Access to reliable data and tools that enable business end-users to perform in-depth analyses by themselves are crucial elements of any BI environment. Even more so, that is the case for telecom service providers, as they act in a rapidly changing market where competition is fierce.
When our client got in touch with us about two years ago, they wanted us to review their existing BI environment. While that environment enabled them to run different types of customer analyses, it depended too heavily on ad hoc (and error-prone!) scripts written by programming experts. Our client wanted to understand what it would take to transform their existing complex BI environment into a trustworthy future-proof platform. Their main requirements were to restore business end-users trust and empower them to perform in-depth analysis all by themselves.
After a technical and functional audit, we advised our client’s senior management to migrate the existing BI environment. A new data platform set up using a low-code approach (through the Pentaho Data Integration ETL-tool) should deliver solutions faster with minimal hand-coding. At the same time, we emphasized the need for a business glossary. After all, if there is no consensus on what an active customer or an active subscription is, it's tough to interpret the outcome of any analysis – regardless of the BI environment.
We started implementing the new data warehouse with the design and development of three structured data models that covered a significant part of our client's reporting needs. From the very beginning, we collaborated closely on those data models with the business end-users. At the same time, we worked with our client to redesign the Tableau environment and connect it to the new data warehouse. The primary outcome of this intense 6-months collaboration process was a fresh start for the business end-users. They could now perform in-depth analysis by building on the best-practice-based data models we developed.
This point in time marked the beginning of a second 1-year phase. As our client's newly built data team and the end-user community became more proficient and independent in deploying and using the new BI environment, our role as a consultant evolved. We now focused primarily on supporting the development of new structural data models related, for example, to customer lifecycle management or monthly subscription analysis and optimization. With the number of data models ever-increasing, the use and ROI of the new BI environment got a serious boost.
The new BI environment included no less than eight structural data models taking into account current and future requirements. Regardless of the number of new data models, the low-code set-up has had the most significant impact on our client's operations. Whereas the ad-hoc scripts used in the past were error-prone and required specialist programming skills, that is no longer the case with the new data platform. Data models are now well documented, can easily be maintained and shared with the entire business team. Putting business end-users in control of the analyses they need and enabling self-service BI reporting restored their trust in the data environment.
With the arrival of new members on our client’s internal data team during our 1,5-year collaboration process, this team has become increasingly self-reliant in managing the new BI environment. That probably shows best that the new platform is future-proof indeed.