Paving the way for self-service BI Reporting

A race against time to build a future-proof data environment


Build a future-proof data environment, and do it fast

A client of ours, a streaming service provider, needed a future-proof data environment that can quickly evolve with changing BI reporting needs as its newly launched streaming service grows. Since we joined our client’s project close to the streaming service launch date, our primary challenge was to deliver the essential BI reporting capabilities at very short notice.

And while that is quite an exciting challenge by itself, the joint ambition was to go far beyond a short-term solution. To pave the way for self-service BI reporting, where business teams can develop dashboards by themselves, we focused on increasing the level of automation and designing a scalable data architecture and environment.


Deploy a multi-skilled team with a true business mindset

To live up to our client’s expectations, we built a strong team of complementary multi-skilled specialists, including data engineers to design the data platform and BI consultants for functional analysis and reporting. But foremost, we made sure to form a team with a true business mindset, a team that can cope with the urgent needs of a fast-evolving business and has the maturity to work together with all stakeholders and align their perspectives.

Our team's first assignment was to take care of the data platform's technical realization and secure our customer's most urgent reporting needs. They set up the AWS environment as the core of the cloud architecture, with an Amazon S3-based data lake solution using Amazon S3 as its primary storage platform for data from all stakeholders. Tableau was selected for data analysis and reporting, providing the initial solution for data processing (Tableau Prep) as well as the platform to enable self-service BI reporting for our client’s end-users (Tableau Online).

Soon after, our BI consultants started designing the BI foundation and set up Tableau Online to publish the most urgently needed BI dashboards. During this process, they collaborated with the operational teams to gather data from different sources. In addition, they worked closely with the key commercial and financial users to set up the primary data models needed for BI reporting in a structured way.


A business-oriented dashboard in time for product launch: check. Extensions: check. Data models for self-service BI reporting: coming up next!

Within one month from the start, our project team accomplished the first deliverable: a business-oriented dashboard providing the reporting capabilities that our client urgently needed to launch their new streaming service. It proved to be quite a challenge to completely and correctly collect all necessary data during the technical implementation. But thanks to our strong focus on achieving a working solution and the powerful possibilities of the AWS stack and Tableau, we managed to complete the required back-end setup and automation – and do this on time.

During the next few weeks, the first dashboard was extended, ad-hoc analyses were made, and the second block of data was enabled for reporting and analysis. With the most urgent reporting needs fully under control, our attention is now shifting to the next steps: building data models to enable self-service BI reporting to follow up on how the company is doing, and developing predictive models for promising use cases.

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