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The Crucial Role of Data Management in the Pharmaceutical Industry

data management in pharma industry

by Kimberly Gregorio | Last Updated: June 28, 2024 | Read Time 1 min read

Data management is crucial in today's data-driven society. More data is being generated than ever before. According to IDC, the datasphere will grow to a staggering 175 zettabytes by 2025, nearly four times the 45 zettabytes recorded in 2019 (Source) . If you were to put the entire datasphere of 175 zettabytes on DVDs, experts estimate that you would have enough disks to circle Earth 222 times. This illustrates the sheer volume of data we are dealing with today.
Effective data management helps organizations organize, store, and retrieve their data efficiently. In the pharmaceutical industry, this is particularly important due to the vast amounts of data generated from research, clinical trials, and patient records. Proper data management ensures that this information is accessible and usable, leading to better decision-making and improved operational efficiency.

Benefits of Master Data Management

team discussion

Master data management (MDM) plays a critical role in maintaining data integrity and accuracy. It ensures that all stakeholders have access to consistent and reliable data, which is essential for regulatory compliance and patient safety. With MDM, pharmaceutical companies can streamline their operations, reduce costs, and enhance their ability to respond to market changes.

Key Points to Remember

  • Data Explosion: The global data volume is expected to reach 175 zettabytes by 2025.
  • Importance of Organization: Effective data management is essential for operational efficiency and decision-making.
  • Master Data Management: Ensures data integrity, accuracy, and compliance.
    By focusing on these key aspects, pharmaceutical companies can navigate the complexities of data management and harness the power of their data for better outcomes.

The Challenge of Unstructured Data in Data Management

Forbes reported that 95% of businesses (Source) feel unstructured data is a problem. And there is a good reason for this. A deep dive by CIO revealed3 that 80-90% of the data generated today is unstructured (Source) . It’s very likely that your organization is having data issues of its own.

Unfortunately, if your organization isn’t storing, processing, and monitoring the data it holds correctly, problems will follow. For example, when unstructured data is at play, it’s easy for incomplete or inaccurate information to slip into analytical models, CRMs, revenue cycle management and other enterprise applications. Unstructured data also makes it hard to spot errors. And when errors go unnoticed, data issues accumulate and can impact everything from quarterly reports to sales forecasting.

Why Unstructured Data is a Widespread Problem?

  1. Difficulty in Analysis: Traditional data analysis tools are designed for structured data. Extracting meaningful insights from unstructured data requires advanced technologies like natural language processing and machine learning, which many organizations lack.
  2. Storage Challenges: Unstructured data consumes vast amounts of storage space. Managing this data efficiently while ensuring it remains accessible is a significant challenge for IT departments.
  3. Security Risks: Unstructured data often contains sensitive information. Without proper management, this data can pose significant security and compliance risks, particularly in regulated industries.
  4. Inefficient Decision-Making: When valuable information is buried in unstructured data, decision-makers may miss crucial insights, leading to suboptimal business choices.
  5. Reduced Productivity: Employees spend countless hours searching for information hidden in unstructured data, reducing overall productivity.
  6. Compliance Issues: With regulations like GDPR and CCPA, these laws say companies must find and protect people's personal information. This includes data that's not neatly organized. Companies need to know where all personal data is stored.
  7. Data Silos: Unstructured data often resides in disparate systems, creating data silos that hinder collaboration and holistic analysis.

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AI and Machine Learning Rely on Good Data Management

Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries, enhancing decision-making, automating processes, and driving innovation. However, the success of these technologies heavily depends on the quality of the data they use. Good data management is also critically important for organizations that want to deploy AI and machine learning.

Thanks to advances in computing and data science, these powerful new technologies can bring game-changing insights and efficiency at every level. And organizations that deploy the technology correctly have a significant competitive advantage. However, no matter how innovative organization algorithms are, if they run on top of scattered, inconsistent and outdated data, results can be harmful.

When it comes to master data management, there are three letters that every data team should know: ETL.
ETL means Extract, Transform, and Load. These are the main steps in managing data. ETL helps companies gather information from different places. It then changes this information to make it useful. Finally, it puts all the data in one spot. This process creates a single, reliable source of information. Many businesses use ETL to handle their data.

