You’re facing common challenges in Data Warehousing. What’s the best way to overcome them?

In my experience, Data warehouse data quality initiatives almost always fail because no-one wants / claims real ownership. However hard it is, imho the only way to scale data quality is to 'push it down' to the owners / developers of the source systems OR teach people to use source systems in a better way.

Aqui algunas ideas con las que suelo trabajar. * Si la falta de calidad de datos viene por que los sistemas de origen no recogen la información con la suficiente calidad, promueva estrategias de solución en el origen. No parcheemos después la información, porque jamás seremos capaces de arreglar todo. La calidad es un proceso transversal y afecta a todos los proceso. * Si la importancia de los datos es grande, compruebe que no hayan servicios que puedan ayudar a arreglarlos. Hay servicios que por ejemplo arreglan direcciones, nombre de empresa y ese tipo de información desde bases de datos globales, uselos. Nombre responsables por entidades o grupos de negocio y cree iniciativas que los arreglen.

The biggest challenge in DWH is data quality and it gets worse, as no one is willing to take the ownership of correcting it. The best way to overcome DQ issues is to get the perspective about data from the source system and perform thorough data profiling. Data profiling plays a key role in minimizing the impact of data in the downstream. Also understand the requirement from the PL teams to help them build the derived fields from the kind of data you are receiving.

Data quality challenges in business warehousing can be overcome by stringently following below mentioned countermeasures 1. Establishing data governance policies 2. Implementing data validation checks 3. Conducting regular audits 4. Investing in data cleansing tools to ensure accuracy and consistency. Most importantly, regularly training and involving business stakeholders in maintaining data quality standards.

To overcome data quality challenges in data warehousing: Profiling: Understand data sources. Standardization: Define formats and rules. Cleansing: Automate cleaning processes. Governance: Establish policies and oversight. Monitoring: Regularly check data quality. Training: Educate users on best practices. Feedback: Implement user reporting mechanisms. By implementing these strategies, organization's can significantly enhance the quality of their data in data warehousing systems.

Data security is a critical area of consideration. In the current age of Cloud Modernization and GenAI initiatives, it becomes quite a critical aspect for compliance. This would include authentication, authorization, access management (RBAC/ABAC), handling of PII data elements through anonymization and audits/monitoring to trigger alerts. Security will need to be implemented at all levels including network, Infra, data and a Governance framework to cater to policies.

Protecting sensitive data from unauthorized access is paramount. Implement robust access controls, data masking, and encryption. Regular audits can also ensure security measures are effective.

There are also teams who need access to unhashed customer data, like the customer support team. In this case, limiting the amount of sensitive data shared and the number of people who can access such sensitive data goes a long way in ensuring some sort of data security. Also, schema level access control and BI tool level permissions can be used to effectively control the amount of people who have access to such sensitive data, where there is no other workaround.

Overcoming data security challenges in data warehousing requires a multi-faceted approach: Access Control: Implement role-based access controls. Encryption: Encrypt data at rest and in transit. Authentication: Use strong authentication mechanisms. Monitoring: Monitor access and detect anomalies. Regular Auditing: Conduct regular security audits. Data Masking: Mask sensitive data for non-production environments. Employee Training: Educate employees on security protocols. By adopting these measures, organizations can enhance the security of their data warehousing environments and mitigate the risk of data breaches and unauthorized access.

The modular data platforms are very robust in terms of security as they enable multiple layer of firewall to restrict unauthorised access to the system. Managing data security in a data warehouse is critical to safeguarding sensitive information and ensuring compliance with regulations. 1. Avoid providing user level access instead use role based access. It also helps in proper access monitoring. 2. Restrict unauthorised access using MFA. 3.Create data policies & aware the users to adhere to the same. 4. Use updated software from authorised vendors.

It's important to understand the nature of data and its need while developing ETL pipelines and integrating the data, so as to successfully interpret the requirements and how those can be fulfilled. This may include data modeling, data profiling and data quality, so that any inconsistency or discrepancy can be identified beforehand, such as duplication. It's also critical to understand the source and target system, so that incoming data is extracted accurately and the processed data can be integrated accurately.

