Struggling to build trust with business users for Data Warehousing projects?

Involving the business users from day one in the data modelling phase itself is very helpful. Getting regular feedback from business users and helping them solve the most important problems by integrating the right sources to get a model which can handle complex to simple slicing and dicing is at the required granular level is key.

Data is like the new precious fuel that drives businesses forward. However, in today's business world, much of the data collected is unorganized and often useless. Studies show that only 10-30% of this data is actually usable for analytics, which is disappointing because quality and consistent data are essential for effective analytics and business intelligence. To improve this, the processes for collecting data need to be refined. Just like a mine is only valuable if it yields a lot of useful material, data collection processes must be streamlined to maximize the value of the data. This will definitely increase business confidence over Data Warehouses.

It's key for any DWH project to document every need of people on the floor. This ensures what data needed, how to source and how to represent..

Trust follows the transparency complimented with clarity on the communications, having these maintained diligently will certainly improve the credibility but the this should be supported with technical expertise too...

The key lies in clear communication and demonstrating the value of the project. Start by understanding their needs and concerns, ensuring that the data warehousing solutions are aligned with their business objectives. Transparency throughout the project, from planning to execution, helps in setting realistic expectations. Regular updates and involving them in decision-making processes can foster a sense of ownership and trust. Additionally, showcasing quick wins and delivering tangible results early on can prove the project's worth. Training and support to help them navigate the new system will further solidify their trust, making them more likely to embrace and advocate for the data warehousing project.

I think involving business users early and throughout the process is not just needed but critical. It doesn't matter how robust or great the data warehouse is; users won't use it if they don't trust the data. They need to understand where the data comes from and how it's being handled, especially if they're used to querying the data themselves. Also, using non-technical language is important. Being overly technical creates barriers and frustration for the business. One thing I’ve found helpful is getting users’ approval and sign-off on all business logic. This ensures everyone is on the same page and builds confidence in the final product.

To build trust in a data warehouse project, engage business users from the beginning through regular communication and collaborative planning, ensuring their needs are met, and implement robust data quality management, validation, and governance practices to deliver accurate, reliable information, providing continuous support and training to foster user confidence and satisfaction.

Involving business users throughout the entire process is crucial as it instills confidence and provides them with a sense of oversight. Effective communication is key to achieving shared goals and ensures that the project is validated at each iteration. Striving for out-of-the-box solutions fosters perfection and user satisfaction, reassuring users that the project is in capable hands. Demonstrating the project's value and presenting a comprehensive 360-degree view of its benefits highlights how it adds value to the business.

Design a "Collaborative Architecture Process" that integrates user insights into the warehouse blueprint. Think of it as participatory urban planning - you're co-creating the data landscape. Deploy "User Story Mapping" techniques to translate business narratives into tangible warehouse features.

Envolver os usuários corporativos no design e na implementa??o de um data warehouse é essencial para criar confian?a. Sempre convido os usuários a contribuir com suas ideias durante a fase de planejamento e a fornecer feedback sobre os protótipos. Essa inclus?o garante que o produto final esteja alinhado com seus requisitos e lhes dá um senso de propriedade sobre o projeto.

Building trust with business users for Data Warehousing projects involves clear communication about project goals, timelines, and benefits. Show expertise by delivering accurate, timely data. Involve users early in requirements gathering and seek feedback regularly. Transparency in data sources and transformations builds credibility. Address concerns promptly and demonstrate how data insights can drive business decisions.

Maintaining high data quality is essential for trust. Implement rigorous validation processes to ensure data accuracy, consistency, and timeliness. Educate users about these processes to highlight the emphasis on quality. Demonstrating a commitment to data integrity will boost users' confidence in the data warehouse.

Quality assurance is critical when the project is being delivered, to prove data quality and consistency. It is also required on a day-by-day basis to ensure that data remains consistent and any errors are captured and resolved very quickly, ideally before users notice.

Ja, Data Cleansing ist ein Muss in der Vereinheitlichung und Qualifizierung von Daten. Hier sollten im ELT/ETL prozess die besten Spezialisten eingesetzt werden. Dort sollte auch die doublettenelimierung für die Vermeidung von Auswertungsfehlern eine gro?e Rolle spielen. Und wir wissen ist dies ein eigenes komplexes Thema.

AUTOMATION is the only way to ensure quality in data warehousing. Make sure the data is consistent, complete, correct, clean, and current.

I strongly agree with the fact that using high-flung, overly technical language in communicating warehousing needs or challenges creates a barrier between the business user and the implementing agency. Besides being misunderstood, in my experience such alienation could also lead to users losing interest and sometimes outright refusal to co-operate. The use of simple language and levelling with the end-users fosters an ambience of collaboration, making them feel a part of the solution team thereby yielding better outcomes and long-term connections.

