How to Manage an End-to-End BI Project - Guidelines

Hello and Welcome,

?In this article I will talk about how to manage an End-to-End BI solution project which includes creating a DWH, ETL, semantic layer (like Tabular) and reports.

There are ways to approach a project like this, the most common are Waterfall and Agile:

1)?????Waterfall – this is the traditional way, it has distinct phases, such as analysis, design, development, testing, and deployment, that are completed one after another. Waterfall projects require detailed upfront planning and documentation, and do not accommodate changes easily.

2)?????Agile – breaking a project down into smaller and more manageable chunks, called sprints or iterations. Agile teams work collaboratively with stakeholders and customers to deliver value faster and with fewer headaches.

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I prefer to work in an agile way of managing a project, however, it might not be up to you. Agile allows for a product to be seen and felt by the client and they usually provide necessary and critical points which mitigates the risk, brings the client on board and lets them feel a part of the project and be more understanding when critical issues surface.

If your project doesn’t have sprints, you can replace sprints with scheduled meetings alongside the end clients to show POC, development status, get feedback and discuss new features.

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Managing a BI project with DWH, ETL, tabular model and reports is a complex and challenging task, so I will break it down into steps. Here are some general guidelines that might help you start:

1)?Interview end clients, key stakeholders to understand their needs and expectations for the BI project.?You will need to understand the business needs, define the goals of the project and how to expand it after delivery and acceptance by the business.

Make sure to meet with current report users so you know that you improve their workflow and not just add new functionalities. Saving end user\client on work time will dramatically improve the integration of the new system in the business. Clarify the scope, objectives, deliverables, timeline, budget, and success criteria of the project. A good place to start would be with clients reporting needs, so you can better evaluate the direction of development.

This is a critical stage and mistakes here can have devastating problems by the end, so make sure to get this right.

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2)?Choose a suitable data warehouse architecture and platform that can handle the volume and variety of data sources you need to integrate.?Consider factors such as scalability, performance, development time, security, and cost. If the business already has systems in use, consider the pros and cons of adding an additional system to the organization. If the current system is limiting the project’s future scalability, it might be best to add\replace it.

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3)?Design a logical and physical data model that reflects the business logic and rules of your data sources. Follow best practices for data modeling, use a star or snowflake schema, define appropriate dimensions and measures, and apply consistent naming conventions. Best practices help the project avoid issues like scalability, long running times, changes in staff and are more easily quantified for the schedule.

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4)?Developing the design: Develop ETL processes to extract, transform, and load data from various sources into your data warehouse. You should use tools and frameworks that simplify development, automate the ETL and monitor the ETL during and after execution. Use the tools to ensure data quality and integrity by applying validation rules, error handling, and logging mechanisms that will cut down on problem solving time for future issues. This is the foundation of the project so the ETL must be as efficient as possible, especially if the business data collection is growing fast.

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5)?Create a tabular model that provides a semantic layer for your data warehouse. Depending on the tools you use, this is an optional point, but a recommended one.

You should define relationships, hierarchies, measures, KPIs, and perspectives that enable users to analyze data easily and efficiently.

A semantic layer will enable faster and more agile development, as changes in the semantic layer do not affect the underlying data sources and provide an extra security layer for the Data Warehouse. A semantic layer simplifies and harmonizes the data for users, making it easier to understand and use.

However, a semantic layer requires time and effort to build and maintain, especially for complex and dynamic data sources. It may introduce performance issues or bottlenecks, as queries have to go through an additional layer and process runtime.

take into consideration the pros, the cons, if your tools can work well with a semantic layer and of course the cost.

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6)?Design and develop reports and dashboards that present data in a clear and engaging way.

There are many report platforms such as Power BI, Tableau, Qlik, Pyramid Analytics, Excel etc. You can use these tools to create interactive and dynamic reports that support various types of visualizations, filters, slicers, drill-downs, and drill-throughs.?You should design reports that are according to and with the end user while responsive to different devices and screen sizes. Integrate reports with automated systems, like auto-send, to improve the workflow of end users.

When reaching this step, you should now have something to show the client, basic as it might be, but working.

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7)?Test your BI solution thoroughly before deploying it to production.?You should perform unit testing, integration testing, system testing, user acceptance testing, and performance testing to ensure your solution meets the functional and non-functional requirements. If time is short, another alternative would be to deploy the solution for a specific testing group, however internal testing should not be overlooked. This will give better feedback but can open the floodgates and overwhelm the development team with issues, so make sure to prioritize problems to maximize efficiency.

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8)?Deploy your BI solution to production and monitor its performance and usage. You should use tools to manage your data warehouse and semantic layer, to publish and share your reports with end users.

Collect feedback from users and stakeholders to identify any issues or improvement opportunities.

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That’s it, you are done. All you need now is to maintain, find new data to collect and integrate, develop new features, add new tools and scale up as needed. The “easy” part.

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Good Luck,

Itai Shultz

BI Team leader and Project Manager