The importance of data to pharmaceutical sales is well understood. Sales reps and commercial teams have historically relied on a constant flow of sales data and monthly performance results to measure their effectiveness and chart their next move.

But in oncology, dependency on the usual data assets becomes trickier. Complexity of patient pathways and a multitude of fragmented settings of care, therapies delivered by infusion or oral agents, and implications of combination therapies with different dosing and shorter therapy durations, lead to data complexities and increased difficulty in achieving insights.

Typical pain points faced by commercial oncology teams fall into one or more of the following:

• Transactional systems not enabled with basic sets of data that ensure correct profile information, relationships, and hierarchies can be trusted.
• Lack of easy and timely processes allowing commercial teams to make data corrections and/or supplement their territories with relevant oncologists or gatekeepers.
• Too much dependency is placed on the field force to manually provide and collect information, leading to tedious data entry.
• Call planning often involves toggling between different tools that do not link to each other, which places the onus on the user to pinpoint relevant information. This is the same case for territory, account, and KOL engagement planning.
• Absence of oncology-specific details, such as oncology sub-specialties, reporting calls at the indication or regimen level, understanding account information across multiple sites of care (clinic, hospital, physician office, etc.), clinical pathways, and more. 
• Inability to truly measure their performance, the impact of their efforts, or to have true insights available that lead to identifying opportunities and potential problems. 

Increasing market complexity demands timely access to data and data mining tools. Resources need to be able to derive easy-to-understand insights that improve commercial teams’ productivity, and measure the teams’ effectiveness through streamlined key performance indicators (KPIs); and they also must be able to put these insights into context for their relevant stakeholders.

Integrated data solves the problem

Achieving the above is no easy task, and the path to getting there is riddled with hurdles. The scale and complexity of working the data should not surprise anyone. Linking data across the patient treatment journey and making sense of that data is critical to understanding treatment progression and changes over time.

Many data management undertakings end up producing less-than-ideal results due to a lack of accuracy. The inaccuracy leads to a loss of trust over time, a lack of timeliness and consistency in delivery, and/or the inability to achieve a single source of truth for customer. Nevertheless, integrating multiple oncology datasets provides the greatest utility across stakeholders and results in a comprehensive view of patient and physician behavior. This is why it is imperative not only to resolve data challenges, but to provide this data and relevant insights to your customer-facing roles.

Pharma companies that want to excel in this environment need timely access to integrated data sets and analytics tools that can quickly provide insights via easy-to-read dashboards. Unfortunately, most pharma companies’ current business intelligence tools are, for most part, not easily integrated or embedded, so they are unable to provide quick access or to support the daily use of customer-facing roles.

For those companies, that means commercial teams have only two options. They can make decisions with little data to back them up. Or, they can spend significant time copying individual pieces of data from the company’s CRM systems, business intelligence platform, and other data systems, so they can analyze the results and properly plan their activities. Even then, the limited internal data they can access may not provide sufficient detail to determine next-best actions.

Multiple data sources provide the answers

When commercial teams can access multiple relevant sources of data simultaneously, they can answer questions, such as:

• How is my market share performing by indication? 
• What is a specific oncologist’s patient mix? 
• How does this specific account’s performance compare to hub performance at the national level?  
• How long are patients staying on a specific regimen, and at which line of therapy? 
• What combinations of therapies are being used, and how are these modified during the treatment cycle?
• Is there an IDN influence/payer dynamic impacting uptake?
• Is there a persistence of patients on brand-level support programs?

Understanding the above scenarios is achieved by merging multiple assets from medical, pharmacy, institutional claims, sales, prescription, oncology EMR, lab and hospital data, remittance, specialty/hub to promotional and non-promotional activity, and digital data.  Inclusion of additional data points such as affiliations, or national comprehensive cancer network (NCCN) or national cancer institute (NCI) designations, can be included to further inform on key institutions that specialize in cancer treatment.

To succeed in the highly complex and competitive oncology marketplace, pharma companies have to use all of the data available to them, and create seamless workflows that allow commercial teams to gain actionable insights. The impact of this kind of data clarity can be profound for commercial teams trying to navigate the oncology landscape by providing confidence in decision-making that leads to more positive performance impact.