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. The majority of this data, about 80%, is unstructured and derived from diverse sources, each with unique data handling methods and equipment. However, most companies only analyze 12% of the data, leaving the remaining 88% unexplored. Additionally, 73% of unstructured patient data and content cannot be accessed by clinical stakeholders for evaluation and analysis.
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 historic 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, and it is necessary to structure the data.
Aside from financial and security concerns, another issue with unstructured data is its impact on digital transformation. 73.4% of companies report difficulties adopting Big Data Analytics and AI initiatives. AI and Blockchain are transforming clinical trials and drug discovery in the pharmaceutical industry. Big Data lays the groundwork for many of these breakthroughs. But a long-standing problem with unstructured data must be solved for innovations like AI to reach their full potential.
Three Strategies for Tackling Unstructured Data Growth
To effectively tackle unstructured data growth, there are several tips that can help. One of the most important is reducing data storage costs. This can be achieved by adopting a more efficient storage system or implementing compression techniques to reduce the amount of physical space required for storing data.
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 when it comes to 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.
- Reducing Data Storage Costs
As businesses generate and collect vast amounts of unstructured data, one of the biggest challenges they face is managing the cost of storing that data. Unstructured data, such as images, videos, and social media posts, can be difficult to store and manage efficiently. 50%* of an organization’s retained information has no business value, and a large company with 10 petabytes of data could be spending as much as $34.5* million on data that could be deleted. The difficulty lies in distinguishing which data should be deleted, which should be retained, and which should be archived.
Here are seven tips on how to reduce data storage costs when dealing with massive amounts of unstructured data:
- Use Metadata Analytics: Metadata analytics is a process of analyzing metadata, which is data that describes other data. In the context of unstructured data, metadata can include information such as file type, file size, creation date, modification date, author, and location. Metadata analytics involves using tools and techniques to extract, process, and analyze this metadata in order to gain insights into the data it describes. Metadata analytics can be used to identify patterns and trends in unstructured data, providing organizations with valuable insights into their data. For example, by analyzing the metadata associated with a large collection of documents, organizations can identify which documents are most frequently accessed, which ones are redundant, and which ones are potentially valuable but have not been fully utilized. This information can be used to make decisions about data retention, archiving, or deletion, helping to reduce data sprawl. Metadata analytics can also be used to improve data governance by enabling organizations to track and monitor data access, usage, and modification. By analyzing metadata associated with user access logs, organizations can identify potential data breaches, unauthorized access, and misuse.
- Use a Tiered Storage System: A tiered storage system is a storage architecture that uses multiple tiers of storage media to manage and store data based on its access frequency and value. The most frequently accessed or critical data is typically stored on high-performance, expensive storage media, such as solid-state drives (SSDs) or high-speed disks, while less frequently accessed or less critical data is stored on lower-cost media, such as tape or cloud storage. The data is automatically moved between tiers based on policies that are defined by the organization, with the goal of optimizing performance, cost, and capacity utilization. Tiered storage systems help organizations balance their storage costs and performance requirements by ensuring that data is stored on the most appropriate media at the most cost-effective price point.
- Use Data Archiving: Not all unstructured data needs to be stored for long periods of time. Implementing a data archiving strategy can help you identify and move less frequently accessed data to lower-cost storage tiers. Data archiving is the process of moving data that is no longer actively used to a separate storage location for long-term retention. This data is typically stored on lower-cost and less frequently accessed storage media, such as tape, optical discs, or cloud-based object storage. Data archiving is typically used for data that have historical or reference value but are no longer needed for daily operations. This can include data such as old email messages, customer records, financial records, and other documents. Archiving data offers several benefits, including reducing storage costs by moving data to lower-cost storage tiers, improving system performance by freeing up space on primary storage systems and ensuring compliance with regulatory requirements for data retention. Data archiving can be implemented through various methods, including hierarchical storage management (HSM) systems, which automatically move data to different storage tiers based on usage patterns, or through manual archiving processes, which involve identifying and manually moving data to an archive storage location.
- Use Data Compression and Deduplication: Data compression and deduplication are two techniques used to curtail unstructured data sprawl. Data compression is the process of reducing the size of data by encoding it in a more efficient way. This is achieved by removing redundancies in the data and representing it in a more compact form. By compressing data, less storage space is required, reducing storage costs and helping to curb unstructured data sprawl. Deduplication, on the other hand, is the process of identifying and removing duplicate copies of data. This can be accomplished through various techniques, such as file-level deduplication, which identifies and removes identical files, and block-level deduplication, which identifies and removes duplicate blocks of data within files. By removing redundant data, less storage space is required, reducing storage costs and helping to curb unstructured data sprawl. Together, data compression and deduplication can significantly reduce the amount of storage space required to store unstructured data, helping to manage data sprawl. These techniques are often used in conjunction with other data management practices, such as tiered storage and data archiving, to optimize storage infrastructure and control costs. However, it’s important to note that data compression and deduplication can also have performance implications, especially when working with large amounts of data. Therefore, it’s important to balance storage optimization with performance requirements when implementing these techniques.
