D
Don Miller
Guest
Progress Software is an Organizational Member of the Pistoia Alliance. For life sciences R&D teams, the relationship matters because many of the obstacles to useful data and AI extend beyond one company’s systems. Research information moves among laboratories, partners and disciplines, yet the meaning of a term, the source of a finding or the rules for using a dataset may not move with it. Shared approaches can make that information easier to find, interpret and reuse. Membership gives Progress a place to listen to those challenges and contribute its perspective on data, context and governance.
The Pistoia Alliance is a global, not-for-profit members organization that brings together the biopharmaceutical industry and other participants in research. According to the Alliance, its membership includes more than 200 companies. Members pool expertise and R&D resources to address pre-competitive challenges that recur across organizations, without exchanging competitively sensitive data.
The work often begins where one organization’s research meets another’s data practices. If teams use different identifiers for the same substance or record experimental data in incompatible ways, each transfer between systems can require translation and review. Common practices and standards give researchers a clearer starting point. The Alliance supports collaboration through projects, expert communities, events and training. Its membership is diverse, so a proposed approach can be examined from the perspectives of research, technology and the wider life sciences community.
Progress joined to engage with the people working through these shared R&D data challenges. The membership creates opportunities to understand how priorities are changing across life sciences, contribute practical knowledge about complex data and learn from organizations that produce, manage and use scientific information every day.
For Progress, the relevant questions are concrete. How can a researcher follow the evidence trail when reports, experimental records and reference data live in different systems? How can the meaning of a scientific term travel with the information when another team reuses it? What context should accompany an AI-generated answer so a person can examine its source? These are questions that benefit from discussion across organizational boundaries. A software provider can contribute experience with data architecture and governance while learning where existing approaches create work for researchers and informatics teams.
Membership gives Progress a forum for that exchange. It should not be taken as a claim that Progress participates in any particular Alliance project or has determined the direction of an industry standard. Specific contributions deserve their own evidence and explanation when they are available.
Life sciences data rarely arrives in one uniform shape. A study may leave evidence across structured measurements, laboratory documents, published research and reference terminology. A useful answer brings those sources into view while retaining what each record means and where it came from. Without consistent identifiers, definitions and provenance, teams can spend time reconciling information before they can assess it.
The Alliance’s strategic priorities include improving how the industry manages and standardizes data, supporting FAIR data practices and exploring responsible AI adoption in R&D. FAIR refers to data that is findable, accessible, interoperable and reusable. For a research team, those principles call for more than collecting files in one location. People need enough shared context to understand whether two terms refer to the same concept, whether data is appropriate for a new analysis and which source supports a conclusion.
AI makes these questions more visible. A system may retrieve a plausible passage while missing a critical relationship or the provenance needed to evaluate it. Semantic interoperability carries shared meaning across sources and helps people and systems interpret them consistently. Governance and lineage leave a trace that teams can follow to see how information was assembled and applied. Neither a common vocabulary nor a data platform removes the need for scientific judgment, but both can make that judgment better informed.
The Progress Data Platform addresses a related set of enterprise data problems. Progress describes it as connecting data across formats and systems, adding semantic context and applying governance so information can be used in AI and analytics workflows. Its pharma and life sciences overview explains how these capabilities can bring structured and unstructured scientific content into a shared view while retaining context, lineage and provenance.
Consider a researcher looking for prior evidence about a compound. Relevant material might sit in an experiment record, a document repository and a reference database under different names or identifiers. Connecting those sources makes them available to a common workflow; semantic models describe relationships among the concepts; lineage helps a reviewer follow an answer back to its supporting material. The architecture and controls take shape around each organization’s data and requirements. The practical aim is to make research information more usable while keeping the evidence behind an AI answer within reach of a reviewer.
The platform and the Alliance address overlapping challenges, but they have different roles. Progress offers software that organizations can evaluate for their own workflows. The Alliance brings members together around shared industry questions. Membership does not mean the Pistoia Alliance endorses, certifies, approves or gives preferred-vendor status to the Progress Data Platform or any other Progress product.
Industry engagement can help Progress keep its conversations with life sciences customers grounded in the problems they face: disconnected scientific knowledge, inconsistent terminology and the work of tracing evidence across research processes. It also gives Progress a way to hear how the broader community approaches data practices and emerging AI use cases. Customers can use those conversations to test whether a proposed architecture fits their own sources, teams and review needs.
Membership alone does not establish a product outcome, change a roadmap or replace the careful work of a customer evaluation. Its value grows through sustained dialogue and clear explanations of where Progress technology may help with a particular data challenge. A team deciding how to connect research information still needs to examine its source systems, terminology, governance requirements and intended uses before choosing an approach.
Progress Software’s membership reflects a commitment to learn from and contribute to the life sciences community’s work on shared data challenges. When information carries its meaning and provenance across systems, teams have a stronger basis for evaluating research evidence and the AI tools that draw on it. That work depends on collaboration as well as technology.
