would again call it about the, four pillars here, right? Data integration, data quality, data modeling, and data analysis and visualization. So data integrations, as we discussed, right? Like we need to follow all that. which we discussed, right? And the data quality, implement, validation rules and automated cleansing workflow that catch errors before they impact your decisions. Our systematic approaches ensure your data remains, pristine, liable, And they're ready to drive meaningful action across your organization and the data modeling. This is really, the important, one of the important part which will play a major role. So whoever is building the data model, like normally people use like in the data model also will call it is and we have the couple of tools offered to build the data modeling, but which are tool which we are, which you are opting right? Make sure our the large scale physical data model in place. And, so when you see the logical, so you'll have all the table, And how they establish the relation between each tables and what is the dependency and how they're related. So the clear logical structure has to be there and it has to run by, respective teams, And then followed by, we need to build the, A physical data model, which will give you even more detail, right? what is the table, right? which table contains what kind of elements, what are the primary and foreign keys, and what is the relationship between them? What kind of data each table holds? So this, two documents are really key aspects to move forward To build the enterprise data warehousing. So again, like now, make sure we need, we have captured this step and we followed without missing it because this is a step which we are going to use as a future reference, right? Like now if something you wanted to see it. So we, this is a place where we are going to, in a reference as a master copy, right? And, and data analysis and visualization. As everyone knows, our end goal is to provide a best visualization, with a, with a data analysis, right? Um, so that way that meaningful analysis, when, the, when the management is saying they can take. the informed decisions, based out of the visualization, which we provided, right? That's when our, we achieved our end goal, right? Just having the enterprise data warehousing, and if you're not providing any, analytics, Our visualizations to remain as main. that, that might be, the different way of looking into it, right? So how best, we can provide the visualization side, like now operational reports and, analytical reports. So operational versus analytical reports, right? You can talk about, operational, report. Reporting means, the reports which business is looking for near real time data, right? And, when you're looking for analytics, like now it's, it's you can set the rules, whether it's the T minus one day data or T minus two day data, based on, again, your source system, how, you set up the scheduling and how that works, right? now, let's talk about the data quality, right? this is one of the key, I personally, I like it because what I believe here, no matter, how, The best reporting visualizations, right? Or no matter how big volume of data you are, giving to your management. If the data is not accurate, if the data is not quality enough, so obviously, it will lead to in other directions. so that is the reason we wanted to make sure what are the data we are bringing into our warehouse. It should be, quality enough and to do that, you just need to ensure all the steps we have forwarded, right? Smart Data Validation, like we have different ways of validating it, right? So you need to connect to the Smart Data Validation, Real time Quality Monitoring, Clear Data Standards, Advanced Data Profiling, Automated Cleansing Workflow, and Data Governance Framework in place, right? So when you follow, this, the six steps, and make sure in each stage, you have, You have set the right rules and you have validated and everything and executed well, right? So that's when you will get that quality of the data in your warehouse along with the, the data governance framework. Now let's talk about the data modeling, uncovering actionable insights. As we talked about, right? we divided it into two parts, right? the large scale data model and the physical model. Okay. the donor segmentation, right? We wanted to segment different level right here. So create dynamic donor, persons using advanced clustering analysis that combine giving patterns, engagement metrics, and demographic data to deliver hyper personalized outcomes. Outreach that increases donor retention by up to 30%. And then, the next step, program performance analysis, and operational efficiency tracking, and predictive analytics integration, and multidimensional data modeling. to consider all these, we need to build this, multidimensional data modeling before we, start to build, the enterprise data warehousing. Now let's talk about the data analysis and visualization. this is the driving informed decision making right, as we mentioned before. So how this is gonna work, right? Strateg decision intelligence. And donor relations cultivation, real time analytics integration, operational performance optimizations, interactive visualization tools. Custom reporting framework, right? so that based on the reports which we provide based on the data in our warehouse, that's when, try a strategy decisions in, decisions, simply, make the decisions here. And this will also help to know donor relationship cultivation. So based on the visualizations of the best of the data, which we provided to the management. So they will analyze, how the donor, cultivation is, is working. Yeah. And yeah, real time analytics integration, implement real time analytics capabilities with automated data refresh pipelines, enabling, immediate insights and rapid response to changing conditions. through live monitoring dashboards. So this is what we were talking about, the operational, the reporting system, right? So we need to build and our warehouse should be sustainable to provide the real time data warehousing. So again, like this will work case by case. Um, so your source and your, the ETL. And, your, your job, your jobs and, your reporting, what are the reporting systems you're using? we need to align in the same place in order to provide the real time analytics, right? So it's not that easy, when we say the real time data analytics, we even want to get it, like, when you plug in from source to, warehouse and from the reporting, right? So some reportings work, the different way. And some reports are working in a different way, right? Compared to the live connection versus the extract connection. So we need to make, smarter decisions, based on the business needs, like when the business is asked for the real time analytics. But, I haven't said, providing real time analytics is not enough. Is the key for any organization to take informed decisions and on the fly, right? Interactive visualizations, like definitely that will gonna, the game changer, like when you provide interactive visualizations, like which means so when I said interactive analysis means, right? So when you click on, maybe, The map, for instance, if you consider the map, the US map having all the states. So when you click on maybe New Jersey or New York, like now you're going to see, same, the state related entire information followed by just one or two click, based on the interaction, how we set up. And then, having that custom reporting framework, this will also helpful, for any organization to set up the template so that, we can follow the same template for across the organization. So these are all the steps will help us to, provide the best analytics platform. Now, transforming operations and automation and collaboration, right? Automated reporting, real time analytics, cross department collaborations, advanced integration hub. And a predictive analytics engine. So this all will help us, to place where we, provide the, all the automation, with the help of all this collaborations, right? So that's when we are going to, provide a complete, And two and a hundred percent automated, the analytics, based on the cost department operations, setting up the real time and I'll take setting up the, automated systems in entire, end to end flow, right? so that will really help us, to provide the transform operations. Now, let's talk about some case studies, right? Outcomes of how that world is successing these stories. So implementing the enterprise data warehousing, right? so yeah, these are the some statistics which I collected, right? example of non profit leveraging AWS, right? Boost fundraising success. So that way, that's when you can, boost your fundraising, right? And improve transparency and accountability with automated reporting. Align strategy goals with the mission impact and achieve 60 percent reduction in data processing time. implement real time cross platform data integration, enhanced data security, and compliance framework, right? So discover how enterprise data warehousing or your evaluation non profit operations, enabling organizations to unlock valuable insights. Boost efficiency and accelerated social impact across, critical sectors. So this is a real, this world example demonstrate the transforming power of, strategic data integration. So each case study tells a story of, innovation, right? Yeah. highlighting how nonprofits have leveraged data to enhance organizational performance, optimizing resource allocation, and create a measurable, lasting, community impact. Through modern ETL process, a cloud based architecture and advanced analytics capabilities. These organizations have transformed their technical infrastructures while maintaining focus on their core missions. Now, let's talk about the building a data driven future for nonprofits. So increase fundraising success. that's what. our end goal at how we can increase our fundraising success, right? And improve transparency and accountability and the strategic alignment impact on scalable data infrastructure, advanced analytics, integration and automated data governance. So it, Increased fundraising success, right? So let's talk about this, how this is going to help. So data driven fundraising strategies enables nonprofits to personalize appeal, identify potential donors, and optimize campaign performance, right? Based on all these steps, we can increase our fundraising success, right? How we can measure the fundraising success. And improve transparency and accountability. EDWs enhance transparency by providing clear and consistency data about donor contribution, program outcomes, and financial performances. And, strategic alignment and impact. data driven insights enable nonprofits to align their program and initiatives with their strategic goals. Maximize their impact and achieving their mission. now, let's talk about scalable data infrastructure, as we discussed it, right? your data infrastructure has to be scalable enough, right? Having 24 by 7 data up and running and you have the latest recovery plan in place, right? So we need to consider the cost of maintain and cost of engine and the performance, right? And advanced analytics integration, right? A built in analytics capabilities enable predictive modeling, mission learning, and the real time reporting for data driven decisions making, automated data governance. It's a robust data governance framework, ensure data quality, security, and