Analytics Playbook - Altimetrik

How to Ensure Your Analytics Investment is a Success?

Analytics provides a powerful edge to the modern enterprise by enhancing decision-making across a vast spectrum of business functions. It compounds the power of people and strategy, to drive intentional innovation.

Take the fast track to correctly embed data and analytics into everyday business decisions.

“Gartner and other industry analysts have found that 75-85% of AI/ML projects fail.”

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What is Big Data? Untangling Data Terminology In Pursuit Of Digital Success

With global spending on big data and business analytics forecasted to increase 10.1% this year, most every enterprise across every industry is gearing up for its own data initiatives

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Analytics Maturity Assessment

Every journey begins with a single step, and the pandemic advanced those steps worldwide for rapid digitalization. The journey of AI/ML too has leap-frogged for organizations across industries towards unlocking tremendous business value.

While every business has a unique set of resources, teams, tech stacks, and priorities, we’ve found that they can be clustered into distinct paths of AI/ML journey which can range from non-existent to advanced & industry-leading practitioner-led models.

This Analytics Maturity Assessment (AMA) model is a quick evaluation for you with checkpoints to examine your company’s AI/ML journey benchmarked with industry peers. This will help you to determine where you are in this journey, assess your organization’s willingness to adapt to accelerate promotion to the next stage, and bring you close to relevant practices that could help your organisation’s AI/ML evolution.

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Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.	Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.	Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.	Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.	Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.	Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.	Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.	Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.	Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.	Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.	Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.	Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indications	Utilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes