Home / Intelligence / Blog / Transforming Data Operations: Adding Value with an Automated, Scalable Data Foundation
Published January 6, 2025
In today’s fast-paced business environment, the ability to harness data effectively can be a game-changer. Companies that can quickly integrate and analyze data from various sources are better positioned to make informed decisions, optimize operations and stay ahead of the competition.
The complexity of managing diverse data sources, ensuring data quality and maintaining consistent performance metrics can be daunting. These challenges are often exacerbated during periods of rapid growth, such as when a company is acquiring new businesses and integrating their data systems.
How can data operations efficiency challenges be addressed?
There are several areas to focus on:
- An automated, scalable data foundation is essential for organizations looking to streamline data operations and improve efficiency.
- Companies need flexible data systems that can accommodate new data sources without significant alterations.
- Standardizing KPIs across the organization helps create a common language for performance measurement, reducing confusion and enabling more effective communication.
Additionally, implementing tools that facilitate quick and efficient data management can significantly reduce the time and effort required to maintain data systems, allowing teams to focus on deriving insights and driving business value.
Why is an automated, scalable data foundation necessary for a company with diverse data sources?
Fragmented data ecosystems can lead to resource-intensive maintenance of custom data pipelines, inconsistent performance metrics, friction between teams and delays in decision-making.
For any company, integrating new acquisitions can be slow and challenging without a unified data foundation.
An automated, scalable data foundation can enable faster integration of new acquisitions, quickly generating value.
Trinity Case Study
An Automated, Scalable Data Foundation to Generate Value from Newly Acquired Assets in 45 Days
The Situation
A mid-size biopharma company had an ambitious inorganic growth strategy but a fragmented data ecosystem, with each brand and function using its own data universe.
- There were a variety of data sources, models and integrations. Every brand needed data from different vendors depending on therapeutic area, so data models and integration were implemented for each brand. Maintaining these custom pipelines and ensuring data quality became a resource-intensive exercise as the usage of data grew.
- Performance metrics meant different things cross-functionally. Having multiple definitions for the same metric (such as sales or conversions) led to friction between teams, delays in decision-making and the need to find out who had what data.
Trinity’s Solution
Trinity recognized these issues as data operations efficiency challenges and created a data ecosystem that operationalizes data in a scalable manner so data from new acquisitions could be easily integrated.
- Vendor-Agnostic Data Model: Leveraging our deep life sciences domain expertise and data partnerships, a vendor-agnostic common data model was designed that could connect to any data source and automatically trigger the right transformations to ensure that the relevant KPIs were calculated and refreshed. The data model is easily extendible to include new metrics and first party data whenever needed.
- KPI Standardization: Trinity’s data analysts worked with business teams from across the organization to identify important common KPIs, create standard definitions and evangelize them—essentially creating a common language for company communication.
- No-code, Life-Sciences-Specific Tools: Trinity Terra, a comprehensive data management solution, was used to implement the common data model for existing brands. TrinityEDGE™ Foundations tools facilitated no-code data wrangling to quickly create new data flows and add new metrics that powered requirements that weren’t part of the initial design.
Project Outcomes
A single source of truth and common language for performance metrics across brands and functions.
Built-in accelerators for life sciences data resulted in 50% faster implementation of data foundation vs. the client’s initial estimate.
Integration of newly acquired brands within 45 days instead of 6-7 months
(industry average).
Authors: Paramita Kar and Vishnu Prashanth Veerasamy
If you have any questions, we’re here to answer them.
We look forward to helping identify solutions for you.
Technologies and approaches that leverage advanced analytics and AI for driving commercialization strategies will have a significant impact on LSOs. We expect digital life science platforms to be mainstream in the next five to 10 years, enabling them to nimbly adapt their business and operating models in response to external disruption and change in business strategy.
—Gartner®*
In the August 2024 Gartner® Hype Cycle™ for Life Sciences Commercial Operations report, Trinity Life Sciences is recognized as a Sample Vendor in the following sectors:
- AI in Commercial Operations
- Advanced Decision Support for Sales
- Personalization Engines in LS (Life Science)
- D&A (Data & Analytics) Platforms in Commercial LS
*Gartner, Hype Cycle for Life Sciences Commercial Operations, Animesh Gandhi, published 13 August 2024.
GARTNER is a registered trademark and service mark of Gartner and Hype Cycle is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
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An Automated, Scalable Data Foundation to Generate Value from Newly Acquired Assets in 45 Days
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