You're tasked with a data warehousing project. How can you ensure success?

From my experience, I think a data warehousing project is different. You can't just define everything it needs at the start. For example, if you build a data warehouse just for sales reports, you'll pick tools that work for that. It'll seem great at first as you get your reports. But soon, you'll find that the company needs more from the warehouse than just sales data. It'll grow quickly, and your initial setup might not handle it. You might even have to rebuild the warehouse. So, I believe it's important to think about the company's future needs and how the warehouse can grow and connect with other systems. Also, keep in mind the costs will increase as it grows.

Data warehousing project involves a combination of strategic planning, effective implementation, and ongoing management. start with Define Clear Objectives - objectives that the data warehousing project aims to achieve, Requirements Analysis, Data Quality Management, Scalable Architecture, Robust ETL, Data Security and Compliance, Reporting, Performance Monitoring and Tuning, Backup and Disaster Recovery Regularly assess the effectiveness of the data warehouse against business goals. Incorporate feedback and lessons learned to continuously improve processes and performance. By following these steps and maintaining a focus on collaboration, adaptability, and ongoing improvement lead to success in a data warehousing project.

In my experience, undertaking a data warehousing project is a complex endeavor that requires careful planning, execution, and ongoing management. Success in such a project hinges on various factors, including clear objectives, robust data management processes, effective technology choices, and strong collaboration with stakeholders. By following a structured approach and adhering to best practices, you can maximize the benefits of data warehousing and achieve desired outcomes.

in my experience, DWH projects required different kind of experience just exposure to OLTP knowledge or RDBMS knowledge is not sufficient. many up-stream, down-stream applications. The huge data you are or will be processing (cleaning, crunching, validating etc) not only for record purpose but use it for future prediction purpose. The success relies on few factors like accurate date, timely data, consumed by many departments to make sure everyone has only one version of the data. DWH projects are heavy infra and extra costs to the company hence, clear objectives and scope must be defined earlier. once you have it then, all engaged in the project will enjoy the journey.

It is safe to assume that business involvement and buy-in is critical but that is generally the last mile of the data journey. To me the first mile of the data and the platform are equally if not more important. Data Contracts, SLAs, Data Governance and a platform that is purpose built with data security and data organization as a first class asset is as important as business involvement. Never assume the people who started with you on the journey will be the only ones. Build a platform that is more extensible then your initial requirements.

Para escolher a arquitetura certa, você tem que olhar os dados e entende-los! é aquele momento onde uma análise exploratória de dados faz toda a diferen?a. Depois de entender bem o dado, ai você pode planejar qual a melhor arquitetura a ser utilizada naquele dados para atender aquela(s) necessidade(s) do negócio.

Based on different type of data sources one need to select the appropriate ETL tool. Decide on data marts depending on requirements and choose conformed dimensions wisely for optimal model. Choose cloud ETL options which can work with heterogeneous sources and can utilize python like languages for different data operations. This can give additional capabilities and flexibility for DWH building and option to choose proper DWH or Datalake based architecture. Some cloud architectures also has sql-like interactivity with datalake objects which can be very useful handling modern data structures. Based on DWH based reporting access and visualization tools and target audience need to put security methods in place. MDM/DQ will make DWH accurate.

In my experience choosing the right tools is one of the challenging aspects, As some companies will stick to using their standard practices and tools throughout all their projects or sometimes due to the limitations from the client side. The ability to freely choose the ideal tools is one of the key factors in ensuring flawless project completion.

I have built a few data platforms, with that experience I could say that identifying the source, understanding and translating the data in the right format / context is the key. Architecturally once needs to consider the data ingestion methods, ensuring data quality and build the right data mart(s) is the key.

At the outset of a Data Warehouse (DWH) project, selecting the appropriate architecture and tools is of paramount importance. This decision is critical because the DWH will be tasked with collecting data from a diverse range of sources, each exhibiting its own unique data quality characteristics. Some sources may provide clean data, while others might necessitate meticulous cleaning at a granular level. The choice of tools is pivotal in determining the overall data quality, subsequently influencing the accuracy and reliability of the analysis conducted downstream. Hence, careful consideration and selection of the right tools and architecture are crucial steps in ensuring the success and effectiveness of a Data Warehouse project.

