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March 11, 2022

Is the Life Sciences Industry Ready for the Rise of the Augmented Consumer?

Rohit Vashisht
Rohit Vashisht
Is the Life Sciences Industry Ready for the Rise of the Augmented Consumer?

Augmented consumer tools are quickly becoming essential for life science teams that want to retain a competitive edge. In 2021, Gartner coined the phrase "augmented consumer" as an evolution of the augmented analytics market trend that aims to place insights directly into the hands of a decision-maker, traditionally thought of as a data "consumer." But switching from legacy BI and analytics systems to a brand new way of doing things isn't always easy. There are important organizational and readiness challenges to consider. So what should life science companies considering the switch look for in an augmented consumer platform? How should they prepare? And finally, how should they roll everything out? All these questions need answering to ensure a smooth transition. 

What life sciences teams have to consider before switching to an augmented consumer platform

Life sciences companies have access to more data than ever before. In theory, all that data should make decision-making faster and easier, but theory doesn't always work in practice. Here's why: too many pharmaceutical companies still rely on traditional analytics systems and analysts to extract critical insights. Advances in these systems tend to be slow and incremental while using them well requires deep technical knowledge. Thus, making the insights hidden in the data hard to access. 

Augmented consumer platforms are picking up speed as legacy analytics systems are slowing down. Unlike legacy systems, they have a low barrier to entry. Because they rely on domain cognition, artificial intelligence, natural language processing and machine learning, accessing insights is quick and effortless: users can interact with the data by talking. 

One of the teams at a top 3 global pharma began to explore the potential of augmented consumer solutions three years ago. Three key factors drove this exploration. 

  • The existing BI platforms had become highly limited.

Initially, our client used the best technology available at the time. But, in recent years, the more the company advanced, the harder it became to get personalized data insights at a large scale. The cost of extracting insights went up while the implementation speed dropped considerably. This insight generation was not agile or competitive enough for the changing analytics marketplace. 

  • Novel technologies had started to mature.

Over the last few years, novel technologies like machine learning and natural language processing began to mature. Voice assistants, search algorithms, and chatbots became better at recognizing speech patterns and picking up on nuances. Various companies started developing solutions that would let users interact with data directly. 

  • The new type of analytics had extraordinary potential.

Augmented consumer platforms were powerful enough to solve the three main issues with business intelligence: usability, speed, and scalability. They could deliver personalized business insights faster and with greater accuracy. When the client's team found a domain-specific platform built for pharma from the ground up, they decided it was time to embrace - and test - this new technology. 

Preparing your team (and your company) for an augmented consumer platformSo what happens after you decide to switch to an augmented consumer platform? You have to prepare your team, company, and data architecture for the switch. And, of course, you have to find the right solution. 

  1. Get your data in shape. Establish a robust data lake, data strategy, and solid security.  
  2. Pick a domain-specific platform. Life Sciences is a unique and complex industry. Any practical solution would need to understand the nuances and terminology straight away to understand the way your team speaks. 
  3. If you have an international team, don't just rely on English. If you're operating in a multilingual environment, choose a solution that allows users to interact with it in their own language, thus, increasing the quality of insights and improving adoption. 
  4. Set your organization up for success. Don't forget to focus on change management. Make sure you place ambassadors at various levels in the organization, provide excellent training and opportunities for everyone involved and keep all relevant teams and leadership in the loop.

How to ensure a smooth rollout 

Rolling out new analytics software isn't an easy task. While the best process will depend on the size of your department, there are three steps you shouldn't forget. 

  • Prepare your team

If you want a successful switch, user adoption is crucial. And to ensure high user adoption, you have to prepare your team. Explain why this switch is being made and highlight how the new solutions will make their lives easier:

  1. Near-instant access to personalized, data-driven insights
  2. Works by using natural language. (No need to memorize queries.)
  3. Works on all the devices they already use
  4. It puts the power of competitive insights at the fingertips
  5. It opens the door to the collective knowledge
  6. Requires almost no training
  • Set relevant benchmarks

Estimate the time level of effort and cost this switch would take. When the client set their benchmarks for this switch to WhizAI, they were pleasantly surprised. Implementing WhizAI was significantly faster and easier than they expected. 

  • Guide the initial user experience

The first couple of times your team uses the new solution are crucial as this will set the tone for the future. Set clear expectations with your team and help create a smooth user experience. 

This pharma giant leveraged many distinct data sources from traditional internal analytics data sets to external market data and sources derived from secondary market research. With lots of data to work with and query, the teams quickly saw the possibilities. 

How implementing an augmented consumer platform brought value to the company 

Obtaining relevant insights becomes more accessible with the right analytics platform. Before using WhizAI, generating insights were the domain of the financial and business analyst community. Reports used to be highly pre-defined, creating a very constrained user experience. 

Today, top management to the field force staff can create their insights without technical experience. The team can get answers to complex questions on the spot. This newly implemented system significantly reduces the time to insights generation. 

Just think about how increased speed to insights, greater accuracy, and a smooth, intuitive user experience can help you and your team compete with global pharma. Want to know how WhizAI might fit within your current ecosystem? Have a product question? Our product experts are here to help.

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