How Pharma CI Team Structures impacts the Choice of CI Platforms?

Historically, strategy teams must deal with unfathomable amount of data and it’s not just a competitive intelligence (CI) platform that does the trick in processing the data for insights. The structure of CI teams is also very crucial here in the way data is managed. The kind of a CI team structure affects the way insights and intelligence from all departments in an organization is curated and analyzed. The CI or strategy team decides how activities like information collection and organization, analysis and implementation are carried out.

Till very recently, pharma companies and life sciences majors were not aware of using the services of an analytical CI platform. Recently, in a 2021 Forrester survey with Cipher, pharmaceutical and biotechnology, medical device manufacturers globally reported a marked benefit of using CI platforms that better managed third-party vendor progress and management of data and business intelligence. Nearly 57% of firms surveyed globally said that they had greater visibility into the insights generated by the comprehensive CI tools used by the organization.

According to Forrester, most CI teams want their analysts to focus of high-value tasks and allow automation to investigate the low-value support that is critical in business intelligence. This depends on the kind of CI platform these teams invest in, with pharma companies focusing on CI team formations while managing its relationship with CI platforms, thereby avoiding the age-old drawbacks in CI research.

Competitive intelligence teams in life sciences companies are organized and defined by a wide range of variables such as budget and corporate structure. The role of CI team is determined by its control structure. Most CI teams are either centralized or decentralized and that determines how strategy teams work and perform in the larger organization.

In a centralized structure, a CI team functions as a standalone unit within the wider business. A centralized CI team reports directly to the senior management and interfaces with various therapeutic groups and service providers. In large life sciences companies, centralized CI teams consist of nearly 15 to 20 people. Everyone is responsible for running the CI of separate therapeutic groups such as oncology drugs and infectious diseases. Each individual works in cohesion with the other in the centralized CI team environment and is responsible for the competitive intelligence strategy of the entire company. If a company has a central CI unit, analysts can avoid overlapping of data research on the same dataset twice and eliminate redundancies by centralizing resources and sharing of knowledge. Many pharma companies also find the centralized team structure and a centralized CI platform devoid of bureaucratic inefficiencies. The use of a centralized CI platform streamlines the vendor management and makes it more efficient creating economies of scale in external partnerships.

There is easy sharing of insights and analysis with best practices been distributed among various therapeutic groups. Centralized CI teams also enable unified reporting to senior management with increased organizational visibility. In pharma companies that have a centralized CI team, the need for an organization-wide central repository of real-time data feeds as well as static information makes it imperative to invest in analytical tools that manage and utilize disparate and tactical data across different organizational units. Centralized CI knowledge repositories effectively help organizations collaborate and strategize on data that point solutions may not offer otherwise. Centralized CI platforms and tools transcends the individual silo and helps bridge departmental and geographical divides by functioning as a central data processing intelligence hub.

In a decentralized approach to CI in pharma teams, the CI professionals are in each therapeutic group like one in oncology drugs and another in infectious diseases for instance. Each CI professional reports to the leader of the individual business unit and operates in silos with little synergy between departments. The use of a centralized CI platform is often halted in this set up. Pharma companies end up paying multiple times for the same data and software rather than effectively sharing resources. Decentralized pharma CI teams are closer to each therapeutic area but are wracked by inefficiencies and duplication in key processes. Decentralized CI teams often used specialized tools that are designed to add focused value against a defined set of activities in market intelligence. These specialized tools might provide a single, specific type of information like news, IP data or even financials, focusing on specific aspects of the CI workflow. But a decentralized structure often tries to cobble together an overall solution from multiple point solutions and generate insight from multiple source locations and systems which could be erroneous.

Centralized pharma CI teams have a CI platform that has a unified reporting structure that enables teams to codify all knowledge in a central location with higher visibility organizationally. Pivoting away from a decentralized approach also enables CI teams to get higher returns on their investment on CI tools. Centralized CI platforms consolidate data into a central hub and produce actionable insights to help teams visualize data and conduct analysis.

A centralized CI team also streamlines data management and avoids duplicitous software licensing and data sourcing agreements thereby reducing the expenses of the company greatly. A centralized data sharing arrangement and unified reporting also makes analysis and best practices among therapeutic groups easy and effortless. There is greater organizational visibility and better management of resources.

Centralized management and platforms may also offer a few cons. Therapeutic groups may often feel further removed from the entire insight generation process and without tier-level leadership with top heavy managers. CI functions may also become quite insular within the entire organization without strong relationships with all members in an organization.

While times are changing for decentralized market intelligence teams, there are a few advantages to this model. Since each organizational unit has an intelligence team, therapeutic teams can build closer relationships with all stakeholders at every level. As a function of increased proximity, decentralized CI teams have a far greater ability to react to changing business needs and can share updates regularly with their counterparts in other teams and within their own unit.

Decentralized CI teams also suffer from pilferage of data and inefficient management of resources. There is often limited scope for growth for CI professionals within the larger organization and best practices are not shared widely among various functionaries in a company.

Centralizing the CI teams alongside Technology stack investments into a single CI platform remains critical. Centralized CI analytical platforms consolidates data from disparate sources like scientific journals, regulators, third party data collection companies, conferences, and news sites to produce actionable insights after visualizing data and conducting analyses. Without a centralized CI platform, data collection and parsing become tedious for CI teams. Centralized CI platforms also automate many time-consuming and relatively low value CI work such as organizing, collecting, and curating data. Centralized CI teams also free up expensive human resources to tackle the other CI challenges organizationally.

Most centralized CI platforms deal with enormous qualifiable information and offer a single, integrated, and intelligent solution that helps predict market activities in the long run. Traditional patchwork of point solutions often is not able to present the single source of truth to CI teams and professionals. Augmented intelligence (AI) has a significant impact on insight generation and building business intelligence knowledge for companies. The data to insights journey is accelerated by leveraging pre-built visualizations and fast customizations spearheaded by AI. The returns are significantly higher when an AI-powered CI tool is used for data management and insight generation. Predictive AI-powered CI tools that are centralized, often help data processes to be more democratized, reducing human agency and costs, thereby generating a higher opportunity cost. Therefore, the technology investments in such intelligent solutions are also now on a higher side.