It's a common way to make sure all company information works together. ETL helps turn messy data into clear, usable facts. This makes it easier for companies to understand their information and make smart choices. But for organizations that need help with this, P360 has a solution: the BirdzAI Data and Analytics platform!

Why AI and ML Need Good Data Management?

good data management for ai and ml

Artificial Intelligence (AI) and Machine Learning (ML) are transforming how businesses operate, enabling smarter decisions, automation, and innovation. However, the effectiveness of AI and ML heavily depends on the quality of the data they use.
Good data management is essential for AI and ML to function correctly and produce reliable results. The better the data, the more accurate and reliable the models will be. Here are some key reasons why good data management is essential for AI and ML:

  1. Data Quality: High-quality data is accurate, complete, and consistent. AI and ML models learn from data, and the quality of the data directly impacts their performance. Poor quality data can lead to incorrect predictions and unreliable insights. Good data management practices ensure that data is cleaned, validated, and maintained to a high standard, resulting in more accurate and reliable models.
  2. Data Accessibility: AI and ML models require access to large datasets to learn and make predictions. Good data management ensures that data is stored and organized in a way that makes it easily accessible. This involves using efficient data storage solutions and indexing methods that allow quick retrieval of data when needed.
  3. Data Integration: Businesses often collect data from multiple sources, such as customer databases, transaction records, and social media platforms. Good data management practices include data integration processes that combine data from different sources, providing a holistic view that enhances the capabilities of AI and ML models.
  4. Data Security: Protecting data from unauthorized access and breaches is vital, especially when dealing with sensitive information. Good data management involves implementing robust security measures to safeguard data. This ensures that AI and ML models only use secure and authorized data, maintaining the integrity and privacy of the information.
  5. Data Governance: Data governance involves establishing policies and procedures for managing data assets. It ensures that data is handled consistently and according to regulatory requirements. Good data management practices enforce data governance, ensuring compliance with regulations and maintaining the integrity of AI and ML processes.

BirdzAI Helps Pharmaceutical Companies Make Out of Data for effective Data Management

BirdzAI is a powerful yet flexible solution that comes equipped with four pre-built modules for Master Data Management, Insights & Analytics, Sales Operations, and Marketing Operations. The modules can be deployed independently or combined for a complete data and analytics solution. But no matter what, the real magic begins at the data layer.

BirdzAI supports pharmaceutical commercial operations. With rapid, end-to-end data management capabilities, including the ingestion, storage, processing, and analysis of data derived, from commonly used sources. Sources like first and third-party prescription data, specialty pharmacy data feeds, CRM systems, marketing interactions reports and more.

These are important features because, as noted above, organization data often comes unstructured from a wide variety of sources. If not effectively managed, this can result in redundant and even conflicting information.

To help eliminate this issue, BirdzAI integrates easily with any commercial operations workflow. Also creates a single source of truth by bringing all of an organization’s commercial data together into a state-of-the-art master data management ecosystem.

This is what enables the platform’s advanced AI and machine learning algorithms to then turn data into insights for real-time sales and marketing operations decision-making. Including forecasting, brand propensity analysis, next best action insights, customer alignment, customer, territory planning, sizing, incentive compensation strategy, and much more.

Why Data Management Matters in Pharmaceuticals?

