- As the pharmaceutical industry continues to grow and evolve, so does the amount of unstructured data that companies must navigate.
- Pharmaceutical companies accumulate vast amounts of data each year, spanning all stages of research and development. With clinical trials alone producing around 3.6 million data points, three times more than a decade ago, the total amount of data can reach hundreds of terabytes.
- According to a report by IDC, 80% of data generated in the healthcare industry is unstructured and comes from diverse sources, making it difficult to analyze and utilize. It can cause colossal damage hindering the adoption of AI, crucial for advancements in drug discovery and clinical trials.
- The key to tackling it lies in being intentional about managing it from the outset rather than waiting until things get out of hand before taking action.
- Choosing the right approach to managing your data—whether building an in-house team or outsourcing to a third-party vendor—can make all the difference in streamlining your operations and maximizing profitability.
Introduction to Unstructured Data Growth in Pharma
As the pharmaceutical industry continues to grow and evolve, so does the amount of unstructured data that companies must navigate. Unstructured data refers to information that isn’t easily categorized or organized, such as text, images, and videos. From research reports and clinical trial results to marketing materials and customer feedback, managing this influx of information can quickly become overwhelming – not to mention costly. However, cutting corners is not an option for maintaining compliance and delivering high-quality products. That’s why we’ve compiled our top tips for tackling unstructured data growth in pharma without sacrificing accuracy or efficiency. So grab a cup of coffee, sit back, and let’s dive into cost-effective strategies for staying on top of your data game.
The Problem With Unstructured Data Growth
Pharmaceutical companies accumulate vast amounts of data each year, spanning all stages of research and development. With clinical trials alone producing around 3.6 million data points, three times more than a decade ago, the total amount of data can reach hundreds of terabytes. According to a report by IDC, 80% of data generated in the healthcare industry is unstructured and comes from diverse sources, making it difficult to analyze and utilize. Despite its vast potential, only 12% of unstructured data is currently analyzed, leaving a goldmine of insights untapped. A study by Harvard Business Review found that clinical stakeholders cannot access 73% of unstructured patient data and content for evaluation and analysis. This inaccessibility hampers innovation and collaboration.
This exponential growth of data presents two challenges: how to lower the cost of keeping and maintaining such a massive amount of data and how to facilitate simple access to historical data for academics and collaborators when they need it.
Furthermore, unstructured data can pose security risks if access controls aren’t implemented properly. Pharmaceutical companies reported the largest cybersecurity breaches compared to other industries. For organizations to understand what kind of unstructured data they have and take action, it is necessary to structure the data.
Aside from financial and security concerns, another issue with unstructured data is its impact on digital transformation. Unstructured data also hinders the adoption of Big Data and AI initiatives, which are crucial for advancements in drug discovery and clinical trials. As much as 73.4% of companies report difficulties adopting Big Data Analytics and AI initiatives, highlighting the roadblock unstructured data creates. AI and Blockchain are transforming clinical trials and drug discovery in the pharmaceutical industry.
Three Strategies for Tackling Unstructured Data Growth
There are several tips that can help effectively tackle unstructured data growth. One of the most important aspects is optimizing storage practices. This can be achieved by adopting a more efficient storage system or implementing techniques to use storage space more effectively.
Another tip is implementing data management policies. By setting clear guidelines and standards for how data should be managed, stored, and accessed, businesses can better control their unstructured data growth while also improving its quality and accuracy.
Automating data management processes is also crucial for tackling unstructured data growth. With automation tools in place, businesses can streamline their workflows and ensure consistent management practices across all departments.
It’s important to note that there isn’t a one-size-fits-all solution when it comes to managing unstructured data growth. Depending on your business’s unique needs and constraints, you may need to consider building your own custom solutions, buying pre-built software, or outsourcing some aspects of your operations.
Ultimately, the key to tackling unstructured data growth lies in being intentional about managing it from the outset rather than waiting until things get out of hand before taking action. By putting these tips into practice early on, you’ll be able to stay ahead of any challenges that arise as your business grows over time.
Optimizing Storage Practices
Unstructured data, like emails and documents, can quickly balloon out of control. Here are key strategies to optimize storage for this ever-growing data type:
- Gain Insights and Tier Storage: Utilize metadata analysis to understand what data you have and how it’s used. This helps identify inactive data for archiving or deletion. Implement tiered storage, placing frequently accessed critical data on high-performance drives and less critical data on lower-cost options like cloud storage.
- Reduce Footprint and Leverage Scalability: Employ data compression and deduplication techniques to shrink file sizes and eliminate redundant copies. Utilize object storage for its scalability and cost-effectiveness in handling massive amounts of unstructured data. Cloud storage offers another scalable and potentially cost-saving option for remote data storage with improved accessibility.
- Implement Lifecycle Management: Establish a data lifecycle management (DLM) strategy. This plan dictates how data is created, stored, archived, and deleted based on its value and regulatory needs. DLM helps control data sprawl and optimizes storage utilization by ensuring data is managed efficiently throughout its lifecycle.
Implementing these strategies can help you control your unstructured data, reduce storage costs, and ensure valuable information is readily available.
