Availability of the right information to the right people at right time is critical for understanding and improving existing processes within an organization. For a data-intensive industry such as biopharmaceuticals development and manufacturing, access to data and information enables companies to understand and streamline their operating and business processes. But most organizations fail to leverage such information for better decisions because they mismanage it. Some try to fill the gap between information and end users by introducing various applications and building data repositories but suffer from inherent complexities involved in integrating disparate sources.

This two-part article explores some related tools and technologies biopharmaceutical companies can leverage to build an efficient mechanism for capturing and delivering valuable information. In this issue, Part 1 focuses on infrastructure selection and how hardware, software, and information systems form an ecosystem. Simplicity, sustainability, and scalability can be achieved only when the trio is designed from a holistic perspective. We further explore structured data capture and analysis tools.

Challenges and Obstacles

Various tools are on the market for information management, but many fail to gain popularity because of low adoption and use rates. Adoption rate refers to the degree to which companies purchase tools for their employees. The major obstacle in the adoption of a tool is the “time and complexity to deploy” it. Most tools have complex architectures that organizations can find difficult to customize for their own specific needs. It can be difficult for them to realize the full benefits of such tools.

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Use rate refers to the extent to which users are using a tool. Once the tool is deployed, the next hurdle companies face is getting their users to make use of it. Tools often fail to gain popularity among end users because of complicated interfaces and slow query response times. The prevalence of other legacy applications also presents a major impediment to the adoption of new tools among end users.

The combination of these adoption and use rates provides criteria to measure and evaluate the usability of information management tools (1). It becomes imperative that information management tools be designed taking user requirements into account so that desired rates can be achieved.

From the Ground Up

Building an effective information management solution requires understanding an organization's data and information landscape. An important aspect of that is related to understanding key business processes and systems for the information management solution. That requires three phases of activity (Figure 1): determining user requirements, establishing architectural goals, and defining the design space.

Figure 1: (Click to enlarge)

Determine User Requirements: The successful design of an information management system depends on deep understanding of processes and systems within an organization. Most software solutions come bundled with many features that never get used to their fullest extent because they fail to engage users. What matters most to end users is an easy path to the correct information — rather than large numbers of features and their related learning curve. On that basis, the main end-user requirements can be explained as simplicity, fast response, and flexibility.

Simplicity: Any solution that presents a simple interface easily becomes popular among end users. Both the Gmail service (http://mail.google.com) and Google search (www.google.com) are classic examples that easily gained good adoption and use rates because they are easy to use. The more complex an interface, the more time it takes for users to learn it, so the more reluctant they become toward adopting that new solution or technology.

For example, an information management system should present a single window for access to its various data sources. The various components should integrate well into one unified platform that serves as a gateway for end users to reach all data sources. Otherwise, it becomes cumbersome and time consuming to collect information from different places.

Fast Response: A data management system should respond quickly to user queries. It should be able to fetch information from different sources and present it in a unified and timely form so end users don't have to wait for queries to execute and results to load.

Flexibility: The system's design should be as flexible as possible to serve different types of users. Some users like to drill down and interact and thus won't be satisfied with merely static content. Such users should be allowed some level of interactivity with their results.

Establish Architectural Goals: Once process and data systems are understood, the next step is to establish architectural goals that translate processes into business goals and define the principles and philosophies behind infrastructure design. These form a basis for setting up criteria to evaluate a technological solution in terms of those business goals (typically scalability, availability, and administration).

Scalability: The entire architecture of an information-management solution should be designed to meet an organization's scalability requirements. It should scale well according to the growing numbers and needs of the company, but at the same time should not be an exaggerated case that's never fully leveraged. One way to bring scalability into a design can be to make it more modular so components can be added/removed as requirements change. Adopting open standards helps companies improve scalability of their data management systems.

Determine Availability: A company's system architecture should meet its availability requirements so as to prevent business and productivity losses from unexpected downtimes and failures. The IT infrastructure should be fault tolerant enough to prevent single points of failure — but that always comes at a price. Each organization should seek understanding of how much downtime is acceptable without causing too much loss.

Administration: The complexity and sophistication of a system's architectural backbone can make it difficult to manage. More may have to be spent on resources and manpower just to administer it. How much cost a company can incur on administering its data management system is something that should be considered at the design stage.

Define Design Space: The deep understanding of processes and systems (user requirements) and architectural goals set the stage for the next phase, in which a technology's design space gets defined. This maps the requirements along with the technology to provide the most effective and efficient solution possible. Otherwise, adopting and adapting the tools and technology can be a challenge.

The design space consists of three components. Hardware forms the backbone to which applications will be deployed and should be fault tolerant, redundant, scalable, and compatible with those applications. Suitable software tools and technology must fulfill both the business and user requirements. And technologies are needed for integrating a range of sources into one unified platform.