How can you ensure your BI project meets industry standards?

"If I would have 8 hours to work, I would spend 6 hours sharpening the Axe" This is exactly how we used to work while I was providing BI Consulting to Fortune 500 clients. Completing the BI project wasn't an issue as much was completing a BI project as per Client's expectations, requirement, timelines and budget. Therefore, we used to spend first few days in finalizing the scope of the project, understanding the data sources, data variables, creating a wireframe and taking Client's approval so that things are very clear what we want to achieve at the end of the project

Don't build on quicksand ! It's crucial to focus on building a strong foundation for your solution by dedicating adequate time and effort to developing a robust data model. Following industry standard design principles such as Kimball Dimension Modelling can guide you in this process. You need to create a data model that is fast, understandable, and extensible, ensuring it can handle quick information retrieval, is easy for both developers and end-users to query and understand, and can scale to new data and reporting requirements. By prioritising these aspects, your BI project will be better equipped to provide meaningful insights and support business understanding, aligning with industry standards and avoiding common pitfalls.

Ensure your BI project meets industry standards by: Defining clear project objectives. Identifying relevant standards and regulations (e.g., GDPR, HIPAA). Adhering to data quality and security guidelines. Regularly auditing and validating data sources. Employing experienced BI professionals. Conducting testing and quality assurance. Providing ongoing training and staying updated with industry best practices.

One key input is to deeply vet your team, whether in-house or 3rd party. There are many players in the BI/Analytics space, and it is easy to make a slick first impression. Be critical. Dig into technical expertise (is this team fluent in your desired stack?), industry expertise (is this team experienced in the nuances of your industry data?), and how your partner will empower all relevant stakeholders.

For example, a new metric may involve a 1 second response time instead of 10 seconds. That requires a whole organization portfolio of programs.

A beautiful & strong house is always build on a strong foundation. Foundation is some work that isn't visible, takes lot of time to construct but definitely impacts the life of the house. Similarly a BI project is developed over a sustainable, trustworthy, collaborative and optimized data sources. Once you put in efforts in setting up an optimized data sources, your Dashboards will work much effectively which is finally what is expected Make sure to test: - Speed of the Dashboards - Data Accuracy - Restricted Excess to only relevant stakeholders - Clearly defined Data Definitions - Data Governance - Providing Walkthroughs to the stakeholders

Define objectives and scope. Assess data sources and quality. Plan data extraction and transformation. Design the data warehouse or data model. Develop ETL processes and reports. Test thoroughly for accuracy. Implement and monitor performance. Ensure compliance with industry standards and regulations. Provide user training and ongoing support. Continuously evaluate and improve the BI solution.

A very critical step is Data Governance as you don't want your Data to be accessible to all - Encrypt the critical data mainly the PII (Personal Identifiable Information) like Customer contact details, email id, Social Security Number, Aadhar card etc - Have restricted access to such information - Have correct data stored and manage the accuracy

To implement robust data governance and security in your BI project: Define data ownership and stewardship. Establish data access controls. Encrypt sensitive data. Monitor and audit data access. Enforce data retention policies. Educate employees on data security. Comply with relevant regulations. Implement data governance tools. Continuously assess and enhance data security measures. Foster a culture of data responsibility and accountability.

Engaging users and stakeholders is vital for a successful BI project. To achieve this, regularly communicate with them through surveys, interviews, and workshops to understand their needs. Create user personas, stories, and prototypes to align the project with their expectations. Keep them informed of the project's progress, offer training and support, and encourage their feedback and reviews. This engagement enhances user adoption and satisfaction, ensuring that the BI project meets their requirements and ultimately succeeds.

Leveraging industry standards and best practices is crucial for a successful BI project. Follow data warehouse design principles like dimensional modeling and star schemas. Utilize reputable BI tools and platforms such as Microsoft Power BI or Tableau. Adhere to data visualization and reporting standards like IBCS or Data Visualization Checklist. Join BI associations and networks to stay updated and connected. This ensures your BI project is credible, reliable, and compatible with industry norms, ultimately enhancing its effectiveness and value.