Product innovation is a risky business. The majority of new products that enter the market fail, and the research required to adapt products to improve sales and provide the most value is costly and time consuming. In response, companies are turning to big data platforms like Hadoop to help provide faster insights in order to shorten time to market, improve product adoption, reduce costs, and create innovative after-sales service offerings.
Big data has the potential to influence multiple sectors, but certain sectors, such as healthcare and manufacturing, seem particularly well-suited to take advantage of big data to improve innovation.
- Shorter time to market: Research and development of new drugs takes years of research and millions of dollars. However, a 2011 report from McKinsey & Company suggested that predictive modelling using big data can cut three to five years off the approximately 13 years it takes to bring a new drug to market.
Predictive modelling can access all of the data available from other research projects and medical findings to better predict the most efficient and cost effective allocation of R&D resources. The data can also help predict clinical outcomes as quickly as possible, such as product safety, potential side effects and even overall trial outcomes. Finally, when a drug reaches clinical trial, developers would be able to design the trial and find the best candidates faster by mining patient data. These insights make it possible for development companies to bring new drugs to the market faster with a higher therapeutic success rate.
- Greater personalised healthcare: Big Data can also play a big part in the push for personalised healthcare. With access to extensive patient data and medical research, doctors would be better able to detect and diagnose diseases in their early stages, assign more effective therapies based on a patient’s genetic makeup, and adjust drug doses to minimise side effects and improve effectiveness.
- Improved decision-making: Manufacturers can also benefit by analysing large and varied data sets. With access to unstructured data, such as Google searches and social content, manufacturers can more easily hone in on which product features provide the most value to the consumer and which features can be eliminated. This ability can dramatically reduce production costs as resources won’t be spent on unneeded features or on multiple test prototypes. These product insights will increase profits as customers will ultimately purchase the product that offers the most value.
An example mentioned in the 2011 McKinsey report states that a “manufacturer of telecom equipment used customer insights data to improve gross margin by 30 percent in 24 months, eliminating unnecessary costly features and adding those that had higher value to the customer and for which the customer was willing to pay a higher price.”
Big data can also improve open innovation by helping manufacturers to extract the most valuable ideas from open forums quickly, without having to sift through hundreds of ideas one by one. This is particularly attractive for companies with limited resources, as big data lets them leverage open forums to reduce the cost of research and development.
Improved equipment service: Additionally, manufacturers can offer better repair services through big data. Manufacturers of complex equipment such as aircraft and elevators have started putting sensors in their machinery to alert them of needed repairs, and by analysing all the sensor data it allows them to address service issues before the customer notices a problem.
Faster time to market, reduced production costs, and improved service are just some of the ways companies are leveraging big data analytics to drive innovation. More and more companies are putting big data analysis at the centre of product development, and they’re seeing improvements across the board, from product quality to customer satisfaction.
Michele Nemschoff is vice president of corporate marketing at MapR Technologies.