The headlines today still tell stories of challenges facing pharma.
“Why Eli Lilly Shares Lost 8% in November” The Motley Fool
“Sanofi Points to Lower Profit for 2017” Financial Times
“Struggling Teva eyes thousands of job cuts” FiercePharma
How can these headlines become weighted on the whole to be positive for pharma rather than negative? The answer lies in becoming data-driven – intelligently. It is well known that Teva have been working with IBM Watson to change their fortunes for some time, and yet in 2017 the results of that partnership do not appear to be changing their financial situation.
Big data and AI are on a lot of agendas and everyone is determined to use all their data. According to Gartner, 64% of companies had already invested in big data technologies but less than 8% of these had actually used them successfully. That is bad. But the reason is worse. Many companies simply do not have any idea of what they are doing in this space and turn to tech companies to implement these projects but tech companies are tech companies who do tech. They do not do strategy for the tech. Tech is an enabler. It is not a panacea.
Because of this many companies fail in their big data efforts and do not reap the anticipated financial rewards.
What are the main reasons we see in the rescue projects we take over from big tech in pharma.
1. Not starting with a clear business objective.
Companies get caught up in hype and focus on the how rather than the why all too often
2. Starting by being too ambitious.
By starting with a goal of combining ‘all’ your data, you are in for a long and costly project that may not yield results. Start smaller with specific business goals.
3. Selecting the wrong use cases.
Because most companies go with tech, they start somewhere that tech can do. This may not be where your company is going to get the best business value.
4. Asking the wrong questions.
Doing this optimally means combining deep domain knowledge (the deep understanding of pharma), and data science expertise (machine learning, fuzzy logic, deep learning, etc. alongside strong coding skills). In fact, many people who have a job title of ‘data scientist’ are not real data scientists. A PhD in the right field, and relevant data analysis experience does not necessarily make you a data scientist. You need good data scientists to create and code the best algorithms specifically for your data sets and objectives.
5. Problems unforeseen beyond the technology.
Setting up the tech and analysing the data is one component of a project but other issues sometimes thwart projects also such as a lack of management understanding, silo-ed approaches etc).
6. Inferior communication.
A famous example of such an error was the NASA Mars Climate Orbiter disaster where the $125 million piece of equipment was lost because one group of engineers used metric units and another used imperial units for a key operation. These projects have to be designed as an integral part of the end users daily workflow to be used seamlessly.
7. Avoidance.
Sometimes teams feel the data will tell them something they don’t want to acknowledge and do something about.
A common theme here is people. The tech and data can do a lot, but it is people being intelligent in their use of it that drives results. Ask the right questions, start small, get quick wins, scale and be flexible.
For help in utilizing big data and AI to your advantage, please contact us. www.eularis.com
Topics: Marketing Insights, Business Analytics, Artificial Intelligence, Big Data
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To learn more about how Eularis can help you find the best solutions to the challenges faced by healthcare teams, please drop us a note or email the author at abates@eularis.com.