What are the most common challenges in cleaning and maintaining BI data quality?

As data practitioners, we may rarely have the mandate to push an IT strategy agenda—but good data quality starts with a well-designed structured source system with ample processes and formalized data owners or stewards. Data quality is not in the hands of neither BI developers, analysts or data engineers. However, pushing for better workflows and systems helps tremendously—but it can be an uphill battle.

Common challenges include: Data Inconsistency: Sources often have varying formats and standards. Data Completeness: Missing values or incomplete data records skew analysis. Data Accuracy: Incorrect data so faulty insights. Timeliness: Outdated data make reports irrelevant. Duplication: Redundant data entries. Complexity: BI systems involve complex data from multiple sources. Scalability: Ensuring data quality at scale is challenging. Integration: Combining data sources introducing errors. Compliance: Adhering to data protection regulations. Human Error: Manual data entry or handling. Addressing these challenges requires a combination of automated data cleaning tools, well-defined processes, and data governance.

Formats can be accommodated. I'm a firm believer in not modifying the data. There needs to be data quality rule in place and for data that fails, communication needs to go back to the source system owner(data steward) for remediation.

Siempre sugiero que en vez de implementar ETLs se implementen ELTs. La diferencia es que con los ELTs se extrae y cargan datos en bruto en un datawarehouse y luego se transforman dentro del mismo. En el momento de la transformación se pueden implementar tests lógicos y transformaciones que ayuden a identificar y corregir potenciales problemas en la calidad de los datos.

In general, none of the solutions are the best in all areas On the other hand, each of us can only see some aspects of things, and generally some aspects of the matter remain hidden from our view. So, if we could create a system where we can tell it what we have and what we see and share the relevant documents, and others add their views and documents and at the same time they could comment on our views, opinions and documents. And system could give a weight to each of these opinions and documents, finally we could choose the heaviest solution.

Invalid data directly impacts your business decisions and pose a huge impact on overall business as all these decisions are taken based on KPIs in your report. Inconsistent or missing data may provide a completely opposite insight of the data. Continuous quality checks and data maintenance are necessary for your data.

The zero tolerance for invalid data - both in terms of completeness and accuracy is essential. Don't waste the time and efforts of BI resources to fix the mess created in the source systems in order to make some sense out of a nonsense data.

There are highlighted some key aspects of data quality in Business Intelligence (BI): Data Completeness: This refers to the extent to which data covers all relevant aspects of a business problem or question. Ensuring completeness often involves checking for missing or outdated data, and filling in gaps where necessary. Data Accuracy: This is about ensuring the data reflects the true state of reality. It involves verifying data against reliable sources or benchmarks, and handling any outliers, anomalies, or noise that may distort data analysis. These challenges underline the importance of rigorous data validation processes in BI. It’s a complex task, but essential for reliable, insightful analysis. ??

From my previous projects, have faced challenge in the Data accuracy and completeness. When we look into data completeness we should ensure whether all the necessary information are available. If information is incomplete, it might be unusable. And also Accuracy is a crucial data quality characteristic because inaccurate information can cause significant problems with severe consequences. Because if the data is inaccurate then it will lead to wrong picturization and mislead the users.

Understanding not only "the data itself" but also the business rules that surround them is fundamental for the BI analyst. The fact that a table does not have "empty" or invalid values does not guarantee that these data are correct for use in generating insights. It is necessary to comprehend the data globally, understand how they are generated, their applications, and all the logic behind their existence. In other words, it is necessary to have knowledge "of the business" to which these data belong. Therefore, the knowledge of the BI analyst goes beyond the knowledge of databases, ETL techniques, or dataviz; one must have the ability (and willingness) to learn about the context of the data in which they will work.

El equipo de analistas de BI para cualquier proyecto debe tener un Gantt bien estructurado con fechas de entrega de los diferentes informes que deben presentar, así como las fuentes de información que estos necesitan recolectar y así saber con claridad el tiempo que va a tomar cada uno de estos informes, el cumplir con nuestros clientes es prioridad al momento de entregar información para la toma de decisiones y también es importante que esa información esté a tiempo para ser pulida, estructurada y presentada de manera oportuna.

Data timeliness is crucial in decision-making processes, and in my experience, setting up alerts in Power BI can serve as an effective tool to ensure that KPIs are monitored and actioned on time. These alerts can be configured to notify users when a KPI reaches a specific threshold, enabling immediate action. Integrating these alerts with Power Automate can further enhance their effectiveness by triggering automated workflows, such as sending emails to relevant personnel whenever an alert is activated. This combination ensures that crucial data don't just get reported but also prompt the necessary responses. All of this would only be effective if the underlying data is up to date though.

Less is more in this situation. The main point is that users and managers think it is necessary to collect as much data as possible. And then they will see and analyze what is really important. This force an rework that can cost a lot. And sometimes, a simple comunication between teams can help to define what the C-levels need to take a decision.

Visualizing data "in real-time" is the "dream" of every manager in a BI project. But this is not always truly necessary. There may be challenges that seemingly make displaying data in real-time impractical, but this should not become a "roadblock." If it is genuinely necessary, various strategies and technologies can be applied to enable this update frequency. However, what I always emphasize with my clients is: is this really necessary? And most of the time, it is more of a desire than a necessity. Real-time updating may be a "wish" but not feasible. Nevertheless, the shortest time possible between updates is something that should always be sought.

Um bom profissional em BI, deve ter uma sensibilidade forte para identificar nos discursos dos stakeholders os principais problemas e ter análises precisas. Nem sempre o escopo aparece 100% definido, por essa raz?o, devemos conhecer de maneira profunda e exploratória os principais pontos de controle e alerta dos indicadores da opera??o, para ent?o conseguir priorizar pontos de aten??o e oportunidades. Outra coisa muito importante, é ter o controle das atualiza??es e a periodicidade que ocorrem, para garantir a regularidade da disposi??o recente da informa??o e possíveis erros quanto a padroniza??o de entidades.

