In the first post in this series, we identified the value that data “in the gaps” can bring to an organization and how taking a deep search approach can help an organization overcome the challenges associated with finding and utilizing this data. We now turn to an exploration of some specific ways that deep search can help organizations, beginning with maximizing return on investment into existing data sources.
Organizations using licensed or publicly available databases may not find all the data they need in these sources, or they may encounter the opposite problem, finding an overwhelming amount of data full of noise that’s distracting and difficult to sift through as they try to locate the information they need.
A common example of the above situation can be seen in researching grants and funders. Organizations interested in tracking grants of interest, and understanding more about the funders behind these offers, are often trying to better position themselves to receive them. There is also simple intelligence to be had from seeing what grants are out there, and who is funding what. Grant tracking can be hampered by potential issues, including gathering information that is irrelevant to the organization’s needs, perhaps because a broad database is used covering domain areas of no interest, or too large of a geographical region. In other situations, the organization can fail to obtain the data it needs, as when a database does not include relevant information, such as the link between a principal investigator and their institution, that would help an organization better understand the context around a grant or funding.
By having to either tune out the noise of unnecessary data or find additional data sources to get the information needed, organizations can lose valuable return on time invested. On top of this, the need to use multiple grant databases makes it highly unlikely that the data found will be consistently normalized, increasing the difficulty in getting a complete picture.
Deep search helps address the above issues in part by using database information as a starting point, and — when multiple databases are used — cleaning and normalizing this data. Potential noise is automatically tuned out through deep search by an organization’s ability to focus on what it wants to see through filtering, keywords, ontologies, etc. By helping connect an organization to relevant information, a more complete, targeted dataset is created that focuses exclusively on the area of interest extracted from the wealth of available data.
In this context, deep search can help an organization maximize its return on time invested and the value of the licensed data sources it may have paid for. This is also true when your licensed databases might not have certain information — deep search makes it possible to add new information to a combined dataset by crawling websites in a focused, targeted way. The information obtained through deep search in this way can then be shared with stakeholders via emailed reports, and alerts can be set up in case information changes.
This is the second in a four-part series on how deeper, automated searching can help your organization more easily find the information needed to make the right business decisions. View the first blog post in this series here: Beyond Standard Search: Getting the Targeted Data Your Organization Needs