TL;DR | The Highlights
- Life sciences organizations generate extraordinary amounts of data, but the value of that data depends on whether teams can find, trust, and use it across functions.
- Cloud storage and analytics are essential foundations. The next challenge is connecting insights to the people and processes that act on them.
- Better connected data can improve decisions across clinical operations, supply planning, patient services, and commercial engagement. Each use case requires its own measures of value and appropriate controls.
- Salesforce Data 360, Life Sciences Cloud, Health Cloud, and Agentforce can support parts of this work, particularly where data needs to inform coordinated engagement and action.
- In a regulated environment, data access, governance, validation, and change management must be designed alongside the use case.
Life sciences has made remarkable progress in what it can measure. Clinical, genomic, imaging, manufacturing, and commercial data can reveal patterns that were difficult to see even a few years ago. Yet producing more information does not automatically make an organization better at using it. If it wasn’t obvious, yes, this is an operations problem.
Consider a question that spans several teams: Which patients are experiencing delays in starting therapy, and where is the delay occurring? Setting aside government regulations and healthcare policies outside the business’s control, the answer may depend on data held by a patient services team, a specialty pharmacy, an access program, and a commercial organization. Each team may understand its part of the journey. Bringing those views together, with the right permissions and context, is considerably harder.
That is the challenge facing many business leaders within health and life sciences organizations today. Data transformation is no longer simply a matter of collecting and storing more information. It is about making reliable information available at the point where a decision or handoff occurs.
The challenge has outgrown any one system
Bringing a therapy or device to market involves a long sequence of decisions across discovery, clinical development, regulatory affairs, manufacturing, supply chain, and commercialization. These functions have different responsibilities and good reasons to use specialized systems. A clinical trial platform, an enterprise resource planning (ERP) system, and a customer relationship management (CRM) platform were built to do different jobs.
The difficulty arises when work crosses the boundaries between them.
Teams may still depend on spreadsheets, manual exports, and standalone reporting tools to reconcile data. A discrepancy that looks small in a dashboard can take days to investigate when its source, definition, or latest update is unclear. That delay affects more than reporting. It can slow planning, obscure a supply risk, or leave a patient services team without the context it needs to resolve a case.
The goal should not be to force every function into one application. It should be to establish which system owns each record, how information moves between systems, and what each team is authorized to see and do with it. That is as much an operating model decision as a technology decision.
Cloud infrastructure creates capacity. Connected workflows create value.
The case for cloud based data and analytics platforms remains strong. Data lakes can accommodate large volumes of structured and unstructured information. Warehouses can support curated, governed analysis. Analytics and machine learning can help teams identify relationships, forecast demand, and investigate patterns at a scale that manual processes cannot match.
Those capabilities matter in life sciences, where a useful insight may depend on information from several domains. But a well designed data platform does not, by itself, change what happens next. An insight has limited value if it reaches the right person too late, lacks the context needed to act, or cannot be used within an approved workflow.
This is where an organization should move beyond the broad ambition of a “single source of truth.” In practice, different systems remain authoritative for different kinds of data. What leaders need is a trusted, governed view of the information required for a particular decision, with its source and meaning intact.
For organizations using Salesforce, Data 360 can help connect data from Salesforce and external platforms, including supported zero copy connections to platforms such as Snowflake and Databricks. That can make existing data more useful in operational workflows without requiring every source to be replaced. The architecture still needs careful design: teams must decide which data is needed, how current it must be, and what access and visibility is appropriate.
Where better connected data can make a difference
The strongest transformation programs begin with a consequential question, then work backward to the data and processes needed to answer it.
Clinical and development operations. Bringing together relevant trial, site, and operational information can help teams identify recruitment or execution issues sooner. Research datasets require particular care: access, interpretation, and permitted use must reflect the scientific and regulatory context. The objective is to help teams make better decisions, not to imply that connecting systems alone will shorten drug development.
Supply chain and manufacturing. Better visibility across suppliers, inventory, distribution, and demand can help teams anticipate disruptions and plan more effectively. For temperature sensitive products, timely information can be especially consequential. ERP and manufacturing systems remain central to the underlying transactions; connected analytics can help leaders see how events across those systems affect availability and service.
Patient services and care coordination. A patient’s experience can involve enrollment, access support, education, and ongoing assistance. When information is fragmented, people may have to repeat details while teams reconstruct the status of a case. Health Cloud and Life Sciences Cloud can support coordinated workflows where they fit the organization’s model, helping authorized teams work with relevant context. The value lies in resolving a specific service gap while respecting consent, privacy, and the boundaries between functions.
Medical and commercial engagement. Field and account teams need an accurate view of previous interactions, current priorities, and approved information. Life Sciences Cloud can support planning and engagement across these teams. Better data can help someone prepare for a useful conversation; it does not remove the need for appropriate review, clear roles, or sound professional judgment.
Across all four areas, the measure of success should be concrete. How long does it take to answer a critical question? How often does a handoff require manual reconciliation? How early can a team identify an issue? Those measures tell leaders far more than the volume of data brought into a platform.
AI raises the value of good data; and the cost of weak foundations
The conversation has changed in the last few years. Generative and agentic AI have expanded what organizations can do with information in day to day work. An agent might help a service team find relevant case history, prepare a field colleague for a visit, or route a request through an established process. Agentforce and AI agents for life sciences can be considered for those kinds of defined workflows when it is grounded in the right data and configured with appropriate permissions and controls.
That promise also makes longstanding data problems more visible. If records conflict, ownership is unclear, or access rules are too broad, an agent can carry those weaknesses into its response or action. In life sciences, the answer is to be precise about the job an agent is allowed to perform: what information it may use, which steps it may take, when a person must review its work, and how the organization will monitor the outcome.
AI should enter through a use case with a clear owner and a measurable result. A narrowly scoped workflow that reliably removes friction can create more value than an ambitious deployment that teams cannot confidently govern.
Build for the regulatory environment from the start
Life sciences organizations cannot treat governance as a final implementation task. Depending on the use case, a system may need to meet requirements concerning electronic records, audit trails, privacy, GxP processes, or validated software. The obligations are not identical across research, manufacturing, patient services, and commercial work, so the controls cannot be identical either.
This has practical implications for implementation. Teams need to document intended use, determine which records are authoritative, define access by role, test the workflows that matter, and manage changes after launch. They also need to understand where a platform supports a regulated process and where a specialized system remains the system of record.
That discipline should enable progress. When the use case and its risks are clear, an organization can apply the appropriate level of rigor and move with greater confidence.
Start with the decision you need to improve
A data and analytics transformation can become unwieldy when it begins with the instruction to “connect everything.” It’s also apparent to the majority that transformation within a highly regulated industry takes some time. A stronger starting point is determining one decision or handoff that matters to the business.
Identify the people involved, the systems they use, and the information they currently have to gather by hand. Establish how success will be measured. Then design the data connections, workflow, and controls around that outcome. The result can become a repeatable foundation for the next use case.
That is where the right implementation partner earns its place. Technology expertise matters, but so does the ability to understand how clinical, operational, patient facing, and commercial teams actually work; and to translate those realities into an architecture that can be governed and extended.
At Lane Four, we help organizations connect business needs to the Salesforce architecture and workflows that support them. For life sciences leaders, the opportunity is substantial: make the data you already hold more useful to the people making decisions, serving patients, and moving important work forward. Let’s chat.