Avoiding technical jargon and using straightforward language to explain validation processes and data integrity measures to business users. For instance, instead of discussing complex algorithms, use simple examples, like comparing data accuracy to ensuring every ingredient in a recipe is measured correctly. Regular briefings on progress and quick resolutions to flagged discrepancies help maintain user interest and support. This approach not only clarifies the importance of data quality but also build trust, showing users the commitment in delivering reliable information.

It's important to establish a direct connection between the technical aspects of the warehouse and the tangible business realities users understand. The ideal data warehouse should mirror the structure and operations of the business itself. This alignment allows for seamless translation of data warehouse functions into relatable business terms. By emphasising this inherent connection, you can demystify the data warehouse, showcasing its value as a strategic tool for achieving business objectives, rather than just a technical repository.

Open and transparent communication is crucial. Your colleague should proactively share project updates, progress reports, and any potential challenges or roadblocks. Using clear and non-technical language when communicating with business users will make the information more accessible and understandable.

Correlacionando o Uso do Data Warehouse com Desempenho e Resultados Caso de Estudo: Um estudo interno mostrou que as equipes que utilizaram o armazém de dados apresentaram um desempenho 30% melhor em rela??o às metas de negócios em compara??o com as equipes que n?o o utilizaram. Métricas de Sucesso: Definir métricas de sucesso, como o ROI (retorno sobre o investimento) ou o payback period, para quantificar o valor do data warehouse e demonstrar sua contribui??o para os resultados da empresa. Ao fornecer exemplos concretos, histórias de sucesso e correlacionando o uso do data warehouse com desempenho e resultados, você pode destacar seu valor e promover maior confian?a entre os usuários de negócios.

To truly showcase the value of a data warehouse, it's essential to go beyond just delivering data and insights. The warehouse should seamlessly integrate with existing business processes, becoming an active participant in workflows. It must also cater to diverse users, from those needing granular details to those seeking high-level overviews, all without sacrificing speed or adaptability. By emphasising these tangible benefits alongside traditional KPIs, ensures the data warehouse remains a valuable, long-term asset for the organisation.

Most often we keep innovation and BAU activity separate but mantra is to apply innovation while doing BAU activity will result in added value to business .

The overall goals are making data available at fingertips and enable data driven decision making. the business users will have to be kept engaged through continual exercise for realizing how the data warehouse is helping them make better decision and also establish how it was missing when data warehouse wasn't completed.

Establishing a clear and verifiable data lineage is essential when building data warehouses. This ensures that every decision made using the data can be traced back to its origins, enabling meaningful and quantifiable business insights that directly support your strategic goals, and add value.

Actively request your users to raise any problems, specially with data quality and consistency, so you can immediately resolve or explain any issues. Make sure to respond swiftly, before it starts impacting the credibility of the data warehouse.

Value comes from trust, and trust comes from increased usage, continuous feedback, and fixing bugs. You'll see the true value when users notice and complain if a report is down or data isn't refreshed, showing their reliance on the system.

Building trust is an ongoing process. Your colleague should provide continuous support and training to business users, ensuring they are comfortable using the data warehouse and maximizing its potential. Being readily available to address questions or concerns will further strengthen the relationship.

Remember, you are building an enterprise data warehouse, which means dimensions are built once and updated later. Explain the bronze, silver, and gold data layers properly to the business and always design with the business user in mind. A house is built brick by brick, and a data warehouse is built column by column. Both require a solid foundation, careful planning, and attention to detail to stand the test of time. As House MD might say -Data is like patients; they both lie. It's our job to find the truth.

Ein DWH ist nur so gut wie es kontinuierlich die unternehmerischen Belange bedienen kann. Es muss somit leicht erweiterbar und wartbar sein. Ggf. Sollte geprüft werden, ob der Data Vault Ansatz von Dan Linstedt der bessere als Kimbal (Dimensionales DWH) oder Inmon (Enterprise DWH) ist.

Many business stakeholders and business user struggle to understand value propositions and many a times, think that there may not enough ROI or cost justifications for the investments made for data warehouse. In such scenarios, it will certainly a good idea to carry out exercise for cost justifications and clarify the apprehensions of business stakeholders & business users.

For any organization, maintaining transparency with business users on how, which and why any particular data set being used across different departments and teams is very very critical. One can use some fantastic tools available like Ab Initio Metadata which helps govern enterprise metadata. It helps in controlling and security of access to metadata, e2e data lineage, data flow and different transformation from the inception. This will give a huge boost in confidence and trust for any business users on data protection and security.

Now a days most of the new Data Warehousing projects aim to replace either an existing legacy DWH (e.g on prem to cloud migration) or some simple reporting solution (e.g OTB reports from ERP/CRM system). The users of the existing solution have trust and comfort on the old system. To gain their trust we need to carry out detailed reconciliation of metrics between old and new world. Scheduling workshops with users to share those recon results, explaining differences (which will always be there), regularly running and sharing of automated recon results will help to gain trust of the users. Along with that providing additional benefits like better SLA, lower failure rates, more detailed data will help in gaining confidence on the new solution.