Data Monetization Strategy for Life Sciences Companies

If you are reading this then there is an 83% chance that you have come across the phrase "data is the new gold" and a 97% chance that you have digitally interfaced with a system that has run analytics on your preferences to provide you with a customized, personalized user experience.

So, what happened? When did the world suddenly become so nice and involved in your life to offer you your choices without even asking? Well, it turns out the world is still the same and all it's interested in is the new gold aka your data.

Numerous industry case studies prove beyond any doubt that harnessing relevant insights from data has enabled better decision making, provided personalized customer experiences, and helped organizations attain a competitive edge in the market. However, the objective of this article is not to highlight the above advantages but to help organizations encash their data for a new & sustainable revenue stream.

Life Sciences (LS) companies generate volumes of monetizable data from various stages in their value chain. There are multiple data sources such as Scientific data (from Instruments, experiments, etc.), Asset Management data (from consumables, spare parts, service schedules, etc.), Enterprise data, and so on. Since it’s a highly regulated industry and federal laws dictate that PII (Personally identifiable information) and PHI (Protected Health Information) data is handled securely it’s prudent that we restrict ourselves to only those datasets that pass the regulatory compliance filters such as HIPAA, GDPR & CCPA to begin with.

While it is established today, that a lot of companies are taking advantage of advanced analytics & machine learning models to monetize data, however, there are still many who are clueless about where to begin. This article could be useful for such LS companies to embark on their data monetization journey by answering 3 main questions:

1. What to sell?
2. Whom to sell?
3. How to sell?

What to Sell?

Knowing what to sell is the first step.

1. Raw Data â€“ This means selling data directly to customers. It’s the simplest and most direct form of monetization. The buyers in this case either do not need analytics-based outcomes from the data or prefer to run their own algorithms for the same. 
2. Insights â€“ In this case, the seller runs analytics on the incoming data lake and provides useful insights to its potential customers. The seller may or may not share the datasets used and may limit the outcome to specific datasets based on the signed agreement.
3. Analytics enabled Platform â€“ In this case, the seller develops an analytics platform, capable of running analytics on-demand and at scale. A seasoned data service provider and IT support would be required to deploy, execute & maintain the platform. A cloud-agnostic Business Intelligence (BI) platform will generate more demand.

Whom to Sell?

A life sciences company can sell the above solutions to the following potential customers:

1. Providers & Healthcare partners
2. Academic & Research institutes
3. Payer Intermediaries/health insurers
4. Patient engagement & wellness solution providers
5. Pharmaceutical research organizations
6. Suppliers of devices & instruments manufacturers

How to Sell?

This three-step process would provide a methodological approach for monetizing your data.

Step 1: Assessment of Data 

A typical LS company ingests and produces terabytes of data every year. The entire data lake needs to be assessed in terms of:

1. Value of the Data ($) – The measure of the direct impact on revenue or measure of indirect impact at achieving a positive business outcome, from a dataset can be quantified as the Value of that data. For example – data about the seasonal rate of consumption of a critical reagent/consumable could help in managing operating expenses by keeping a check on ordering costs, inventory carrying costs & service levels. This could potentially be a high-value dataset.
2. Volume of the Data – The count of the number of data points is defined as the volume of the data. More number of data points improves data quality, removes statical anomalies & provides confidence in identifying trends.

Plotting all the datasets as shown below would help in identifying the right datasets for monetization.

(Sample representation)

Note: Both X-axis & the Y-axis parameters need to be factored appropriately to plot together on a single graph as shown above

Both high-value datasets and high volume datasets (Quadrant 1,2 & 4) would be useful in creating data monetization strategies.

Step 2: Assessment of Data sources & IT Systems

A detailed assessment of data sources such as enterprise IT systems, laboratory equipment, production ecosystem, etc. would help clients classify their systems on a digital maturity scale as shown below. For example, A system that can interact with other systems (connected) and allows interoperability should rate high on the digital maturity scale. This assessment will also help companies to understand their current state and gauge future modernization needs. 

Step 3: Data Monetization framework

The outcomes of the above steps help create the below data monetization framework.

Note: The X-axis is defined by the multiplication of data volume & data value factors to nullify the effect of a dataset of high volume & low value and vice versa. The Y-axis indicates the digital maturity of the source of the datasets.

After mapping the datasets in the above framework, monetization strategies for each quadrant can be formulated based on below guidelines:

1.      Quick wins: Quadrant 1 indicates high-value data from digitally matured sources that could be easily fed into AI/ML models to derive meaningful insights. LS companies should exploit such data lakes first and generate revenue by selling Insights as a Service or by selling Analytics- enabled platform as a Service. Potential customers for such insights would be Healthcare Providers and Academic & Research institutes. Data in this quadrant should be priced dynamically, as per the perceived value to individual customers. Value-based & transaction-driven pricing models should be employed for the offerings in this quadrant.

2.      Foundational & Strategic: Quadrant 4 requires a more strategic focus as it has high-value data ready to be monetized. As a tactical measure, LS companies can add to their top line by selling the data directly or provide ‘Data as a Service’. However, product & application modernization would help them to shift upwards to quadrant 1, resulting in greater revenues in the long run. As an alternative approach, they can invest in creating premium-priced solutions that provide useful insights and real-time data for targeted customers.

3.      Stage 2 Implementation: Quadrant 2 would be easy to monetize as it incorporates data from systems with high digital maturity, however, due to the low value of the data it would yield less revenue. Hence, companies can employ a subscription-based pricing strategy for long-term revenue. Potential customers could be payer wellness programs or health insurance companies.

4.      Low Priority: Quadrant 3 contains datasets that are not ready to be monetized. LS companies can, however, create goodwill solutions and cross-sell to their existing customers to gain customer loyalty.

The above framework not only provides business leaders a structured approach to monetizing data but also helps them in visualizing their next steps in this journey. Relevant stakeholders can build future roadmaps by evaluating whether to Productize the information (i.e., create new information-based offerings) or Informationalize the products (i.e., including information as a value-add component of an existing offering) using this framework.