Organizations struggle to translate proof-of-concept pilots into scalable AI systems. These challenges reflect a lack of mature data architecture: no governed pipelines, no anomaly tracking, no decentralized harmony. As a result, high-potential AI tools underperform or introduce regulatory, clinical, or reputational risks.

The Solution: finding the right Data Engineering talent

A seasoned data-engineering team will use the latest technology to ensure that the quality of data – cornerstone of reliable predictive analytics – is infallible.

At Space Inventive, we build scalable analytics through a five-prong approach:

• Accurate Data Quality : Our data engineers employ sophisticated data cleansing techniques to identify and rectify inconsistencies, errors, and missing values within your data sets. We implement data profiling and validation procedures to guarantee the accuracy and completeness of data.
• Intelligent Data Modelling: Our approach is adaptable, ensuring your data models can evolve alongside your organization. We utilize various data modeling methodologies, including dimensional, relational, and star schema models, to create structures that optimize data storage, retrieval, and analysis efficiency.
• Customized Engineering and Analytics: Our team listens to you to define your goals and develop a customized solution that seamlessly integrates data acquisition, transformation, storage, and analysis. Most importantly, we deliver the data how best you need and when you need.
• Visualize real-time insights: We are fluent in the language that translates insights into benefits and risks. And we ensure a user-friendly visualization technique, so that intel in not lost in translations.
• Robust Data Governance: The final goal is to build trust. And no amount of a smart charts and designed decks will suffice if the data is not governed as per the dynamic regulatory landscape. Our trained compliance team will unload regulatory paper works from your team – and you manage what is most important, health.

Inside Space Inventive: Solving Critical Challenges in Life Sciences

In last 14 years, we solved many emerging technology problems faced by some of the leading Life Sciences industries. Here is a case study on our Intelligent Center:

Business Problem: One of our pharmaceutical clients faced a critical challenge in their clinical trials: manual data collection by designated team members on local systems. This approach exposed their valuable data to the risk of loss due to system crashes. Additionally, cloud storage options, while available, restricted data access to the cloud account holder, hindering collaboration among team members.

Solution Offered: To address our client's data management challenges, we developed Intelligence Center, a cutting-edge solution that streamlines data retrieval, enhances search capabilities, and prioritizes user-friendliness within the clinical trial environment.

Impact: Our pharmaceutical client, using Intelligence Center, received the prestigious Patient Focused Medicines Development (PFMD) "Made With Patients" Award, recognizing Intelligence Center's significant contributions to developing patient-centric medicines, improved clinical trial outcomes, and overall enhanced patient engagement.

The Concluding Recommendation: Hybrid model of in-house CoE and a niche Data Engineering Partner

The ability to scale AI in life sciences is no longer limited by interest or investment, but by a clear talent gap. Most lack critical expertise in areas like LLM operations, agent-based architecture, and production-scale data pipelines.

McKinsey reports that only 6% of life sciences companies have assessed their workforce against GenAI needs. A practical strategy is to establish an in-house Center of Excellence to maintain institutional knowledge and domain alignment, while bringing in a niche data engineering partner to accelerate execution. These partners offer ready-made frameworks, cloud-native skills, and regulatory fluency that help reduce time-to-scale.

This hybrid approach balances control with capability. It allows life sciences companies to build sustainable internal capacity while benefiting from external expertise in areas where the talent gap is most acute.

Frequently Asked Questions:

1. Why can’t AI models scale beyond pilot projects in Life Sciences?
Most AI pilots stall due to underlying data fragmentation, inconsistent metadata, and lack of real-time integration between R&D, clinical, and regulatory systems. Without a governed, interoperable data layer, even the most promising AI use cases fail to reach operational scale.

Solution: Space Inventive’s Intelligent Center provides a centralized, compliant platform that harmonizes structured and unstructured data across trial systems, labs, and cloud environments—accelerating AI deployment and adoption.

2. How do we enable real-time visibility across research, clinical trials, and operations?
Real-time insights require not just dashboards, but data pipelines that unify diverse sources, from EHRs to lab systems to field reports, in a governed, scalable format.

Solution: sharkvision by Space Inventive transforms your data into actionable, real-time intelligence with user-centric dashboards tailored for R&D, pharmacovigilance, and supply chain teams. Read More.

3. How is sharkvision different from other pharmacovigilance dashboards and analytics tools?
Most pharmacovigilance tools focus on static dashboards or single-point visualizations. They lack true integration across trial data, safety signals, regulatory timelines, and investigator feedback. Teams are forced to switch between systems, leading to delays, inconsistencies, and missed insights.

Solution: sharkvision is not just a dashboard – it is built specifically for Life Sciences as a decision-enhancer. It brings together real-time safety data, clinical KPIs, regulatory triggers, and stakeholder workflows into a single, intuitive interface. With customizable visualizations, preconfigured templates, and seamless compliance integration, it offers unmatched visibility and control over pharmacovigilance processes.

4. What’s the best way to improve pharmacovigilance using machine learning?
Early detection of adverse events depends on aggregating diverse patient, trial, and post-market data streams, then applying ML models trained on high-integrity datasets. Most pharma systems aren’t ready for that.

Solution: With embedded compliance, lineage tracking, and analytics-ready outputs, Intelligent Center helps you ingest and structure data for continuous signal detection and smarter pharmacovigilance.

5. Can Space Inventive accelerate data audit and governance along with infrastructure modernization?
Many analytics efforts fail because regulatory data governance is treated as a separate layer.

Solution: Space Inventive's platforms are built with GxP, HIPAA, and GDPR controls embedded at the data layer, enabling audit readiness and smoother regulatory workflows, without slowing down innovation.

Citations

• 1. Realworld data quality: What are the opportunities and challenges?” Scientists are using digital and advanced data analysis to advance innovation. | McKinsey
• 2. World Health Organization, Mental Health and COVID-19: Early Evidence of the Pandemic’s Impact (Geneva: WHO, March 2, 2022).
• 3. Shitalkumar Mahajan, Digital Transformation: Life Science Industry with SAP S/4HANA Cloud Technologies (ResearchGate, December 2023).