A Fortune 500 medical device manufacturer needed improvements to a siloed data management and manual analytic approach. A transformative solution to their reporting, leveraging the AWS Cloud, enabled significant value.
Situation and Business Challenge
The siloed data management and manual analytic practices utilized by a Fortune 500 medical device manufacturer’s Cardiac Rhythm Management (CRM) division were not keeping pace with the speed of business. A high degree of compliance and regulatory data complexities, coupled with a centralized enterprise IT structure, made for lengthy turnaround times for analytic requests, difficulty with accessing and using their data and being compounded by the overall manually intensive nature of their reporting processes. Additionally, the data output was duplicative, inaccurate, and inconsistent.
These challenges and more contributed to a reduced ability for business users to make decisions quickly and trust the quality and accuracy of their data, which greatly limited their need for enhanced analytic insights and self-service capabilities. The CRM division is a highly dynamic and high-performing division that delivers significant value to not only its customers but drives a substantial portion of the revenue for the entire enterprise. They recognized their current situation and knew they needed to shift to meet market demands, increase efficiencies, drive down costs and improve analytic decision-making capabilities.
Although they were highly knowledgeable about their business, their customers, and their impact on the organization, they lacked the internal technical knowledge to design and implement a solution that could meet their needs. Additionally, being such a large organization, there were many competing internal enterprise initiatives that were consuming IT capacity, preventing them from supporting this type of analytic solution. The CRM division needed to search externally for a strategic, efficient and effective data and analytics partner.
Based not only on AIM Consulting’s reputation in the market, but a successful record of delivering strategic analytic solutions, the CRM division discussed, aligned, and agreed to initiate a strategic partnership from design to deployment. AIM and the client immediately jumped into action, starting a current-state assessment coupled with an ‘art of the possible’ ideation business roadshow to evangelize and empower end-users on the possible capabilities and opportunities. The overall scope of the discussions ranged from daily tactical business processes and reporting needs to opportunities leveraging predictive and prescriptive analytics. AIM and the CRM team collaborated with multiple groups within the division to incorporate their specific strategic needs and align those with the overall division and enterprise strategic vision. At the end of this phase, AIM delivered a highly robust, effective, and scalable modern data architecture strategy and implementation roadmap that fulfilled the core needs of the CRM division while aligning with the vision of the enterprise.
The ideation sessions and proposed strategy informed the company’s CRM team of possibilities for a more robust and scalable solution beyond their immediate reporting needs. AIM presented a future state that was administratively light, highly cost-effective, and scalable. They also recommended an implementation roadmap to remove risks and avoid slipping back to the previous state where processes and solutions were costly, inefficient, and unreliable. The CRM team chose to partner with AIM because of the robust and scalable solution, AIM’s experience implementing modern architectures and their overall reputation for delivering high-quality, cost-effective solutions at an enterprise scale.
AIM delivered a highly robust, effective, and scalable modern data architecture strategy and implementation roadmap that fulfilled the core needs of the CRM division while aligning with the vision of the enterprise.
AIM’s Approach
AIM successfully architected and deployed a scalable and modern data architecture coupled with end-to-end business ideation, intake, and prioritization processes. Leveraging AIM’s proprietary analytics methodology along with a modern data architecture strategy offering, AIM architected and deployed a consolidated data lakehouse on the AWS Cloud. The platform included Extract, Load, and Transform (ELT) automation that integrates with the client’s existing IT infrastructure. In addition to implementing this technical solution, AIM played a critical role in designing and deploying operational change management processes.
The overall value proposition for this leading-edge platform is the CRM team can now achieve self-service analytics services efficiently and accurately, without time-consuming IT processes, allowing them to make decisions and deliver for their customers more quickly.
The end-to-end program, called the “Data Lab,” accomplished the following:
- A reduction in manual report creation and data duplication
- An increase in data quality, transparency, and availability
- A well-defined and transparent process for business units to achieve their analytic goals
- Automated data ingestion to a consolidated data lakehouse for greater ease of use and exploration
- Ideation, intake, and prioritization processes to support capital planning and strategic solution development
Key Results
Before AIM created the Data Lab solution, the CRM division stored its data in Cloudera, SAP, MES, and 13 other source systems and files. The Data Lab allowed the client to legacy eight of these sources and consolidated the remaining into a singular repository.
The AIM solution was so successful that it is currently being replicated and integrated into the overall enterprise digital transformation vision, with AIM alongside them on their journey, collaborating and empowering for continued success in the future.
As a result, the CRM team, which is responsible for $2B in annual revenue (18% of total yearly revenue), was able to:
- Reduce source systems by 50%
- Reduce licensing fees by $1.5M
- Save a total of 3,328 hours of data aggregation and prep time
- Reduce redundant efforts by 23%
- Increase cross-functional collaboration by 18%
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