Data Quality: A Modern Approach to an Age-Old Problem

Biopharma companies have relied on, and been challenged by, maintaining high-quality data since the industry’s early days. Now, these firms are relentlessly accumulating more commercial data and increasingly moving promotional efforts to data-heavy digital channels.

As the industry navigates these shifts, it’s critical to understand how biopharma companies stand to benefit from recognizing the importance of—and continually endeavoring to improve—data quality.

A thoughtful data strategy is essential to a biopharma company’s sales force effectiveness – making clear where product is going and instilling confidence in the sales force.

The Foundation of Commercial Success

Data quality is crucial to success in the biopharma realm (and indeed, in any commercial business). How can your firm leverage successful high-quality data management to realize achievements like more accurate field reporting, better call planning, and a more precise IC program?

Two key aspects of data — reliable accuracy and accessibility — together make the difference in optimizing operations. Because if, for example, reps can’t access the customer information they need while in the field — or if commercial dashboard reporting, targeting, or call planning are based on incomplete input — the effects can create disastrous ripples throughout the company’s sales and operations systems.

Here is where the data quality of a customer master is essential and drives the ability to make accurate insights accessible to reps. If sales are attributed to the wrong reps because there are duplicate entries for a single customer, or digital marketing promotions don’t align with calls and sales, it impedes the ability to measure sales force effectiveness. Meanwhile, if field reps sense that data is wrong when they review it, it frustrates them while also degrading their confidence in the data and the overall trust they have in their management team.

Beyond sales and operations, bad data can bring about compliance issues. If HCP specialty information is outdated or incorrect, companies may be marketing products to specialists who they’re not legally allowed to approach.

Without organized data and accurate customer masters, commercial teams will struggle to optimize customer outreach and measure the effectiveness of those efforts. Time and again, we’ve seen that if the company’s data isn’t both accurate and accessible, the ship will go down with that bad or insufficient data.

A Three-Point Solution

Given the importance of data, biopharma companies must solve how to create and maintain high standards for data quality. While each company will require a unique solution, three fundamentals should guide each effort:

  1. Build in automated checks to prevent inaccurate or incomplete information from entering a data infrastructure, especially the single source of truth that is the customer master. What if a data admin creates “dummy” entries to test certain features of the system, but forgets to delete them? Is your system set up to catch these purposefully false entries that may inaccurately distort the overall data? Or, what if there are data integrity issues from upstream data vendors, skewing the metrics with incomplete data? Will your system alert you to these holes and require completion? The simplest solution to combat human mistakes is to work with a data management system that automatically checks for these types of errors to maintain high data quality.
  2. Optimize UI/UX for reps to encourage them to input information as close to real-time as feasible. Companies need rep participation to gather the most accurate and complete customer data. The best way to accomplish this is to capture the data when it’s freshest—immediately following a customer interaction. If a customer relationship management (CRM) system presents the field rep’s user experience as a burden in any way, the rep is less likely to use it on-the-fly. When field reps don’t stay on top of updates to the primary affiliations and locations of healthcare providers, this outdated data can result in high volumes of discrepancies for the data stewards to review, as well as missed call opportunities for the reps. If it only takes 10 seconds (instead of five minutes) to update an affiliation, any rep is more likely to enter this critical change. Data managers must ensure their company’s system removes any barriers to use and is optimized for a rep to access and interact with it quickly and easily.
  3. Check and clean data on a regular basis to maintain the data quality than enables downstream analytics efforts. Ultimately, regular audits by an automated system, combined with manual checks by a data steward, will ensure the highest quality data. Whenever a customer master ingests a new data source, it is important for data managers to review and be knowledgeable on the data they are working with. This extends to taking the time to review any business rules built into the customer master system and make sure they are accurate and aligned with the commercial strategy. The data manager is the guardian and enforcer of the company’s data policies, and can operate the system to identify nuanced and specific information for downstream analytics. When employing data to deliver analytics that shape decisions for company sales and operations decisions, the data team members are the ultimate defense against faulty data points. The rest of the company depends on them.

Bottom Line: Utilize All Resources

Maintaining good quality data that’s accurate and accessible isn’t difficult, but it will require using all the tools in the data manager’s toolbelt.

Start and stick with the fundamentals: ensure robust checking and cleaning of data is a priority for the data management team. And, just as vital: arm the data team with configurable technology to optimize processes that adjust for their commercial team’s unique situations. This should include everything from improving the UI/UX of the system reps are using in the field, up through the automated checks for missing or bizarre data.

Avoid off-the-shelf solutions that can’t provide the necessary flexibility to implement custom rules. The ultimate data management solution for your team will be the one most tailored to the team’s unique needs, and one that will grow with the company. When considering which tools and platforms to use for a launch, aim for technology that can support changes down the road, such as new indications, updated strategies or additional products.

Together these steps will help to avoid the bane of data management: garbage in, garbage out. The data managers don’t want garbage data to foul up their system — not when company-shifting decisions depend on the data they manage. A strong data management strategy, along with the finest technology solutions, can create a system that sets up a biopharma company for continued success.