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What common mistakes should you avoid when developing a BI data storage framework?

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Business intelligence (BI) is the process of transforming data into insights that support decision making and strategic planning. A BI data storage framework is the foundation that enables data integration, analysis, and visualization. However, developing a BI data storage framework is not a trivial task. It requires careful planning, design, and implementation to avoid common mistakes that can compromise the quality, performance, and scalability of your BI solution. In this article, we will discuss some of these mistakes and how to avoid them.

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Abid Paul

Founder & Principal Data Consultant | 17+ Years in Data & Analytics | Modern Data Platforms |Data Architecture…
Frederico Pádua

Estrategista de Negócios com IA | Analista de Dados | Business Intelligence | Especialista em Power BI

1 Mistake 1: Not defining the business requirements

One of the most common mistakes when developing a BI data storage framework is not defining the business requirements clearly and comprehensively. Business requirements are the specifications of what the users and stakeholders need and expect from the BI solution. They include the key performance indicators (KPIs), metrics, dimensions, filters, reports, dashboards, and other features that will help them answer their business questions and achieve their goals. Without defining the business requirements, you risk creating a BI data storage framework that is irrelevant, incomplete, or inaccurate. To avoid this mistake, you should involve the users and stakeholders in the requirement gathering process, document and validate the requirements, and prioritize them based on their value and feasibility.

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Abid Paul

Founder & Principal Data Consultant | 17+ Years in Data & Analytics | Modern Data Platforms |Data Architecture, Strategy & End-to-End Solutions | Leveraging AI & Emerging Technologies for High-Impact Projects & Contracts
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Business requirements are like the project's roadmap detailing exactly what users and stakeholders expect from the BI solution. This includes everything from key performance indicators (KPIs) to metrics, dimensions, filters, reports, and dashboards – basically, all the tools they need to answer their business questions and meet their objectives. If you don't nail down these business requirements, you're setting yourself up for a framework that's off the mark,incomplete or just plain wrong. It's like trying to build a house without blueprints – you might end up with something that doesn't quite fit the bill.

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Frederico Pádua

Estrategista de Negócios com IA | Analista de Dados | Business Intelligence | Especialista em Power BI
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A n?o defini??o dos requisitos de negocios, ja define que o projeto ira encontrar uma infinidade de desafios extras, pois no inicio nao se entendeu o projeto. Elaborar uma estrutura de armazenamento de dados, sem entender quais os tipos de dados e a frequencia de usabilidade daqueles dados é realmente um grande erro. Na cria??o de uma estrutura de dados, é primordial entender os dados, onde é utilizado, qual a frequência de utiliza??o e atualiza??o, dentre outras especificidades, para que a partir deste entendimento, possa iniciar a configura??o desta estrutura.

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2 Mistake 2: Not choosing the right data storage architecture

Another common mistake when developing a BI data storage framework is not choosing the right data storage architecture for your data and analysis needs. Data storage architecture is the way you organize, store, and access your data in the BI solution. There are different types of data storage architectures, such as data warehouses, data marts, data lakes, and data hubs. Each of them has its own advantages and disadvantages, depending on the volume, variety, velocity, and veracity of your data, as well as the complexity, frequency, and latency of your analysis. Choosing the wrong data storage architecture can result in poor performance, high costs, low flexibility, or low reliability of your BI solution. To avoid this mistake, you should evaluate the characteristics and requirements of your data and analysis, and compare the pros and cons of different data storage architectures.

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Abid Paul

Founder & Principal Data Consultant | 17+ Years in Data & Analytics | Modern Data Platforms |Data Architecture, Strategy & End-to-End Solutions | Leveraging AI & Emerging Technologies for High-Impact Projects & Contracts
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Sizing in a data model is crucial for optimizing the performance and storage efficiency of a business intelligence (BI) storage framework. It involves determining the appropriate amount of resources such as memory and storage needed to handle the data efficiently.It helps in allocating resources effectively preventing issues like slow query response times or system crashes due to insufficient capacity.By understanding the data model's size requirements organizations can make informed decisions about hardware and infrastructure investments. This in turn contributes to a scalable and cost-effective BI storage framework that meets the analytical needs of the business.