Implementing Data Management Policies
Data Management Policies are guidelines and rules that define how data should be managed throughout its lifecycle, from creation to disposal. These policies provide a framework for managing data in a structured and consistent manner, ensuring that data is used effectively and efficiently while maintaining compliance with legal and regulatory requirements. It is one of the most effective ways to tackle unstructured data growth in pharma, and implementing it requires careful planning and coordination across the organization. Here are some steps to consider when implementing Data Management Policies:
- Define the scope: Identify the types of data that the policies will cover, including structured and unstructured data and data in all formats.
- Identify stakeholders: Determine who will be responsible for implementing and enforcing the policies, including IT staff, data owners, and business units.
- Develop the policies: Develop policies that align with the organization’s goals and objectives and are consistent with legal and regulatory requirements. Policies should cover data quality, governance, security, retention, storage, backup, privacy, and confidentiality.
- Communicate the policies: Communicate the policies to all stakeholders, including staff, management, and external parties such as vendors or contractors. Provide training to ensure that all stakeholders understand the policies and their responsibilities.
- Monitor compliance: Monitor compliance with the policies and identify any areas for improvement. Establish procedures for addressing non-compliance and updating the policies as needed.
- Regularly review the policies: Review the policies regularly to ensure that they remain current and relevant and that they continue to support the organization’s goals and objectives.
- Establish metrics: Establish metrics to measure the effectiveness of the policies in achieving their goals, such as data quality, compliance, and cost savings.
Automating Data Management Processes
In today’s data-driven world, the amount of information being generated and collected is staggering. And with this comes the challenge of managing all of it. That’s where automating data management processes comes in. It’s a crucial step in tackling unstructured data growth and ensuring that businesses can leverage the power of their data to make better decisions.
By automating the majority of enterprise data management processes, CIOs can leverage technology to streamline and standardize data management tasks that were previously done manually. This means that data entry, data cleansing, data transformation, and data integration can all be done with ease, improving efficiency, reducing errors, and ensuring consistency in managing data. Specialized software and tools, such as data integration tools, data quality tools, and data governance tools, can be used to automate tasks such as data profiling, data mapping, data cleansing, and data validation, data mobility, allowing organizations to manage data more efficiently and effectively.
Furthermore, automation can also involve using artificial intelligence and machine learning technologies to automate tasks such as data classification, data matching, and data enrichment. These technologies can help organizations extract more value from their data by identifying patterns and insights that might not be visible through manual processes.
It’s important to note that automating data management doesn’t mean completely removing human oversight. There should always be someone responsible for monitoring and maintaining these systems in order to ensure they are functioning properly. Implementing automated processes into your data management strategy can greatly improve efficiency while reducing costs associated with manual labor. It’s worth exploring options available on the market today in order to keep up with growing amounts of unstructured data while staying ahead of potential risks.
Build, Buy, or Outsource Data Management – What’s the Best Way Ahead?
When it comes to managing unstructured data growth, businesses may wonder whether they should build, buy, or outsource their data management systems. Each option has pros and cons that need to be carefully considered before making a decision.
Building an in-house data management system can provide businesses with complete control over the design and implementation of the system. However, this approach requires significant investment in terms of time, money, and resources. It also requires expertise in various technical areas, such as software development, database administration, and security protocols.
Buying an off-the-shelf solution can save businesses time and resources since these solutions are pre-built for specific purposes. This means that they have already been tested extensively by other users, so there is no need to reinvent the wheel. However, buying a solution usually requires yearly licensing fees, which can make it expensive over time.
Outsourcing data management services involves hiring external experts who specialize in managing unstructured data growth on behalf of the business. This approach offers flexibility since businesses only pay for what they use instead of investing upfront capital costs like building an internal team or purchasing licenses for software products.
Ultimately, deciding whether to build, buy, or outsource your data management needs will depend on your business’s unique situation, including available budgetary restrictions, the technical expertise required within internal teams vs. outsourcing partners, etc. However, given the current macro environment and uncertainties, it’s best to opt for outsourcing as it ensures immediate results and even, in some cases, in-year ROI. (Check out Data Dynamics for in-year ROI on your data management software investment).
The Data Dynamics Advantage
The growth of unstructured data in the pharmaceutical industry is a challenge that cannot be ignored. As companies continue to generate massive volumes of data from various sources, it is essential to have an effective plan for managing and storing this information.
Choosing the right approach to managing your data—whether building an in-house team or outsourcing to a third-party vendor—can make all the difference in streamlining your operations and maximizing profitability. Investing time and resources into tackling unstructured data growth may seem daunting at first, but doing so will ultimately put you ahead of competitors who are struggling with inefficient systems. That’s where Data Dynamics comes in.
Data Dynamics is a leading provider of enterprise data management solutions. It helps organizations structure their unstructured data with its Unified Unstructured Data Management software, which encompasses four modules: Data Analytics, Mobility, Security, and Compliance.
Proven in over 28 Fortune 100 organizations, the software uses a blend of automation, AI, ML, and blockchain technologies and scales to meet the requirements of global enterprise workloads. With Data Dynamics, enterprise customers can eliminate the use of individual point solutions with siloed data views. Instead, they can utilize a single software to structure their unstructured data, unlock data-driven insights, secure data, ensure compliance and governance, and drive cloud data management. Ultimately, the company’s vision is to help enterprises achieve data democratization so that users, no matter their technical background, can instantly access, understand, and derive maximum insights from unstructured data sprawls.
To learn more about how Data Dynamics can help your enterprise structure unstructured data, please visit – www.datadynamicsinc.com or contact us at solutions@datdyn.com I (713)-491-4298.
