One particular problem that company leaders have identified is the use of multiple terms or definitions to describe one thing because it may mean that information about a product exists within several systems, with each system having its own definition. Consolidating this information can pose challenges.

Information management aims to reduce the time it takes for people to locate relevant content and enable them to make confident business decisions based on informative data. From a strategic point of view, good information management is what stands between sluggish response times and streamlined business practices. All of a company's information should be connected. Ideally, submissions flow into licensure, which then flows into the portfolio.

Tracking resources via project planning, the authoring process, the submission process, the application process and, ultimately, approval makes it possible to form a bigger picture of how resources are being managed and where they might best be directed. Companies can use this informational picture for resource prediction across multiple products and countries. For instance, good information management can concurrently track license renewals, products that are coming to the end of their patent life, new products entering the market and the state of current R&D projects. This information can help answer questions such as:

• Is it in the company's interest to continue marketing a particular product?
• Should the company look for a partner?
• Are there opportunities for a product extension and what would the market potential for additional indications be?

From bottom to top

Successful information management requires a technology solution that encompasses all enterprise applications, including document management, ERP and customer management. Ovum analyst Sarah Burnett describes the complete, multilayered enterprise package as the information management stack (1):

• The information stack begins at the infrastructure level, which comprises both internal applications, such as file systems, databases, and archiving, and external sources of data, which may encompass cloud deployments such as software services, RSS feeds and web-generated information.
• Next comes data management, life cycle management, the retirement of old data that is no longer required, archiving and records management. Often, companies have data or reports that, while not active, need to be accessible in the near term. Even though some solutions allow for this type of storage, it's also important that companies learn how to classify information to simplify the task of finding it. Furthermore, there's a need to standardise the way information is classified, as the use of different terms negates the value of data storage. Another crucial aspect of data management is metadata management. What that means is that more time needs to be spent resolving issues around data definitions and improving data quality because having good and consistent data from the outset will allow companies to produce better quality reports and analysis.
• The third layer in the information management technology stack comprises data extraction and integration. First, there's data warehousing or data consolidation, which deals with longer-term data cycles. This system is most appropriate when there are definitional discrepancies or when a lot of data cleansing is required. Data warehousing or data consolidation is valuable in so far as it lets personnel conduct multidimensional analyses. If, for example, a number of issues have arisen during drug development, business leaders can use the data to analyse the value and cost of the project.