Of the many applications of deep search benefitting organizations, a standout is the ability for an organization to use the solution to create an entirely new dataset when no single source exists and essential information is spread across multiple unconnected sources.
Creating a Dataset When No Single Source Exists
To illustrate how deep search can create databases from essentially anything, provided that the information needed can be crawled both legally and technically, we consider an example of an organization needing to track activity around regulations to inform its product strategy for both existing products and those in the research phase. The specific information sought by the organization is not contained in regulatory documents, but in some of the comments made on dockets.
For context, regulation documents on a site such as regulations.gov can each receive thousands or tens of thousands of comments apiece. Without using deep search, an organization seeking the desired information would have to review all the documents on this site, filter for the ones related to its product space, read through thousands of comments, and then provide a synopsis of what was said in the comments relevant to the organization’s interests.
Alternatively, this same organization could employ deep search to sift through the thousands of comments, identify the ones relevant to the organization’s needs by filtering for and applying key words, and then direct only the contextually relevant comments to specialists for review, with the insights they gain helping to inform product strategy.
As seen in this example, when desired information does not exist as a single source or as a database in and of itself, deep search makes it possible to collect and curate this information efficiently so that it can be interrogated for useful insights.
Gathering Information from Unconnected Sources
Data collection from unexpected and unconnected sources can be useful to organizations for a myriad of reasons, including protecting their business interests outside of research and development.
A specific example of this involved a client of ours who sought to track the movement of their product across borders to ensure that the product intended for one country did not (by unsanctioned import or export) end up in another country where pricing may be different. While this sort of activity is not uncommon, it can be hard to track and usually is discovered only by chance.
For our client, the data it sought did not readily exist in a single source. Before we helped them with our deep search solution, its process was ad hoc and required a great deal of time and manual effort, including digging through several different data sources such as TradeAtlas and checking customs bills of lading to track the progress of the product across borders.
By using deep search, the client was able to create a single data source compiling all necessary information from different licensed databases. