10 Scenarios of Data Pollution Impacting CRM & Biopharma Commercial Operations


In the world of Biopharma commercial operations, data doesn't remain static in its origin system; it flows, syncs, and integrates across multiple platforms. A seemingly minor error in one system can cascade into significant complications as data progresses from CRM to MDM, and then to Reporting & Analytics. Here's a closer look at how intertwined these repercussions can be:

1. Incorrect Affilitation Attribution in CRM

Example: A sales representative mistakenly associates Dr. Jensen with "HealthFirst Group Practice" instead of her actual affiliation, "WellCare Group Practice", in the CRM. When this flawed data is ingested by the MDM system, it recognizes "HealthFirst" as Dr. Jensen's official affiliation without a thorough review. Consequently, in Reporting & Analytics, sales attributed to Dr. Jensen are miscredited to "HealthFirst". This not only distorts sales figures for both group practices but can also lead to misguided marketing campaigns and misaligned sales strategies.

 

2. Sub-Specialty Mismatch and Its Consequences

Example: A cardiologist who recently branched into "Electrophysiology", a sub-specialty, is still tagged under general "Cardiology" in the CRM. Once this outdated information is mastered in the MDM, any sales or interactions involving this cardiologist are logged under the broader "Cardiology" segment. Reporting & Analytics, drawing from the MDM, will present skewed analytics, potentially leading to inappropriate resource allocation for training, marketing, and incentive compensation tailored to the broader specialty rather than the specific sub-specialty.

 

3. Erroneous Product Interest Indication in CRM

Example: A sales rep mistakenly logs that a physician showed interest in "Drug A" when, in fact, the discussion was about "Drug B". This error, once mastered in the MDM, results in the physician receiving marketing material and follow-ups for the wrong drug. Reporting & Analytics, in turn, could falsely depict an increased demand or interest trend for "Drug A" in that region, leading to misinformed strategic decisions and wasted marketing efforts.

 

4. Flawed Geographical Data in CRM

Example: Dr. Turner recently moved from "City Clinic" in the downtown area to the more suburban "Greenwood Health Center". However, due to oversight or outdated information, the CRM still associates Dr. Turner with "City Clinic". When this incorrect data is pulled into the MDM, it retains Dr. Turner's old affiliation. As a result, sales reps continue targeting the downtown clinic to engage with Dr. Turner, wasting valuable time and resources. Moreover, Reporting & Analytics might still attribute any interactions or sales linked with Dr. Turner to the downtown region, leading to distorted geographical sales data and misguided allocation of marketing efforts for both locations.
 

5. Mistaken Sample Distribution in CRM

Example: Suppose a sales rep wrongly logs in the CRM that a clinic received samples of "Medicine X" when they were actually provided with "Medicine Y". The MDM system, trusting this input, logs this distribution data without flagging the inconsistency with inventory records. Later, Reporting & Analytics might show an unexplained surge in "Medicine X" samples, prompting further distributions, skewed sales predictions, and potential oversights in restocking the genuinely distributed "Medicine Y".

 

6. Inaccurate Event Attendance in CRM

Example: A Biopharma-sponsored symposium's attendee list is incorrectly uploaded to the CRM, missing out on several key healthcare professionals who attended. When this incomplete data gets mastered in the MDM, follow-up campaigns or feedback surveys might exclude these professionals. Consequently, Reporting & Analytics might undervalue the event's success or impact, leading to potentially misguided decisions about future event sponsorships or formats.

 

7. Misclassified Demographics in CRM

Example: A sales rep, based on preliminary data, classifies a healthcare institution's demographic as primarily "Elderly". The MDM system, using this data, might sync this classification across platforms. Reporting & Analytics, drawing from this, could erroneously guide marketing teams to push drugs or therapies tailored for geriatric ailments to this institution. The actual sales figures might then not align with the forecasts, leading to bafflement among sales strategists.

 

8. Flawed Email Interaction Logs in CRM

Example: A glitch or human error in CRM records an email as "unread" by a physician, even though they've not only read it but also showed interest in the content. As this data is integrated into the MDM, automated marketing sequences might re-send the same content or follow-ups tailored to "unengaged" recipients. Reporting & Analytics could then depict this physician as disinterested, potentially leading to lost sales opportunities or misguided relationship-building strategies.

 

9. Misattribution in Sunshine Reporting Due to CRM Errors

Example: A pharmaceutical company organizes a "Lunch and Learn" event, inviting numerous healthcare providers. The event's cost, intended to be split among attending providers as reportable expenses under the Sunshine Act, is logged into the CRM. However, due to a data entry oversight, Dr. Smith's National Provider Identifier (NPI) is mistakenly swapped with Dr. Stevenson's.

 

As this erroneous data is mastered in the MDM, the financial transaction is attributed to Dr. Stevenson, even though they didn't attend the event. When Reporting & Analytics pull this data to generate Sunshine Act compliance reports, Dr. Stevenson's financial interactions with the pharmaceutical company are inflated, while Dr. Smith's are underrepresented.

 

This misattribution poses risks on multiple fronts. Firstly, there's a direct compliance risk as the report to the CMS (Centers for Medicare & Medicaid Services) would be inaccurate. Secondly, Dr. Stevenson, upon discovering the discrepancy, might feel their trust with the pharmaceutical company is breached. Lastly, it could raise unwarranted suspicions about financial relationships, potentially damaging reputations.

 

10. Misaligned Territory Data Affecting Incentive Compensation

Example: A sales rep, Ms. Johnson, secures significant orders from Dr. Roberts, a well-regarded physician in the region. In the CRM, Dr. Roberts is mapped to the northern territory due to his primary affiliation with a renowned healthcare institution there. However, in the MDM, he's aligned to the southern territory because of recent consultations he's held at a clinic in that area. As a result of this territorial misalignment between the CRM and MDM, the sales credited to Ms. Johnson, who covers the northern territory, are mistakenly attributed to Mr. Anderson, the rep covering the southern territory. Consequently, when Reporting & Analytics tools calculate incentive compensation, Ms. Johnson's deserved bonus is directed to Mr. Anderson. This can lead to disputes, morale issues among the sales team, and potential mistrust in the incentive compensation framework of the company.
 

How Gridlex's Unified Platform for Biopharma Commercial Operations Reduces Data Pollution Risks:

Integrated Data Flow: By consolidating multiple applications into a single platform, Gridlex ensures seamless data transfer, eliminating the risks of misalignment between systems.

 

 

Gridlex recognizes the intricate challenges of Biopharma commercial operations and has crafted a solution that not only streamlines processes but also ensures the integrity and reliability of data, forming a robust foundation for informed decision-making.