Master Data Management for Growing Biotech Startups
Keeping pace with the growing need for master data management (MDM) for biotech startups that run clinical trials, monitor patient IoT devices, and model large data sets is likely to become increasingly challenging in the next few years.
The global volume of data generated by human activity is growing at rates impossible for the mind to adequately visualize, having doubled in the last three years – ballooning from 41 zettabytes in 2019 to more than 97 today – and remaining on pace to do so again by 2026. In some industries, the rate of data accumulation is accelerating faster than in others. As of 2022, the healthcare industry – including the biotech and life science companies – accounts for 30% of the daily data added to the global datasphere. By 2025, 36% will be healthcare data, outpacing financial services and entertainment media by 10%.
The global healthcare data management market had a value of $32.9 billion in Q4 of 2021. According to current projections, this value will triple to $105.73 billion over the coming decade, maintaining a compound annual growth rate (CAGR) of 13.85% through 2030. Rising demand for better analytical data modeling in both research and development and in patient care will largely drive market growth in this area.
For biotech companies and other healthcare research organizations, the proliferation of cloud-based data services has made master data management a critical capability for the industry’s coming challenges. In this guide, you’ll learn what master data management is and how it can help biotech companies.
- The global data sphere is growing at a frenzied pace, and within it, the share of data created by healthcare organizations in R&D and operations is overtaking other industries.
- In the coming years, biotech startups will increasingly rely on specialized data skills such as master data management to maintain clean, consistent data that delivers reliable results.
- Startups have more options and mobility in addressing the evolving data management needs of the healthcare industry. Taking the right steps early on can prevent many serious problems down the road.
What Is Master Data Management?
Master data management (MDM) refers to unifying disparate data sources within an organization’s larger, distributed IT system. MDM is an indispensable part of effective data management in any organization that handles large volumes of data across multiple sources. Specifically, MDM ensures data quality by creating consistent rules for data identifiers and other data attributes so that redundancies, variances, and gaps don’t skew extracted values.
MDM also commonly includes processes to facilitate data sharing between arrays of platforms and software-as-a-service applications. As today’s enterprises use an average of 364 SaaS applications, keeping organizational data clean, consistent, and visible within a single source of truth has become more complicated than managing localized IT systems a few decades ago. Successfully applied MDM renders the data fed into analytical programs more reliable, thereby improving the quality of derived insights.
What Is Master Data?
Data scientists often refer to master data as the golden record of information within a data domain. In data science, domains are categories of data types, and they vary widely by industry and use. Businesses typically have domains in their data for customers, products, and services. In healthcare organizations, common domains would include patients, appointments, and provider networks, among many others.
Organizations must record and persist multiple instances of the same data points in various systems. For example, many different departments and applications will need to associate data points such as names and contact information with customers or patients. Over time, inconsistencies or changes to data in systems that do not communicate accumulate. Master data management tools attempt to resolve variances between disparate systems that contribute to the same domain, creating an authoritative master record for that domain.
4 MDM Tips for Biotech Startups
Image Source: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/gx-lshc-dei-global-life-sciences-outlook-report.pdf
After taking a beating during the pandemic shutdowns in 2020, biotech and pharmaceutical R&D showed strong internal rates of return (IRRs) throughout 2021. Average IRR closed out the year in 2021 at 7%, up 259% from 2020 rates. With the vast potential for growth at stake, startups entering the market should prioritize establishing best practices early on. Building an IT architecture that enables MDM and other data-centric tasks is critical to these.
Here are four MDM tips for biotech startups.
1. Learn From the MDM Struggles of the Industry’s Larger Players
Typically, startups seek to model their practices after established larger organizations. However, in the case of healthcare data management, most major organizations are in a period of transition away from legacy IT systems, currently hindering integration projects. Take the chance to build your IT architecture with integration and interoperability in mind from the ground up.
2. Take Advantage of Data Standards
Powerful new resources for data standards become available every year. Fledgling organizations can seize upon these to obviate countless data integration obstacles down the line. In biotech and pharmaceuticals, open data standards that facilitate MDM include the Clinical Data Interchange Standards Consortium (CDISC) and the International Organization for Standardization’s (ISO) work on the Identification of Medicinal Products (IDMP).
3. Don’t Undervalue IT Architecture
With more cloud-native SaaS applications available every day, vendors know to market eye-catching front-end application features to attract clients. Nevertheless, most rely on data lakes to deliver just-in-time data sets. Depending on the nature of your work, creating system-wide interoperability with this approach may be difficult. Alternatively, startups should attempt to align the IT architecture they adopt with the performance needs of their specific requirements.
4. Limit the Creation of Unstructured Data
Image Source: https://www.intellspot.com/big-data-technologies/
Unstructured data refers to data that doesn’t map onto a relational database and is, therefore, not machine-readable. Early in an organization’s work, it can be tempting to backburner processes for structuring data, such as lab notes in text files, spreadsheets, or audio and video files. Nevertheless, as that data accumulates in storage, so does time and expense of structuring it retroactively.
Healthcare Master Data Management With Coperor by Gaine
Coperor is an ecosystem-wide master data management solution capable of integrating data across all systems and contracted partners. With unparalleled data modeling designed specifically for healthcare applications, Coperor can deliver a single source of truth to your organization.
Contact Gaine today to learn more about MDM and next-gen value.
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