The big pharma industry is spending over $154B a year to develop new drugs, but are facing an unprecedented decline in R&D productivity.
Drug manufacturer C-suite is hoping to flip this decline upside by leveraging advanced data science capabilities such as machine learning and AI.
The investment in these technologies is being driven by AI’s promise to identify certain patterns in vast data sets.
But what happens when the data you need is hidden in different database silos, or when billions of dollars are riding on drug testing data you can’t access easily?
Research-based pharmaceutical companies need to gain an advantage over their competitors in both reducing the time it takes to bring drugs to market as well as reducing the investment in R&D. In an increasingly competitive global market, it’s becoming more and more crucial for pharma corporations to gain an edge and use advanced analytics when it comes to influencing decision processes backed by data. Additionally, advanced analytics may improve strategic investments and aid in devising approaches to trials and protocols. Using artificial intelligence may allow systematization over the discovery, development, quality assurance as well as the commercial process of pharmaceutical products.
In the world of increasing cost pressures, expiring patents, decreasing margins and changing business models, pharmaceutical companies are beginning evolving their business strategies
to deliver value-based services and drugs to their customers.
Investments in emerging technologies for your Pharmaceutical Institution may also be a point of discussion. Automation and digitization of labs reduces errors and improves data retrieval and analysis, as well as equipment maintenance and even more advanced automated protocols such as sample delivery and preparation.
However, because your institution may need to assess which technology or data solution will be the most useful as well as provide the most value when beginning a master data management project. As a result, we feel that a deliberate and more measured approach can avoid white-elephant IT investments because companies can evaluate each wave of use cases, defining what data, technology, and partnerships will be needed for success.
We provide reliable, timely intelligence and insights to help you every step of the way during drug development.
Our process involves applying data strategy, management consulting, data modeling, and data governance to aid clients in their ability to gain insight into innovating more competently, identifying which data project will allow you to gain the most ROI, to assess the quality of products, and to increase productivity. Our capabilities allow your company to take control of the data, in order to reduce ineffective data allocation, which may result in missing data limiting your ability to develop new drugs, siloed data lingering across departments without structure, or limited data not available to allow your company to make sense because of a lack of large volumes of data needed for experimental analysis. Aculyst helps companies to speed up innovation across various business initiatives including clinical trial optimization, launch planning, digital health, patient engagement, real-world evidence, health outcomes and others.
Pharma companies need:
Data Integration and Interoperability
- Integrating structured semi structured and unstructured healthcare data
- establish a ‘single source of truth’ for (research, clinical, claims, sales, genomics data)
- building business rules for data normalization and aggregating data into an enterprise data repository.
- integrate real-world data from internal and third-party sources – point-of-care systems, electronic medical records, insurance claims, patient-reported outcomes, third-party data providers and more.
- Custom interfaces, adapters and parsers
- Expertise across (CTMS, CDISC, EDC, CCFL, FHIR, QRDA, CCDA, HL7)
Data Management (EDW & Big Data)
- Aggregate large healthcare data – EDW & Big Data
- Expertise in EDW and data modeling
- Use Hadoop for optimal health resource utilization across different patient cohorts, get a holistic view of cost/quality tradeoffs, analysis of treatment pathways, competitive pricing studies, etc
- Expertise in Hadoop, other big data streaming platforms
- Solid frameworks for data quality and data governance
- Healthcare data management platforms
Performance Management (BI/Analytics)
- Provide secure access to interactive analytics reports to multiple stakeholders
- BI clinical observational research use cases like cohort builder, cost comparison, etc.
- Generates insights from real-world data to support decisions on treatment regimens, gaps in care, reimbursement, formulary access or clinical d
Data Science & Machine Learning
- Mine and leverage clinical healthcare data for actionable insights
- Machine learning & AI for advanced business use cases such as minimizing waste Across the drug manufacturing process
- Predictive analysis to optimize clinical research costs & improve patient outcomes
- Natural Language Processing (NLP) for unstructured data for research analytics
- Proficiencies in using data science and machine learning tools such as R, Python, SAS, Tensorflow
- data profiling, model development, reporting and integrating analytics results with enterprise systems
Our Areas of Expertise
- Big data
- Data strategy
- Modern data architecture
- Advanced Analytics
- Data science & ML
- Data governance
- Data Warehouse/ Data Lake Implementations
- Enterprise data management
- Cloud Migration & Cloud Re-engineering
- Business intelligence
- Data visualization
(Hadoop, In Memory Data Grid, Relational)
Data Integration Tools
(Flume, Hive, Impalla, Informatica, Kafka, PigScoop, Spark, Talend)
(AWS, MS Azure, Google Cloud, Private Cloud)
(Aster, Business Objects, Cognos, Excel, MicroStrategy,R, SAS, Spotfire, Tableau, Qlik View)
Types of Data