Em rela??o a este ponto, o que vejo muito por ai é que, até fazem o projeto de ponta a ponta, mas depois de entregue, esquece... e n?o é bem por ai! Tem todo um ciclo de vida! Aten??o a este ponto! "Entrega final n?o é o fim, é apenas o come?o!"

Design the development plan/SOW for short term and long terms objectives of DWH project. Plan SOW to deliver value added KPIs to business with shorter first iteration and keep on adding objectives in subsequent iterations which will help the business. Document the business requirements, assumption and define attainable goals and get sign-off. For deliverables use agile approach and deliver tasks/KPIs regularly. Technically, use of CI/CD approach with version control is important. Reconciliation with source and target data at regular level is must to avoid data issues at later stages. Proper data modeling and structured development plan for each iteration will provide confidence and patience to business stakeholders to value your efforts.

One common mistake people make is to think that Data is an entirely separate area from Software engineering. Computer scientists have been dealing with the development lifecycle for a long time, and we can benefit a lot from there. So always use a code version system like git, and try to keep all your deployments in an easy and accessible way (using Terraform, for example). Spend time ensuring that your CI/CD pipeline works correctly and is maintainable. Next, you need to see if your pipeline is robust and can deal with failures and, most importantly, if your team can debug, discover, and fix errors. So, using a Logging stack to centralize and quickly access all logs from your applications is a must.

The data warehouse project lifecycle is not like SDLC you need to define priority based on the business values and define short-term and long-term goals based on the value-driven approach to define the process so businesses get values quickly and do not wait until the full cycle finishes.

Following the data warehouse development lifecycle is essential for a systematic and successful data warehousing project. This structured approach encompasses phases like initiation, analysis, design, construction, testing, deployment, and maintenance. By adhering to this lifecycle, you ensure the application of best practices, standards, and methodologies specific to data warehousing. This methodology helps in avoiding common pitfalls and errors, ensuring the quality and usability of the data warehouse throughout its lifecycle.

Aqui a palavra é TRANSPARêNCIA e COMUNICA??O! Como comentado, devemos ter um escopo bem definido, cronograma, or?amento e etc... Tudo isso é muito importante e útil, quando se tem TRANSPARêNCIA e COMUNICA??O! Sen?o tiver TRANSPARêNCIA e COMUNICA??O, no meu ver, nada disso tem valor. Deu qualquer gargalo, comunique antes que vire um gargal?o! Avistou um pequeno risco, já comunique antes que vire um grande risco! Reflitam sobre isso!

I usually choose an agile approached based on the book Agile Analytics by Ken Collier. My experience is that when properly using agile, you are able to deliver results quickly. It also gives you the possibility to respond to changes in scope and wishes, should they arise.

O grande desafio de gerenciar um projeto de DW é definitivamente ter um bom plano de comunica??o, pois por ele passa todo o entendimento das etapas, bem como o acompanhamento das atividades dos envolvidos.

Data warehouse projects extend over several years, and retaining personnel can be challenging throughout the process. It is crucial to effectively manage data flow diagrams to prevent schemas from becoming excessively large and unmanageable. Without proper management, the risk of duplicating information increases significantly and becomes multiple queries and multiple flows serving the same purpose. Stay mindful of this aspect in your project.

The architect runs the warehouse project. Period. The PM is only there to keep the architect aware of the budget, scope creep, and the like. They remove barriers, they don't make decisions on what has already been scoped or make hiring decisions. Stay away from a Scrumer-fall approach, it creates expenses and useless over-head that the project never accounted for and removes the fluid nature and fail-fast culture that Agile encourages. I can't stress enough that Scrum Masters and PMs are not in charge of anything except tracking and removing barriers from the team. Architects should meet regularly with sponsors and have a good rapport with their leadership team too. If you delegate that responsibility to a PM, you're in the wrong role.

Enforce strict security measures, including data encryption, secure data transfer, and role-based access control. Ensure compliance with relevant data protection regulations like GDPR, HIPAA, etc. Conduct extensive testing including unit testing, system testing, and performance testing. Implement data validation checks to ensure the accuracy and integrity of the data.

One thing I observed that some teams attempts to fix the data in perimeter of data warehouse only which should be avoided by talking to owner of source systems and fixing issues at source of origination itself.