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3 Mistake 3: Not following the best practices of data modeling

A third common mistake when developing a BI data storage framework is not following the best practices of data modeling. Data modeling is the process of designing the structure and relationships of your data in the BI solution. It involves creating logical and physical models that define the entities, attributes, keys, constraints, and indexes of your data. Data modeling is crucial for ensuring the consistency, integrity, and efficiency of your data and analysis. However, some developers neglect or ignore the best practices of data modeling, such as using standard naming conventions, applying normalization and denormalization techniques, avoiding redundancy and inconsistency, and optimizing performance and scalability. To avoid this mistake, you should follow the best practices of data modeling, use appropriate tools and methodologies, and review and test your models regularly.

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Frederico Pádua

Estrategista de Negócios com IA | Analista de Dados | Business Intelligence | Especialista em Power BI
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Na área de dados, uma dos primeiros ensinamentos s?o as boas praticas. S?o diretrizes já testadas e consolidadas, que garantem um mínimo de organiza??o e fluidez na elabora??o de uma estrutura de armazenamento de dados. A modelagem de dados é uma das principais etapas em um projeto, pois a partir de uma modelagem bem realizada, a confec??o de cálculos, relatórios e insights se tornam mais fluida, auxiliando na otimiza??o dos processos principalmente na utiliza??o de carga dos equipamentos.

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4 Mistake 4: Not implementing the proper data quality and security measures

A fourth common mistake when developing a BI data storage framework is not implementing the proper data quality and security measures. Data quality and security are essential for ensuring the accuracy, completeness, timeliness, and confidentiality of your data and analysis. However, some developers overlook or underestimate the importance of data quality and security, and fail to implement the necessary measures, such as data validation, data cleansing, data governance, data encryption, data backup, data access control, and data auditing. To avoid this mistake, you should implement the proper data quality and security measures, use reliable and secure tools and platforms, and monitor and update your data and security policies regularly.

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5 Mistake 5: Not testing and optimizing the BI data storage framework

A fifth common mistake when developing a BI data storage framework is not testing and optimizing the BI data storage framework. Testing and optimizing the BI data storage framework are vital for ensuring the functionality, performance, and usability of your BI solution. However, some developers skip or rush the testing and optimization phases, and end up with a BI data storage framework that is buggy, slow, or user-unfriendly. To avoid this mistake, you should test and optimize the BI data storage framework, use realistic and representative data and scenarios, identify and fix the errors and bottlenecks, and solicit and incorporate the feedback and suggestions from the users and stakeholders.

Developing a BI data storage framework is a challenging but rewarding endeavor. By avoiding these common mistakes, you can create a BI data storage framework that meets the business requirements, supports the data and analysis needs, and delivers the desired insights and outcomes.

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6 Here’s what else to consider

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What common mistakes should you avoid when developing a BI data storage framework?

1

2

3

4

5

6

1 Mistake 1: Not defining the business requirements

2 Mistake 2: Not choosing the right data storage architecture

3 Mistake 3: Not following the best practices of data modeling

4 Mistake 4: Not implementing the proper data quality and security measures

5 Mistake 5: Not testing and optimizing the BI data storage framework

6 Here’s what else to consider

Information Systems

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What common mistakes should you avoid when developing a BI data storage framework?

1

2

3

4

5

6

1 Mistake 1: Not defining the business requirements

2 Mistake 2: Not choosing the right data storage architecture

3 Mistake 3: Not following the best practices of data modeling

4 Mistake 4: Not implementing the proper data quality and security measures

5 Mistake 5: Not testing and optimizing the BI data storage framework

6 Here’s what else to consider

Information Systems

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