Impact of data quality in the Life Sciences Industry

Effective resource management is critical in project and portfolio management in life sciences organizations as the stakes are high with a paramount demand for accuracy. Data quality in life sciences impacts decision-making, influencing project timelines and resource allocation. Poor data quality often results in delayed projects, over- and under-allocation of resources, and upsurges in costs.  

In an industry with regulatory constraints and changing demands, having precise and reliable data is highly important to track resource capacity and availability. Hence, quality data plays a significant role and helps the decision makers to take informed decisions that adhere to the strategic goals and improve portfolio delivery. 

In this blog, we will delve into the role of data quality in resource management and key strategies to ensure high quality data in PPM.

The Cost of Poor Data Quality  

The impact of poor data quality in life sciences is significant, both financially and operationally. According to recent estimates from Gartner, businesses lose $10 to $15 million each year due to low data quality, and this number is expected to rise as information systems grow more complex (1). These errors lead to high maintenance costs, system downtimes, and reduced productivity. 

 But the hidden costs are even more bothersome. For instance, team members in life sciences organizations spend a lot of time correcting and cleaning up data errors, which puts pressure on productivity. As per Gartner’s research, poor data quality can reduce 20% labor productivity. For example, project managers often spend most of their time dealing with data issues while they should be working on significant tasks like model development and analysis (2). 

 Poor data quality also leads to higher expenses for organizations as they need to monitor and fix data inconsistencies. It also causes additional problems like weak data strategies, failure to spot errors, under- or over-allocation of resources, inaccurate forecasting, regulatory compliance, lack of real-time data, and data silos, to name a few.  

 The financial burden of poor data quality goes beyond direct costs; it includes increased maintenance expenses and lost productivity. Therefore, ensuring high data quality is crucial for effective resource management and the overall health of the business. 

Strategies for Ensuring High Data Quality in PPM

Maintaining high data quality plays a significant role in life sciences project portfolio management. It is a continuous process, and it would require consistent efforts from various stakeholders involved. 

 Here are a few strategies to ensure high-quality data in PPM. 

1. Establishing data governance: It is essential to implement data governance policies that define standards, responsibilities, roles, and data management processes. With proper guidelines on data storage, collection and processing, organizations can enhance data quality and foster uniformity. Data governance ensures reliability and consistency, reducing errors and improving data integrity.  
2. Offering training on data quality: Employees should be provided with regular training focusing on managing data responsibly. There should be seminars and workshops on error detection, data collection methods, and practices that help teams maintain data quality standards. An informed team can identify and mitigate data concerns, resulting in data reliability.  
3. Keeping up-to-date and accurate information: It is vital to maintain current and accurate data sources, systems, and processes. The documentation includes certain assumptions from the analysis, transformations applied, and data lineage, which further offers clarity to comprehend the data context and prevents misunderstandings. High data quality documentation provides accuracy and transparency throughout the project’s cycle.  
4. Implementing data validation methods: These are the techniques that ensure accurate data in the systems. These methods check range constraints, format validation, and key integrity rules that can prevent inconsistent entries and minimize errors.  
5. Using data cleaning tools: These tools identify and correct issues by comparing datasets against standard patterns. A few tasks like normalizing values, removing duplicates, and correcting issues can be handled effectively through these tools, ensuring reliable, high-quality information required for project management.

The Role of Data Quality in Resource Management

High-quality data plays a key role in resource management within project and portfolio management within life sciences, owing to their complexity and longevity. Accurate and reliable data enables project managers to allocate resources effectively, make right decisions, and attain success. A few highlights of data integrity include: 

1. Precise resource allocation: With reliable data, project managers can understand resourcing needs and allocate resources avoiding over or under utilization. Also, quality data leads to accurate predictions leading to data-driven decisions when it comes to hiring needs. 
2. Reduces inefficiencies and errors: Manual data and dissimilar systems often lead to errors. Minimizing the dependency on manual data and focusing on quality data results in minimal errors, thereby offering efficient resource management.   
3. Improves visibility: With quality data, stakeholders of a project can clearly understand strategic goals and resource availability. It facilitates transparency among all the teams, which in turn reduces discrepancies.  
4. Sound decision-making: High-quality and reliable data offers crucial insights to the project managers, fostering scenario modeling and project prioritization. It eventually helps life sciences organizations to proactively respond to the rapidly changing demands of the industry.  
5. Compliance and risk management: Quality data ensures compliance which ensures timely audits and proactive risk identification, thereby improving risk management through a strong foundation to track project progress and resource allocation. 

Integration of data quality in resource management for life sciences PPM offers many benefits including improved financial performance and better overall outcomes in clinical trials and drug development. Let us learn how organizations can ensure high-quality data. 

 In the regulated life sciences PPM, high data quality plays a crucial role in successful resource management. Establishing a strong data governance framework along with employee training, proper validation methods, updating information, and making proper use of data cleaning tools helps life sciences organizations to enhance their data quality. Reliable and quality data enables project managers and other stakeholders to make right decisions, drive achievements, and optimize resources. 

References:

1. How to Create a Business Case for Data Quality Improvement, Gartner, 2018 
2. Measuring the Business Value of Data Quality, Gartner, 2011