why data management

The Importance of Data Management in Pharma

  1. Enhancing Research and Development (R&D)
    Pharma R&D involves extensive data collection from various sources, including lab experiments, clinical trials, and patient studies. Proper data management allows researchers to organize and analyze this data efficiently. This leads to:
    • Faster Drug Discovery: Accurate and well-organized data accelerates the identification of potential drug candidates.
    • Improved Collaboration: Centralized data management facilitates collaboration among research teams, enhancing productivity and innovation.
    • Reduced Costs: Efficient data management minimizes redundancies and errors, leading to cost savings in R&D efforts.
  2. Ensuring Regulatory Compliance
    The pharma industry is highly regulated, with strict requirements for data accuracy and reporting. Effective data management helps ensure compliance with regulations such as:
    • Good Manufacturing Practices (GMP): Proper data management ensures that manufacturing processes meet regulatory standards.
    • Good Clinical Practices (GCP): Accurate data management is essential for maintaining the integrity of clinical trials and ensuring patient safety.
    • FDA and EMA Requirements: Data management ensures that all necessary documentation and data submissions meet the stringent requirements of regulatory bodies like the FDA (Food and Drug Administration) and EMA (European Medicines Agency).
  3. Improving Patient Safety and Treatment Outcomes
    Patient data is critical for effective treatment and safety monitoring. Good data management practices ensure that patient information is:
    • Accurate: Reducing the risk of errors in patient records enhances treatment effectiveness.
    • Accessible: Easily accessible data allows healthcare providers to make informed decisions quickly.
    • Secure: Protecting patient data from unauthorized access ensures privacy and compliance with regulations like HIPAA (Health Insurance Portability and Accountability Act).
  4. Optimizing Supply Chain Management
    The pharma supply chain involves managing the flow of raw materials, manufacturing processes, and distribution of finished products. Data management helps in:
    • Inventory Management: Accurate data on inventory levels prevents stockouts and overstock situations.
    • Tracking and Traceability: Efficient data management enables the tracking of products through the supply chain, ensuring quality and compliance.
    • Demand Forecasting: Analyzing data helps predict demand patterns, leading to better inventory planning and cost savings.
  5. Supporting Business Decision-Making
    Accurate and well-managed data provides insights that support strategic decision-making in the pharma industry. This includes:
    • Market Analysis: Data analysis helps identify market trends and opportunities, guiding product development and marketing strategies.
    • Performance Monitoring: Tracking key performance indicators (KPIs) helps assess the effectiveness of business operations and identify areas for improvement.
    • Risk Management: Analyzing data helps identify potential risks and develop mitigation strategies, ensuring business continuity.

In the pharma industry, data management is crucial for several reasons:

  1. Regulatory Compliance: Pharma companies must comply with strict regulations regarding data accuracy and reporting. Proper data management ensures compliance and avoids hefty fines.
  2. Research and Development: Efficient data management accelerates research and development (R&D) by providing accurate and comprehensive data, leading to faster drug discovery and development.
  3. Patient Safety: Accurate data management ensures that patient information is correctly recorded and analyzed, enhancing patient safety and treatment effectiveness.
  4. Operational Efficiency: Streamlined data management improves operational efficiency by reducing redundancies and errors, leading to cost savings and better resource allocation.

How BirdzAI Helps Pharma Companies

  1. Centralized Data Collection: BirdzAI collects data from various sources, including clinical trials, patient records, and supply chain systems. It centralizes this data, providing a unified view that is easily accessible and manageable.
  2. Data Integration: One of the biggest challenges in data management is integrating data from different sources. BirdzAI seamlessly integrates data, ensuring that all information is connected and can be analyzed together. This integration helps in identifying patterns and correlations that might be missed when data is siloed.
  3. Advanced Analytics: BirdzAI uses AI and ML algorithms to analyze data. These advanced analytics provide deeper insights into complex datasets. helping pharma companies uncover trends, predict outcomes, and make data-driven decisions. For instance, AI can identify potential side effects of a new drug by analyzing patient data from clinical trials.
  4. Data Quality and Accuracy: Ensuring data quality is vital for reliable analysis. BirdzAI includes robust data cleansing and validation processes, removing duplicates and correcting errors. This ensures that the data used for analysis is accurate and trustworthy.
  5. Real-Time Insights: In the pharma industry, timely information can make a significant difference. BirdzAI provides real-time data updates, allowing companies to react quickly to new information. For example, real-time monitoring of clinical trials can help identify issues early, ensuring the trial stays on track.

BirdzAI Makes Data Capture and Integration Easy

data visualization

There is no doubt, developing a modern data estate can be an extremely complex process. However, with BirdzAI’s data management module, pharmaceutical companies can ingest all data types (structured, unstructured, semi-structured) and easily derive insights from it.

BirdzAI makes data handling easier. It automates data collection from many sources. This process is safe and secure. BirdzAI ensures all data is captured the same way. It then fits this data into the bigger picture. This helps drug companies work smarter. They can gather data quickly. They can combine different types of information.

BirdzAI turns raw data into useful facts. This happens in real-time, without delays. Drug companies can now use their data more effectively. This leads to better business decisions. BirdzAI streamlines the entire data process. A powerful tool for pharma businesses.

These are critical features that forward-thinking pharma companies need, especially as they rely more heavily on data-driven techniques. And to build a solid foundation, the integrity of data becomes increasingly important.

The inability to trust the data gathered from a variety of sources can sabotage an organization’s efforts to enhance revenue and improve operational efficiencies. That’s why proper data capture and integration is a must.

How BirdzAI Simplifies Data Capture for effective Data Management

  1. Automatic Data Collection: BirdzAI automates the process of collecting data from various sources. Whether it's customer information from your CRM, sales data from your POS system, or feedback from social media, BirdzAI gathers it all automatically. This saves time and reduces the risk of errors that come with manual data entry.
  2. Real-Time Updates: BirdzAI, makes your data always up to date. It continuously collects and updates data in real-time, ensuring that you always have the latest information at your fingertips. This is especially important for businesses that need to make quick decisions based on current data.
  3. Easy Integration: One of the biggest challenges in data management is integrating data from different sources. BirdzAI makes this easy by seamlessly combining data from various systems into a single, unified platform. This means you no longer have to switch between different applications to find the information you need.

How BirdzAI Enhances Data Integration in Data Management

  1. Unified Data Platform: BirdzAI creates a centralized data platform where all your data is stored and organized. This unified view makes it easier to analyze and use the data. You can see all your information in one place, making it simpler to identify trends and make informed decisions.
  2. Data Cleaning and Validation: Data quality is crucial for accurate analysis. BirdzAI includes powerful tools to clean and validate your data, removing duplicates and correcting errors. This ensures that the data you rely on is accurate and trustworthy.
  3. Advanced Analytics: BirdzAI uses advanced analytics to turn your data into actionable insights. It can identify patterns, trends, and anomalies in your data, helping you make better business decisions. For example, you can use these insights to improve customer service, optimize your inventory, or boost your marketing efforts.
  4. Scalability: As your business grows, so does your data. BirdzAI is designed to scale with your needs, handling increasing amounts of data without compromising performance. This means you can continue to rely on BirdzAI as your business expands.

Benefits of Using BirdzAI for Master Data Management

Improved Decision-Making: With BirdzAI, pharma companies can make better decisions based on accurate and comprehensive data. Whether it's deciding on the next step in a clinical trial or optimizing the supply chain, data-driven decisions lead to better outcomes.

Accelerated Drug Development: By streamlining data management and providing advanced analytics, BirdzAI accelerates the drug development process. Faster data analysis means quicker identification of potential issues and opportunities, speeding up the path to market.

Enhanced Patient Safety: Accurate data management and real-time insights ensure that patient information is always up to date. This leads to better patient monitoring and enhances overall safety.

Ensure Data Compliance: BirdzAI helps you comply with industry regulations and standards by ensuring that your data is accurate, secure, and properly managed. This reduces the risk of non-compliance and potential fines.

Real-World Applications of BirdzAI in Pharma Industry for Data Management

Supply Chain Management: Managing the pharma supply chain involves tracking inventory, shipments, and logistics. BirdzAI integrates data from various sources, providing a comprehensive view of the supply chain. This helps in optimizing inventory levels, reducing costs, and ensuring timely delivery of products.

Patient Data Management: Accurate patient data is crucial for effective treatment and safety. BirdzAI ensures that patient records are complete, accurate, and easily accessible. This helps healthcare providers make informed decisions and improve patient outcomes.

Regulatory Reporting: Compliance with regulatory requirements is essential in the pharma industry. BirdzAI simplifies regulatory reporting by ensuring that all data is accurate and up to date. This reduces the risk of non-compliance and associated penalties.

To learn how BirdzAI can take your data to the next level, visit BirdzAI

About P360

Delivering a 360 view through the pharma-to-physician ecosystem, P360 designs and deploys capabilities that ensure the highest efficiencies and returns on sales operations, data management, and IoT innovation.
With expertise in supporting commercial operations for companies of all sizes, P360 has built an industry-leading platform that gives customers ownership of their data and the ability to leverage artificial intelligence and machine learning capabilities.