Why a Cloud Data Management Journey Necessary for Small and Medium Businesses (SMBs) in Pharma?

The cloud remains at the forefront of digital transformation efforts across industries. But compared to other consumer-focused industries, pharmaceutical and biotechnology companies have been slower to adopt emerging cloud technologies. According to Accenture, compared to eleven industries surveyed, biopharma ranked the lowest in achieving innovation, data access, and analytics goals from the cloud. Despite these historical trends, the pharma industry has been steadily embracing cloud computing platforms in the recent past. According to Healthcare Information and Management Systems and Society report, over 83 percent of pharmaceutical companies, including small and medium businesses (SMBs) use cloud technologies. The global cloud computing in pharmaceutical market revenue was pegged at $3173 million in 2019, with an expected target of $7021 million in 2025 with a CAGR of 14.15% during 2020-2025.

SMBs, like their bigger counterparts in pharma, generate data at every stage of their drug development journeys. Most require an enterprise-grade cloud data solution to generate actionable business insights across drug discovery, clinical trials, marketing, and drug launches. Existing data architectures ingest data from multiple sources, design algorithms to derive insights, but can be expensive to maintain. Most SMBs are on the lookout for a cost-efficient data management solution that will help them transform their existing data journeys and need an overall digital strategy that runs through the entire enterprise.

In the latest Accenture Cloud Survey, most small and medium pharma companies highlighted their key concerns of a lack of running an integrated business and product strategies in every step of their treatment journey. Most were concerned about sustainable operations from the top-down in the short run rather than business enablement, technological upgrades, and resilience for the long haul.

These companies perennially face constraints with legacy solutions, data, and organizational silos. Many also need a dedicated infrastructure and database to support to avoid a fragmented approach to information processing at each stage of the drug commercialization journey.

Traditional data management tools also are less scalable and adaptable. Many pharma leaders also complain of security hazards with some solutions. Here are some of the problems that on-premises data management journeys experience.

Higher Implementation Time
In a biopharma organization, onboarding data assets, data cleansing, data integration, and provisioning form part of a very complex process that runs across multiple departments or projects. This is not just time-consuming for an organization but also increases the overall operating cost.

Outdated Technologies
The traditional data management technologies were set up years ago and the hardware in these systems can create scalability issues. These also do not use parallel processing and in-memory storage thus hampering data processing capabilities. Traditional data management tools also cannot scale up on-premises data storage capabilities without incurring very high costs.

Inflexible and High Failure Rates
Legacy enterprise data management architecture is not agile and adaptable. It cannot change on demand and can become a bottleneck when meeting business requirements. For instance, a basic change to a data model can take weeks and months to complete, costing the company thousands of dollars. These data management solutions also often fail to meet user requirements and needs and have high failure rates.

High Infrastructure Procurement and Maintenance Cost
In a legacy environment, organizations procure dedicated machines, licenses, tools for each department or even individual projects and invest in support teams who maintain the infrastructure. Moreover, since the procured infrastructure is not shared among departments, it is not used to its full capacity and can increase the overall cost of maintenance.

Recurring Cost on Data Quality
Data quality is crucial for pharma organizations to derive actionable insights for decision-making. But most of the data quality processes are carried out at various departments and there is limited collaboration at the enterprise level. Most departmental data that is generated exists in silos and it is not cost-efficient to process this for organizational foresight.

Duplicate Data Assets
Individual departments often procure data assets considering only their requirements and since its metadata is not maintained organization-wide, companies end up procuring the same type of data assets from different vendors. This increases overall costs and creates disparate sources for the same information.

Lack of Standardization
When the data assets are acquired and processed at the department level, project managers, data stewards, and the development teams tend to use their nomenclature which results in a different set of standards across the same organization. This usually becomes a major roadblock during data integration or migration at the broader enterprise level.

Ineffective Collaboration
Teams working on different tools and technologies in the same organization can find it difficult to collaborate as their ideas and solutions can be restricted to their skills. An enterprise-wide technology platform enables teams to work together and improve efficiency.

Increasingly in the light of these drawbacks, SMBs in pharmaceutical organizations are more likely to implement cloud data management and storage facilities because of reduced maintenance costs with a flexible and scalable data management process in comparison to on-premises deployments. This assures them of a seamless data management journey that on-premises arrangements fail to offer. Virtualized data storage and management platforms also enable interoperability data that is hard to find traditional solutions. Cloud platforms are also useful when migrating data and SMBs don’t need to maintain separate datasets thereby cutting down effort and spending drastically.