There are two crucial aspects of data quality in Business Intelligence (BI): Data Relevance: This refers to how well the data matches the needs and expectations of BI users and stakeholders. Ensuring relevance involves defining and prioritizing key performance indicators (KPIs) and metrics that align with the BI objectives and audience. Data Timeliness: This is about ensuring the data reflects the current or desired situation or period. It involves regularly updating and refreshing the data, and monitoring any changes or trends. These challenges highlight the need for continuous data management and alignment with business objectives in BI. It’s a demanding task, but it’s key to delivering meaningful, actionable insights.

Ensuring data security and privacy is a vital challenge in maintaining BI data quality. Data security involves protecting data from unauthorized access or alterations, while privacy entails respecting the rights of data subjects. BI analysts must adhere to legal frameworks like GDPR or CCPA, which dictate strict guidelines for data handling. Implementing robust security measures such as encryption, authentication, and anonymization is essential to safeguard data integrity. This not only prevents data breaches but also builds trust among stakeholders and customers, ensuring the ethical and responsible use of sensitive information in BI processes.

Para la entidad donde se ejecuta, recomiendo revisar las regulaciones locales como en Peru por ejemplo la ley 29733 que especifica escenarios de uso de datos transfronterisos y a quien comunicar cuando se hace uso de estos.

Hay que considerar que los datos que suelen ser problemáticos para la privacidad, son también datos que no son útiles para procesos analiticos. Por ejemplo, nombres, correos electrónicos, telefonos de contacto, etc., no son utiles para procesos de agregación. En esos casos estas columnas pueden no incluirse en el análisis o no ser extraído de las bases de datos operacionales.

Em minha vis?o, dois pontos s?o de extrema importancia sobre a seguran?a dos dados: Prote??o contra viola??es de dados: Os dados em bi podem conter informa??es confidenciais e valiosas sobre uma organiza??o, seus clientes e funcionários. A falta de seguran?a adequada pode resultar em perda de confian?a dos clientes, danos à reputa??o da empresa e possíveis a??es legais. Outro ponto é conformidade com regulamenta??es. Muitas empresas est?o sujeitas a regulamenta??es rigorosas, como a Lei Geral de Prote??o de Dados (LGPD). Assim é essencial garantir a seguran?a e privacidade dos dados para cumprir essas regulamenta??es e evitar penalidades.

This isn't particular for BI, but data governance and culture are crucial for successful data analytics projects. Even if you have great talents and tools in place, if you don't have data governance and culture, that's the same thing as having a high performing car without a steering wheel. You need to consider how you'll go about data governance and culture up front when starting a BI project.

The culture is crucial. Engaging key business stakeholders with data governance KPIs together with data integrity objectives is key to success. Data should be perceived as a company-wide asstet and can't be considered as a property of the IT/Digital/Data domain alone.

Ensuring data governance and cultivating a data-driven culture are crucial for BI data quality. Data governance requires clear policies on how data is managed, while a data culture emphasizes data-driven decision-making. BI analysts should set consistent data standards and educate users on BI's value. This approach not only improves data quality but also embeds a mindset recognizing data as a key strategic asset.

Lack of a structured data governance framework can lead to unregulated data practices. Define data ownership, create data quality standards, and establish processes for data stewardship.

Regularly measuring and auditing data quality is vital for maintaining information quality in a BI culture. It helps identify issues, prioritize solutions, assess the effectiveness of implemented measures, promote transparency and accountability, and drive continuous improvement.

One thing I have found helpful, is a clear articulation of what accurate data means. Is it accurate data source systems, are these accurate transform processes and how do you measure accuracy? Associating this with why you need accurate data, for instance, to feed into an ML model, to enable human decision making, etc. are also crucial starting points.

Fix the problem at the source or point of data entry instead of relying on data processes further down the pipeline. It’s amazing what a simple conversation can do with an end user or app admin to improve data quality when they understand the impact of the people, processes & technology.

Absolutely, as a BI Engineer, I’d like to add that the process of ensuring data quality is an ongoing one. It’s not just about setting up systems and processes at the start, but also about continuously monitoring and improving them. This involves staying updated with the latest data quality tools and techniques, as well as the evolving business needs and regulatory landscape. Moreover, it’s important to foster a culture of data quality within the organization. This means educating all stakeholders about the importance of data quality, and encouraging them to take ownership of the data they handle. It also means promoting transparency and collaboration, so that data quality issues can be identified and addressed promptly and effectively.

Driving Business Engagement: Data quality issues are symptomatic of root causes such as control gaps in processes, poor adoption/implementation of best practices by the people involved or control/capability gaps in the systems involved. But, partnering with the various teams to address root causes and improve data quality is easier said than done. The key is to uncover use cases which help address pain points for the teams while also resulting in improved data quality. How can one uncover such use cases? Put yourself in the business team's shoes and ask the question: 'What’s in it for me?' Answering this question will help both in unlocking business value and also prioritising data quality and remediation efforts.

Um desafio bastante frequente em análise de dados é a formata??o dos dados. Como lidamos com dados de diferentes fontes, acontece de a formata??o n?o ser a mesma e ao carregar um dado ele vir em um formato diferente do que deveria, por exemplo, era para ser float mas veio como string, ou ele foi carregado e transformado em nota??o cientifica (o que faz um número pequeno parecer enorme). Ent?o antes de analisar os dados é preciso verificar a qualidade e consistência dos mesmos. Sem essa verifica??o sua análise pode ser incorreta ou enviesada.