How DayBlink Consulting supported a major technology upgrade with analytic support
Read the full case study here: Technology Upgrade Data Modeling and Financial Forecasting
Introduction
A large US Communications company wanted to pursue a technology upgrade to provide better service and save on costs. Distributed technology would allow our client to reduce OpEx spend. The previous technology was still functioning well with existing product features, so executives needed to ensure that the return of the large undertaking was worth the risk and investment. Our team built and managed a series of models that consolidated data from multiple sources and stakeholders, consistently cleaned and collected the data, and provided executives in the C-Suite the latest financial and operational information about the program.
The executives used this information to better manage priorities, and to budget resources and time. Analytics included multi-year financial projections, BOM reporting, and ad hoc data pulls. DayBlink Consulting built automation into the models to enable quick generation of reports with the most accurate, up-to-date data.
Problem
Executives sought a more informed view of if and how much ROI a technology upgrade would provide.
Executives sought more trustworthy data to better inform if a technology upgrade would deliver high ROI. The client’s operations teams functioned in a siloed environment without market-segmented data. This created inconsistencies in reporting that led to a lack of trust in the financial reporting that was being delivered by the operational teams. While reports were being generated in a reasonable timeframe from the operational teams, confidence levels the reporting accuracy were not high, as projections were being adjusted in a non-transparent way that was not easily explainable. Teams were unable to dynamically adjust, leading to information that was quickly outdated.
Various components of the complex technology upgrade were not being tracked or reported by teams due to a shortage of resources. With the lack of trustworthy reporting, it was difficult for executives to comprehensively understand the impacts of high-level decisions on the potential cost savings of the program. For different areas of the program that executives wanted more financial projections, any new projection requests that were not previously developed required at least one week of lead time for team members to develop the requested reports.
Solution
Our team solved the client’s operational challenges with a dynamic, multi-model approach.
To initially garner executive buy-in to pursue the program, we built a comprehensive model that gathered all components of the technology upgrade, accounting for geographically-distributed technology with various levels of investments. Once executives decided to move forward, we regularly reported updates based on operational teams’ input. We designed a series of models to support ancillary initiatives related to the primary technology upgrade, including a database that greatly increased report generation velocity. Importantly, we gained buy-in with the operational teams so that they would update our team with the latest, most accurate data. Lastly, we built automation so that clients could quickly make projections with the latest data. The automation built into the modeling was a key factor towards success of the program, as executives were able to quickly receive reporting based on different views that would be beneficial for board-level presentations. In addition, our team was able to provide timely, accurate financial estimates to C-Suite level questions with automated reporting so that executives could better understand the financial impacts of program scheduling changes.
In addition to all of these improvements made to reporting, we fostered a culture of continuous process improvement. Key areas to support our process included analyzing data integrity, improving model automation, and ensuring data source alignment with the operational teams within the department. Because we invested in the team’s supporting processes, report generation continued to be streamlined, consistent and timely.
Outcome
Our models enabled decisions leading to efficiency gains of over 25%.
The models supported decisions such as determining that a large customer market should not receive an unprofitable upgrade due to their unique technology requirements. It also provided more insight into where the program was running over budget, which allowed teams with limited time and resources the ability to focus their efforts in analyzing why costs were higher than expected. In designing modular, detailed models that could predict the level of labor, material, and license-related purchases, we also enabled other operations teams to more effectively plan timing of equipment so that they could maximize cost savings. The solution was not only effective for the C-Suite, but detailed and dynamic enough to support other teams within the operations department, who were able to leverage our reporting. We quantified cost avoidance with the program, in addition to cost savings, which was not previously available to executives. We provided real-time updates on segmented market ROI after product roll-outs to validate to executives the pilot program was successful and tie the actualized financial data back to our projections.
Our work in data modeling and financial forecasting directly enabled executives to continue to prioritize the technology upgrade program, influencing them to spread the program over 5 years and empowering their teams with detailed supporting information necessary to understand where program inefficiencies have space for improvement. Because our modeling showed substantial financial impacts, executives could justify a multi-billion dollar investment in better architecture that will provide a stronger customer experience to maintain the company’s innovative position.