Explore the Progress Data Platform to see how Progress approaches connected data, semantic context and governance for enterprise AI and analytics.
Continue reading...
What the Pistoia Alliance Does for Life Sciences R&D
The Pistoia Alliance is a global, not-for-profit members organization that brings together the biopharmaceutical industry and other participants in research. According to the Alliance, its membership includes more than 200 companies. Members pool expertise and R&D resources to address pre-competitive challenges that recur across organizations, without exchanging competitively sensitive data.
The work often begins where one organization’s research meets another’s data practices. If teams use different identifiers for the same substance or record experimental data in incompatible ways, each transfer between systems can require translation and review. Common practices and standards give researchers a clearer starting point. The Alliance supports collaboration through projects, expert communities, events and training. Its membership is diverse, so a proposed approach can be examined from the perspectives of research, technology and the wider life sciences community.
Why Progress Software Joined the Pistoia Alliance
Progress joined to engage with the people working through these shared R&D data challenges. The membership creates opportunities to understand how priorities are changing across life sciences, contribute practical knowledge about complex data and learn from organizations that produce, manage and use scientific information every day.
For Progress, the relevant questions are concrete. How can a researcher follow the evidence trail when reports, experimental records and reference data live in different systems? How can the meaning of a scientific term travel with the information when another team reuses it? What context should accompany an AI-generated answer so a person can examine its source? These are questions that benefit from discussion across organizational boundaries. A software provider can contribute experience with data architecture and governance while learning where existing approaches create work for researchers and informatics teams.
Membership gives Progress a forum for that exchange. It should not be taken as a claim that Progress participates in any particular Alliance project or has determined the direction of an industry standard. Specific contributions deserve their own evidence and explanation when they are available.
The Shared Data Challenges Behind the Membership
Life sciences data rarely arrives in one uniform shape. A study may leave evidence across structured measurements, laboratory documents, published research and reference terminology. A useful answer brings those sources into view while retaining what each record means and where it came from. Without consistent identifiers, definitions and provenance, teams can spend time reconciling information before they can assess it.
The Alliance’s strategic priorities include improving how the industry manages and standardizes data, supporting FAIR data practices and exploring responsible AI adoption in R&D. FAIR refers to data that is findable, accessible, interoperable and reusable. For a research team, those principles call for more than collecting files in one location. People need enough shared context to understand whether two terms refer to the same concept, whether data is appropriate for a new analysis and which source supports a conclusion.
AI makes these questions more visible. A system may retrieve a plausible passage while missing a critical relationship or the provenance needed to evaluate it. Semantic interoperability carries shared meaning across sources and helps people and systems interpret them consistently. Governance and lineage leave a trace that teams can follow to see how information was assembled and applied. Neither a common vocabulary nor a data platform removes the need for scientific judgment, but both can make that judgment better informed.
How the Progress Data Platform Supports Scientific Context
The Progress Data Platform addresses a related set of enterprise data problems. Progress describes it as connecting data across formats and systems, adding semantic context and applying governance so information can be used in AI and analytics workflows. Its pharma and life sciences overview explains how these capabilities can bring structured and unstructured scientific content into a shared view while retaining context, lineage and provenance.
Consider a researcher looking for prior evidence about a compound. Relevant material might sit in an experiment record, a document repository and a reference database under different names or identifiers. Connecting those sources makes them available to a common workflow; semantic models describe relationships among the concepts; lineage helps a reviewer follow an answer back to its supporting material. The architecture and controls take shape around each organization’s data and requirements. The practical aim is to make research information more usable while keeping the evidence behind an AI answer within reach of a reviewer.
The platform and the Alliance address overlapping challenges, but they have different roles. Progress offers software that organizations can evaluate for their own workflows. The Alliance brings members together around shared industry questions. Membership does not mean the Pistoia Alliance endorses, certifies, approves or gives preferred-vendor status to the Progress Data Platform or any other Progress product.
What Participation Means for Progress Customers
Industry engagement can help Progress keep its conversations with life sciences customers grounded in the problems they face: disconnected scientific knowledge, inconsistent terminology and the work of tracing evidence across research processes. It also gives Progress a way to hear how the broader community approaches data practices and emerging AI use cases. Customers can use those conversations to test whether a proposed architecture fits their own sources, teams and review needs.
Membership alone does not establish a product outcome, change a roadmap or replace the careful work of a customer evaluation. Its value grows through sustained dialogue and clear explanations of where Progress technology may help with a particular data challenge. A team deciding how to connect research information still needs to examine its source systems, terminology, governance requirements and intended uses before choosing an approach.
Advancing Life Sciences R&D Through Collaboration
Progress Software’s membership reflects a commitment to learn from and contribute to the life sciences community’s work on shared data challenges. When information carries its meaning and provenance across systems, teams have a stronger basis for evaluating research evidence and the AI tools that draw on it. That work depends on collaboration as well as technology.
Explore the Progress Data Platform to see how Progress approaches connected data, semantic context and governance for enterprise AI and analytics.
Continue reading...