compliance with the regulatory requirement across all the system. Now let's start with some of the challenges in, when you're implementing the enterprise data warehousing. the complex system integration requiring specialized expert expertise to connect to multiple data sources. As we discussed at lifecycle, it's not just. inter level people, can, plug and play, right? So we need to understand like, 360 degrees, of the user needs and existing systems, the technologies we use, all these things, time consuming data, creating and transformation to maintain the quality across the system. Organizational assistancy to new data systems, acquiring, training, and change management. We never know, right? let's say today we have four systems in place. Tomorrow we may have one more system which may be onboarded into our organization. So we need to make sure we need to consider the future systems as well. All right. To ongoing commitment to governance, including security updates and compliance. Performance and scalability challenges a data volume grow, right? So when you are building the data warehousing, so your data value may be, one terabyte, right? You never know, that may grow, like maybe, 5 TB or the 10 DB, right? We just need to make sure, at least you plan for the next, five years with a predictive analysis, like how our data is going to be and where we are going to land, right? Based on that, you can build your, the entire, infrastructure, right? the technical capability issues between different platforms and interface, right? When you're building it. When we are dealing with the different platforms. So what are the technical capabilities we may end up, right? So we need to well document and prepare for that, security and privacy risks when handling sensitive data across the jurisdiction, right? This is a very, a key aspect, which we need to consider and making sure in all the security privacy risks, which we are handled and we manage the data, right? bear. Based on the different environments, and use your masking, how you are going to handle the masking the data, right? When you have the, the sensitive data in your warehouse, right? So we need to set up the rules based on the data, making sure we follow the security and the privacy risks, right? Now, next, next steps to implement and since we learned, some of the steps, how. We can implement the enterprise data warehousing. So you need to have the defi, you need to have the clear objective and the right teams and the right technologies and the project plan, right? Yeah. So we're going to talk about those things here. So define clear objectives and success metrics aligned with your nonprofit mission. For my cross functional implementation And this is current data landscape, right? And what is going to be, our, data volume like over the period, based on the existing data, right? We can do some, predictive analytics based on the existing data. So that, at least we know where we're going to land next, five or 10 years. yeah. And, select and configure, an appropriate data solution that fits into your needs and budget. Yeah, means the tools, right? the teams, how they want to work. So based on their needs right now, that's when you need to, we need to focus some of the areas here. So develop comprehensive implementation plan, including timeline, resource and risk management. so this is when, when you showcase to the management, so you're going with, the clear directions and the timeline saying that, Hey, this is what we are, like where we are right now. And when you're implementing the EDW, what we are going to achieve, right? why we need to invest, so much of money into the enterprise data warehousing, right? it's not just not benefit like one thing, right? you need to, Prepare that plan. What are the teams you need? what are the skills that you need? What are the technologies you need? If you don't have already in your, in our organization side, that is a prep work, which we need to, have it before, we present this decision and build secure data pipeline with proper validation and the quality controls. so that's when the secure pipeline and the quality controls, right? Like when you implement like now, when if you wanted to build the historical data, you need to implement SCD2, right? when I say SCD2, it is one of the, the concept where you maintain the current history data, in, you call it as a, we call it as a slowly changing dimension type 2, right? uh, SCV2 that is we call it. Yeah. So we need to make sure, we call it the same and which will maintain the current history. that is one of the key aspects to maintain your enterprise data warehousing. Then launch staff training program to ensure effective system adoption and utilization. So this again, like once we are done, implement the solution. So we need to have the staff training program to ensure, system is adopted and it is usable and everyone is benefiting out of it. Yeah. So when you have all this plan in your place and you can move forward and again, so this is. All the steps in which, which I came across, in my journey, right? You may need to tweak, based on your organization needs and based on your, organization budget, based on your, organization, the data volume, and the technologies and the resources, right? But, primarily, if you follow, the steps which we discussed, definitely you gonna have the, really, a secure, Cleanse, quality, data in your enterprise data warehousing and business will definitely benefit out of, bringing the data from the enterprise data warehousing as a, operational and, analytics reporting system. With that, thank you so much for, joining my talk. I hope, you learned something. hope to see you, in some other time. Thank you all. Thank you so much.