What to do about pharma’s disjointed data?

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What to do about pharma’s disjointed data?

By Justin Khalifa, partner at Beghou Consulting

With data proliferating rapidly in the biopharma industry, commercial teams need to harness this data to accurately track prescription pull-through from sales and marketing activities and understand the return on their promotional investments. Marrying these disparate data streams to create holistic data sets can give biopharma commercial leaders a comprehensive view of their company’s promotional efforts and how those efforts play into patient treatment journeys.

If companies can successfully organize and analyze their various streams of data, it’s possible to gain more granular insights into commercial performance, patient treatment journeys and HCP behaviors. But, if they don’t, they’ll be left with disjointed sets of data that don’t provide sufficient insight into their commercial environments. The days of throwing money at marketing campaigns without a close eye on ROI are over. Commercial teams face increased pressure today to prove the value of their promotional efforts and make clear how those efforts impact pull-through. This is especially true in the rare disease space, where marketing budgets are smaller and teams are leaner. It’s crucial that these teams implement the right promotional tactics and put the right people in the right places to succeed. Then, closely monitor results and pivot, when necessary.

Therefore, amid this explosion of data, it’s crucial that biopharma commercial teams bring disjointed data together into holistic data sets that fuel commercial effectiveness. In our view, there are four keys to success here:
1. Proper planning and goal definition.
2. Creative analytical modeling.
3. The use of cloud-based technology that facilitates master data management.
4. Collaboration across commercial teams (sales, marketing, market access).

The opportunities associated with this data explosion are many. However, the challenges and risks are exacerbated as the quantity of data grows. Companies that prepare thoroughly and put in place the infrastructures and processes to organize these data streams and uncover insights will position themselves to respond more agilely to changing market conditions. Those that fail to master these ever-expanding data sources will find themselves more lost than ever.

More promotion, more data

Data is coming at commercial teams from all angles. Today, teams have access to a bewildering array of data types – from HCP-level digital promotion details to patient-level claims. At a high level, the fastest-changing types of data fall into two categories: marketing data and patient-level data.

Digital marketing is by no means new to the biopharma industry. But it has expanded significantly during the COVID-19 pandemic. In conjunction with this rise in digital marketing activity, companies have access to more granular data. For instance, a company can track an individual physician’s interactions with promotional emails and banner ads. They can then marry this data with sales data and insights from the field sales force to understand the interplay between and effectiveness of various commercial efforts targeted at specific HCPs. When they organize and analyze data properly, companies should be able to articulate how sales and marketing activities connect with the writing of a prescription.

Patient data is another important category. Granular patient-level data can offer insights into not only the patient treatment journey but also the process patients go through from the writing of a prescription by an HCP to the filling of a prescription. Companies can use this data to identify drop-off points during the treatment process, identify red-tape issues (related to prior authorization, for example) that stand in the way of a patient starting therapy, and fine-tune patient hub processes. In the end, companies must work to get patients on therapy as quickly as possible so they can realize the benefits of treatment. Thorough analysis of these patient-level data points can help companies identify and remove roadblocks to achieving that objective.

A strategic combination of a company’s own data (e.g., sales data, patient hub data, specialty pharmacy data, marketing campaign data) and third-party data (e.g., claims, lab data, EHR) can provide a 360-degree view of the commercial and patient treatment lifecycle. And advanced analytics processes and robust technology (which we’ll discuss in the next section) enable tactics like tokenization that can help companies anonymously aggregate and track patient-specific data to improve knowledge of the patient journey and take steps to streamline that journey and speed therapy starts.

Creating holistic data sets

These streams of data aren’t all new. But thanks to the increased granularity and adoption of this data, along with the advanced state of the technology they have at their fingertips, biopharma companies can now more readily pull these data streams together and create holistic data sets that tell the detailed and accurate story of the patient treatment journey.

Advance planning is the key to making the most of these various data sources. Companies need to not only have a detailed understanding of their various data sources, but they also need to understand what they are trying to measure and uncover with these data points. This knowledge will lead into a discussion of which analyses they need to perform and which data models they need to build to facilitate these analyses.

Commercial leaders must ask themselves, “what are we trying to learn from this data?” Thinking through this seemingly simple question ahead of time can help leaders develop sound data analytics strategies. The goals can vary. Some teams may be looking to map patient journeys. Others may be looking to better understand duration to inform a forecasting effort. No matter the goals (and there can be many), it’s crucial to map them out in advance.

To put these plans into action, companies need advanced data management technology and sophisticated analytical modeling. Robust cloud-based data management technology that can facilitate master data management and process large amounts of data is an essential foundational piece of this effort. And once a team knows what it wants to uncover from its data, it needs to figure out how it will gain those insights through analytics.

Finally, even after implementing a robust technology and analytics infrastructure and properly planning, all these efforts could be wasted if the company doesn’t collaborate effectively across commercial functions. Given the amount of data that can be captured and the large number of vendors often involved, there are a lot of hands that touch a company’s data. Without effective collaboration, it’s easy for a commercial entity to devolve into siloed units, each working with their own slices of data but failing to bring them together into a cohesive whole.

A rapidly evolving data environment

This increase in the amount of data is not slowing down anytime soon, putting added onus on biopharma companies to get their data management and analytics operations up to speed quickly.
In the end, a mastery of commercial data will help a company better understand the return on their various commercial investments. This understanding helps the commercial team make a case to company leadership for its efforts and, in some cases, secure more budget. It also helps the company more closely monitor results, assess the effectiveness of promotional strategies and refine those strategies over time.
Data is by nature disjointed today. But by turning it into something holistic and usable, companies can gain unprecedented levels of insights into their commercial efforts. In a time of rapid market change, these granular insights can be a significant competitive advantage.