El el contexto de un entorno tipo Lake House: * La integración de datos puede ser un problema exclusivamente técnico, Tipos de datos inconsistentes, páginas de códigos diferentes e incompatibles, información dificil de deduplicar. Sin embargo se pueden usar técnicas de lógica difusa e incluso inteligencia artificial en los casos mas extremos para de-duplicar y convertir en información consistente cada uno de los datos. * La integración puede ser un híbrido entre complejidad técnica y definición de negocio, por ejemplo un mismo número de carnet de conducir,que aparece en dos sistemas con dos nombres de persona distintos. Técnicamente podemos sobre-escribir uno al azar, desde el punto de vista de negocio la solución puede ser otra

In my experience, in addition to other comments below , the important is how to integrate heterogeneous distributed data sources and querying on that. Define your connection strings, build data pipeline strategy for each source type, ensure data transformation happens to a 100% before loading and automate the pipelines. Whether you use any platform AWS, Azure/Microsoft, GCP or Open Source platform, accessing heterogeneous data format through tools is easy and no complexity involved A federated data platform is required for connecting various heterogeneous data sources and writing distributed query on the integration layer. PrestoDB, MariaDB, Snowflake are the best tools

Implement Master Data Management practices to ensure consistency and accuracy in key data entities across the organization. Ensure scalability and flexibility in the framework to accommodate evolving data integration requirements. Implement validation rules to maintain data consistency during integration.

Ensure your data integration processing is nondestructive in analytical data sets. You never know when users will need to get back to the original data -- also, use cases vary greatly and change over time. In general, focus on data integration techniques rather than tools, especially at the design or prototyping stage. Integration tools are helpful, but they can also limit use cases to their own specialized focus. In practice, many data engineers or integration developers address challenges like these with a lot of code and a limited set of tools. That's not for everyone, of course, but a lot of example code and architectural advice is available from people who have solved similar integration problems already.

In my experience, using modern cloud warehouses would be wise choices to address data scalability problems because the storage & compute are separate. You can increase either one based on the need and pay as you use… It is good idea to have different kids of compute for producer ( ETL / Data ingestion pipeline ) with appropriate size to meet SLA and consumer ( for read - BI apps, users , online queries etc , Data as service apis ) …. For storage, nowadays cloud platforms offers more space with less cost

No solamente es una cuestión de recursos, sino también de conocimiento profundo en las herramientas que usamos, un Query SQL escrito por un profano o por un experto puede ser varios ordenes de magnitud mas lento. Lo que quiero decir es que la escalabilidad es una cuestión transversal, si el proyecto ha de ser muy escalable, hay que definir cuales son esos parámetros de escalabilidad y en que contexto se va a evaluar. * Contexto de creación de lakehouse, o entornos en tiempo real o semi real. * Contexto de uso de los recursos, en el que influirán usuarios concurrentes, nivel de complejidad de las consultas, hardware y software disponibles, métodos de almacenamiento etc. Todo en conjunto ha de planificado y tenido en mente en cada paso.

It's a new scenario, data is increasing faster than in the past and we have to find resources in less time than 1year ago ...... Agile, faster and faster ........... SaaS or PaaS cloud services are the future

It is important to have proper logical data modelling and improve the physical design (indexing, partitioning) in order to solve scaling issues. Cloud distributed data warehouses are essential for scalability. Additionally, it is also important to work with the business users to understand their query patterns and data needs.

Cloud platforms offer the flexibility to scale both storage and compute resources based on your evolving data requirements. Enable auto-scaling features that automatically increase or decrease resources in response to demand.

Data governance isn't merely a step; it encompasses the entire data lifecycle, crucially determining what and how data is stored in compliance with regulations. It ensures accuracy, consistency, and compliance for informed decision-making, risk management, and cost optimization in data warehouses. This holistic approach includes managing data quality, regulatory compliance, risk mitigation, integration, and cost optimization.

