How can Big Data Testing for Pharma Sector Boost Innovation?Cigniti Technologies
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According to a joint study by recruitment consultancy Robert Walters and Jobsite, 47% of recruitment Managers have anticipated increased demand for IT workers in 2017. The findings from the survey of 700 senior technology professionals indicated a rising demand for Business Intelligence (BI) and Big Data professionals.
As per the report estimates, 2017 will see a soaring demand for Big Data and Cyber Security experts/professionals. The reasons are obvious – there is increasing awareness amongst enterprises about the benefits that they can reap from Big Data Analytics tools and skills.
While this is logical and evident from industry trends, the specified survey substantiated this point too!
Relevance and application of Big Data and Analytics is apparent across sectors and domains. Concurrently, critical and life-enhancing industries like pharma and biopharma are making a serious stab at the benefits they could reap from Big Data.
At the same time every sector has also put across its own challenges in Big Data integration.
Why is Big Data an Enabler for Pharma sector?
Pharma is a highly research-oriented and data-driven industry that triggers innovations on the basis of data and analytics. The entire drug development process is conceptualized and executed on the basis of past records, namely, clinical trial data, electronic healthcare records, and medical test results.
Over the years, the volume of data has been increasing dramatically, which has posed the biggest challenge for the sector. While the challenges are evident, understanding the relevance is equally important.
- Big Data sources enable pharma companies to drive research for future R&D activities with effective development and identification of Drug candidates.
- Companies that can successfully manage big data would be able to effectively access data and analyze it to tackle challenges related to complex regulations, drug development timelines, and validity of the existing patents.
- Big Data helps in predictive modelling of drugs by leveraging the existing spread of information related to Clinical and Molecular data.
- From Operational perspective, Big Data helps capture logistical data that can help companies boost their supply chain and related internal processes.
- Data captured electronically can run through various functions – discovery to development, external partners to contract research organizations, etc. that enable real-time analysis and help derive business value.
- Real-time monitoring of trials and related data can help identify expected operational and safety issues and help address unexpected events/delays.
- Reference to R&D, Big Data integration enables pharma/drug development companies to combine real-time evidence with existing data streams and derive valuable outcomes.
- Sifting through large volumes of data is practically impossible without implementing advanced analytical capabilities.
- Big Data can enable faster and logical recruitment of candidates for Clinical Trials, thus enabling shorter and cost-effective trials with more success ratio.
Analyst reports have indicated that Pharmaceutical R&D is facing declining success rates and a static pipeline. Big data and Analytics can go a long way in resolving some impending issues.
The McKinsey Global Institute estimates that comprehensive Big Data strategies can help take informed decisions and generate almost $100 billion value annually for the US healthcare system, drive innovation, boost the quality of research and Clinical Trials, develop improved tools for physicians, and better OTC products for individual consumers.
The benefits of Big Data are especially compelling in complex business environments where there are multiple types and volumes of data available.
What are the core challenges of Big Data Integration in Pharma?
As we talk about challenges, experts claim that Big Data remains an opportunity as well as a challenge for the pharma sector. Industry numbers suggest that about 70% of pharma data projects primarily involve Data Management, which comes prior to any further analysis.
Here are some clear challenges:
- When massive data gets more heterogeneous, cleansing and integrating the data gets further complex.
- Testers are supposed to constantly monitor and validate Volume, Variety, Velocity, and Value of the Data, so understanding the data becomes critical and a real challenge.
- Additionally, analyzing unstructured data requires tremendous technical expertise and understanding of tools.
- Having consistent and credible data is the biggest challenge for R&D in pharma.
- Management of data at all levels of the value chain is critical and enables organizations to derive maximum value.
Big Data Testing entails successfully processing terabytes of data using commodity cluster and other components. Considering that the processing is very fast, it requires a high level of testing skills. It involves three types of testing – Batch, Real-time, and Interactive.
Apart from this, it is important to ensure data quality in Big Data Testing. It is essential to check the quality of data, checking various aspects – conformity, accuracy, duplication, consistency, authenticity, and the all-inclusive nature of the data.
Why consider Test Automation for Big Data Testing?
Test Automation for Big Data testing can ensure that large data sets across various data sources are integrated effectively to provide real-time information. It further certifies that the quality of constant data deployments is maintained and it does not hamper the decision making process.
It aligns data with changing parameters to take predictive actions and helps gain right insights from the most minuscule data set. It helps ensure scalability and data processing across various layers of data and touch-points – structured and unstructured.
With Test Automation for Big Data Testing your enterprise can validate both structured and unstructured data from various source points. This also helps improve the quality of data warehouse that ultimately boosts the quality of data to help drive insight-driven business decisions.
Can Big Data testing improve service delivery?
A practical example can very well testify the benefits that Big Data Testing and an experienced Testing partner bring for you.
A leading biopharmaceutical company with a widespread market across US, UK, and Canada collaborated with Cigniti Technologies to test the Aggregate Spend program. The company needed testing expertise for ETL/DW and BI testing of their Aggspend solution.
The client operates in critical therapeutic areas, namely, deficit hyperactivity disorder, human genetic therapies, gastrointestinal diseases and regenerative medicine. They preferably needed strong domain background in Healthcare and Pharmaceutical industry, and if possible, knowledge about the Physician Sunshine Act.
The Cigniti team faced challenges related to quality of data for some entities. The production version feed files were huge, time taken to process the feed files was high during test cycles, and the challenges got doubled with the client’s inadequately defined requirements.
Considering the complexity of the domain and related technical requirements, Cigniti team proposed the right mix of ETL/DW & BI (Data validation, functional and integration) testing and domain experts, and was ultimately selected as the preferred partner.
With detailed study of the client’s needs, support from Business Analysts, Subject Matter Experts and test managers, Cigniti designed a comprehensive testing approach that ensured timely delivery and compliance with high quality standards.
Are you struggling to achieve faster test cycles, Go-LIVE timelines and zero defect leakage into production?
Connect with our experts to achieve an all-inclusive Test Automation strategy for your Big Data Testing needs that can help you build a robust go-to-market strategy, not just for pharma but for any business.
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