Modern Data Platforms: Build or Buy Dilemma
Modern Data Platforms: Build or Buy Dilemma https://datazymes.com/wp-content/uploads/2020/12/Modern-Data-Platforms-Build-or-Buy-3.jpg 874 600 Vivek g https://secure.gravatar.com/avatar/927c001dd3a13b6e015a946097c9aa5c?s=96&d=mm&r=gRead about the key factors to consider while making build versus buy decision for modern data management and insight platforms.
In quest of modernization, most Life Science organizations are embarking to adopt new-age cloud technologies, artificial intelligence, and machine learning capabilities, to gain a competitive edge with innovations and to become a data-driven organization. They have an important decision to make – Build or Buy technology platforms to gain technology advancement and strengthen competitive advantage.
Life Science organizations’ business and IT community often debate and discuss build versus buying decisions for modern data management, smart reporting, and advance analytics platforms. While organizations need to constantly evaluate short- and long-term technology objectives, the question of buy-or-build is always complex as building end to end data and insights capabilities in-house is complex project altogether and out-of-box platforms may bring with their own advantages with challenges.
For small and medium life science organizations, building end to end technology capabilities shift business focus from drug innovation and patient wellness to technology and innovation enablement. The in-house technology build projects often go long term with cost impacts and end up not meeting complete business requirements and use cases. While out-of-box platforms provide significant advance technology capabilities, however technology vendors often play with long term operational cost, making black box solutions thus impacting sense of ownership for life science organizations.
Common set of challenges in both approaches build-or-buy:
- In-house technology expertise required for bring innovation without impacting business objectives. The technology with domain expertise is fairly needed to dissect business needs while enabling innovative solutions.
- While data is continuously exploding in structured and un-structured forms, enabling integration among digital applications internally and externally becomes crucial
- Knowledge base, capacity, and continuity to continuously innovate with emerging technologies while keeping cost of ownership under control
- Getting expertise across technology and business subject areas to enable scalable and agile solution
- Support geography, therapeutic class, and brands expansion
Key factors crucial to contemplate while making build versus buy decisions for modern data management and insight platforms:
Conclusion
The point of view shared here helps Life science organizations in making informed, efficient and effective decisions while initiating modernizations of data management and platforms. Business decisions to build or buy data platforms or analytics capabilities are complex and strategic which need to be evaluated on multiple factors. Life science organization needs to take decisions considering business priorities, overall enterprise information strategy, technology-driven competitive advantages, and an acknowledgment that restrictions will evolve over time. Each life science organization faces a different business situation based on product launch phases, clinical trials, and market dynamics. And thus, it’s imperative to assess all the factors to ensure organizations are choosing the right path for enabling data, insights, and analytics capabilities. While a Life science organization may already have many of the components of a software, maintaining the integrations, development roadmap and data quality is not only a tremendous strain on the IT organization, it can prove quite costly. Buying software that fulfills most business use cases is a significantly faster go-to-market strategy where a given use case, depending on complexity, can be delivered within weeks versus the months/years it can take to build one.