- Use Object Storage: Object storage is a type of storage architecture that is designed to store large amounts of unstructured data. It stores data as objects rather than as files or blocks and provides a scalable and cost-effective storage solution. In object storage, data is stored in discrete units called objects, which contain both data and metadata describing the object. Each object is assigned a unique identifier, known as an object identifier (OID), which is used to retrieve and manage the object. Object storage systems are typically highly scalable and can store petabytes or even exabytes of data, making them well-suited for managing unstructured data sprawl and curtailing it in several ways. Firstly, object storage systems are designed to store large amounts of unstructured data in a highly scalable and efficient manner. This means that businesses can store large amounts of unstructured data without worrying about running out of storage space, helping to reduce data sprawl. Secondly, they typically offer advanced data management capabilities, such as data tiering and data archiving. By using these features, businesses can automatically move data to different storage tiers based on usage patterns or archive data that is no longer frequently accessed. This helps to optimize the storage infrastructure, reduce storage costs, and control data sprawl. Lastly, object storage systems typically offer advanced data protection features, such as data replication and erasure coding. By using these features, businesses can protect against data loss and ensure data availability, reducing the risk of data sprawl due to data loss or corruption.
- Use Cloud Storage: Cloud storage offers a cost-effective way to store unstructured data. It refers to the storage of data on remote servers, which can be accessed over the Internet. This is different from traditional on-premises storage, where data is stored on local servers and devices. Cloud storage services are typically provided by third-party companies like Microsoft, Google, and Amazon, who offer a range of storage options, from free or low-cost plans with limited storage capacity, to more expensive plans with greater capacity and advanced features. By moving and tiering data in the cloud, enterprises can leverage a centralized location for storing and managing all types of data, regardless of where it originates. They can reduce the amount of storage required on local devices, which can help to free up space and improve performance. Additionally, cloud storage services often offer advanced features for organizing, searching, and analyzing data, which can help to make it more manageable and accessible. Finally, it can provide better security and data protection than local storage since cloud providers typically employ advanced security measures and backup systems to protect data from loss or theft.
- Implement a Data Lifecycle Management Strategy: Data Lifecycle Management (DLM) is a strategy that defines how data should be managed throughout its lifecycle, from creation to deletion. It involves managing data according to its value to the organization so that it is used effectively and efficiently while maintaining compliance with legal and regulatory requirements. DLM helps limit unstructured data sprawl by providing a systematic approach to managing data. Unstructured data sprawl occurs when data is created and stored without a clear plan or strategy for how it will be managed over time. This can result in data duplication, data inconsistencies, and wasted resources. DLM helps to avoid unstructured data sprawl by defining clear policies for how data should be managed throughout its lifecycle. This includes defining retention periods for data based on its value, establishing procedures for archiving and deleting data, and defining roles and responsibilities for managing data. By implementing a DLM strategy, organizations can ensure that data is managed in a consistent and structured manner, reducing the risk of unstructured data sprawl. This helps to improve data quality, reduce storage costs, and ensure compliance with legal and regulatory requirements.
- 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 will be covered by the policies, 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 where improvements are needed. Establish procedures for addressing non-compliance and updating the policies as needed.
- Regularly review the policies: Regularly review the policies 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.
What’s more, automation can also involve the use of artificial intelligence and machine learning technologies to automate tasks such as data classification, data matching, and data enrichment. These technologies can help organizations to 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.
(Check out Data Dynamics’ Unified Data Management Platform to know more)
Build, Buy, or Outsource Data Management – What’s the best way ahead
When it comes to managing unstructured data growth, businesses may find themselves wondering whether they should build, buy or outsource their data management systems. Each option has its pros and cons that need to be carefully considered before making a decision.
Building a data management system in-house 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 manage 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, helping organizations structure their unstructured data with their Unified Unstructured Data Management Platform. The platform encompasses four modules- Data Analytics, Mobility, Security, and Compliance.
Proven in over 28 Fortune 100 organizations, the Platform 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 platform 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 and optimize costs, please visit – www.datadynamicsinc.com or contact us at email@example.com I (713)-491-4298.