In my experience the most difficult is to make sure everybody understands the same when talking about data. Different parts of the organization have different understandings on things mean. And once people are aligned on that, you can start making sure that what your definitions correspond to what you actually put in your systems.

Contrary to common belief, I think you CAN start small the data governance initiative. Take a part of business domain and people that can understand the value of implementing data governance, and do a PoC. At a minimum, start with Data Ownership and Data Contracts within the chosen domain. Promote results (benefits) and look for a sponsorship for another case or a program. Than go for a DG framework, promote and evangelize. It'll turn into a successful story!

Can utilize data cataloging tools to create a searchable repository of data assets within the warehouse. This catalog should include metadata, data dictionaries, and annotations that describe the data’s source, structure, and usage guidelines, making it easier for users to find and understand data.

Este es el desafío más grande y en mi experiencia el primer paso en cualquier estrategia de datos. Si no se asigna un “propietario” y stakeholder de una dato, en el futuro con los rápidos cambios en la tecnología ese dato será inútil. La información depende de que el dato sea preservado desde el principio hasta el fin

Implementing a good data catalog with a good level of mining intelligence can make the user's life a lot easier, however it is extremely important to disseminate the data driven culture, accompanied by training and appropriate tools so that the user can extract the maximum possible from the data. It is important that especially people at the highest hierarchical level are engaged with this initiative.

Challenges in data warehousing include ensuring data quality, integrating diverse data sources, optimizing performance, designing user-friendly interfaces, providing training and support, ensuring security and compliance, and addressing scalability needs. Overcoming these challenges requires a holistic approach involving data governance, technological infrastructure, user engagement, and continuous improvement processes.

Data Usability is perhaps one of the most important aspects of ten dimensions of data quality, along with elements like accuracy, validity, completeness, and consistency. In simple terms, data usability refers to the ease with which data can be accessed, understood, and used to inform business decisions. Hence, we need to design Data Warehouse keeping end users and his convenience in mind.

In a data warehousing solution, the planning for data usability starts in the planning phase itself. A thought through analysis is required to understand the needs of the end users and identify the right source systems which can flow in that data. Data modeling is also a key part of this strategy to ensure data access is most optimal for the end users

The primary data usability challenge in Data Warehouses from a business perspective is ensuring that the stored data is understandable, relevant, and easily accessible for decision-makers, enabling them to derive actionable insights efficiently and effectively.

Other considerations will be around FinOps and its Governance throughout the SDLC lifecycle and having in place it's KPIs, alerts and a mechanism of continuous improvement.

En mi opinión el principal aspecto a tener en cuenta es que la naturaleza de los proyectos de datos, en si, es estratégica y como tal se podría decir que tienen inició pero no un fin, la mayoría de los dirigentes de las empresas les cuesta entender eso y creen que son proyectos de desarrollo de software, esto hace que no le asignen los recursos necesarios y adecuados para que la estrategia de datos de la empresa cumpla con su objetivo.

Before you start anything in data , define your goals and more importantly understand the types of data and their relationships from source until their use. Build or choose a model first . Don’t over engineer or for that matter don’t over simplify by ignoring key things about data lifecycle . Understand key elements of data like their quality metrics and define a data governance around them by measuring quality and implementing improvements. Make sure your data pipelines are efficient and effective with all the dependencies in place , at the same time they must be traceable for their impact land potentially ability to reverse their unintended impacts if any. Choose data solutions that have ability to perform data lineage metadata harvesting

The most common issue is people not telling the truth. Or even part of it. In this case I suggest a large kinetic motivator.

We also need to consider the wider organisation ecosystem. Data challenges are often a symptom of wider issues impacting a business and its processes and systems. While some issues can be addressed - or dare I say, masked - at the data warehouse level, solving these issues in isolation won't provide the transformational value that businesses need in order to compete. Data flows into the data warehouse from all business units, often from fragmented, and sometimes redundant systems. To be truly transformational, the data function needs to close the loop between tech & business, and help driving changes in the operational systems. The data professional is well placed to act as a facilitator, and support conversations and arguments with data.