Get your source data then cleanse. If there is data missing this is easier than amending the data at a later date. The source of the data needs to be correct so if you’re pulling from a programme and the data is wrong, fix the data in the programme then run again and check. If you don’t, whilst warehouse data may be correct the source data will still be wrong leading to future problems

It deped on your audince and what data exposed. In the relational DW can expose row/coloum and dynamic masking techniq to secure the data. Furthermore, use filebase data securing techniq to protect lakehouse file system such as cloud native method and custom libraries and tools.

High data quality and robust security protocols are non-negotiable for a successful data warehouse. In my role, implementing rigorous data validation and cleansing processes was essential. Something I found helpful is establishing strict security measures and access controls from the outset, as they are fundamental in protecting sensitive data and maintaining trust in the data warehouse.

To ensure the success of a data warehousing project and deliver accurate and timely insights for informed decision-making, it is crucial to align the project with business objectives and actively involve domain experts throughout the development. Building the data warehouse based on business requirements, incorporating feedback through working wireframes, and addressing evolving business needs are key strategies. The DWH should prioritize the delivery of required KPIs to enable better decision-making. By establishing a collaborative approach and demonstrating prototypes to gather feedback, the DWH can be fine-tuned to meet specific business needs, ensuring its effectiveness in providing valuable information to drive business decisions.

Key items missed during build phase is "Analytical Contexts". This is beyond standard slice and dice operations. In context sensitive design, Facts, Dimensions and KPI's are structured in a way that during analysis automatic switching happens behind the scenes. For example, scatter plot with time and a measure is different from two measures because time is always forward looking. Similarly, a non-additive measure and additive measure cannot be compared side by side. Capturing all the nuances during requirement gathering phase and embedding into the design will help is ease of user adoption. Once the contexts are

The most important success marker of the data warehouse is its capability to provide precise and accurate KPIs and data points to the decision makers to help them make decisions efficiently. The data warehouse should be able to provide, aptly and timely, all the KPIs stakeholders need to make business decisions. To ensure this, data warehouse should be designed keeping in mind the business domain needs, data output should routinely be verified, all the data checks should be in place to provide accurate data and time performance of query execution and report generation should be routinely monitored.

Provide business value and insights Train end users Deploy customized dashboards and reports Promote adoption and use of derived insights Assess user satisfaction and feedback

Any day to warehouse needs to answer the questions that the users require and a lot flexibility for those users to enhance those requirements as time goes back

Com o entendimento da necessidade e do dados, as ferramentas à utilizar vem de forma natural (por isso conhecer várias ferramentas, lhe dará op??es em rela??o a situa??es e cenários). Destacando um ponto aqui que sempre comento... quanto mais você adiciona "ferramentas" na sua "caixa de ferramentas", mais situa??es poderá atender!

A Data Warehouse is not an end-state but more a framework to collect, process and store data in order to maximize its use to an Enterprise. So it’s best to never harden its design to where changes require massive re-work, resources and testing. A second critical factor to its success is to institute oversight (governance) to ensure data’s completeness, accuracy, timeliness, and the data literacy of those using it.

If you want to ensure success in a data warehousing project my recommendation starts at the moment you get tapped for the project. It’s counter-intuitive, but the first thing I recommend you do is a readiness assessment on the executive sponsor. Make sure the executive sponsor knows what they are getting into and what obstacles will be faced along the way. Illustrate what success will look like (highlighting the value) but disillusion them of unrealistic expectations. Make sure you will have their backing and support all the way through the project, and they are prepared to make the decisions that will be required along the way. If you find them prepared jump in, if not advise them they need someone else.

Ensure your infrastructure and design is scalable. Once users start seeing the benefits of DW and BI, there will be a clamour for more and more…

From my experience, there is almost always a recurring doubt (and problem) in Data & Analytics projects, which is how to be sure that the data we show is correct. Especially in the stressful final phases of projects. I'm not talking about the quality of the data that is transformed in the Data Management process itself. This is solved with unit tests (10 in = 10 out), I mean if those 10 are correct in origin. This is resolved at the start of the project. We must request in advance from the user, a set of data or previous reports in whatever format and make sure that we will have a stakeholder who will put the stamp with the final OK. These details are forgotten at the beginning of projects and at the end, they generate a lot of headaches.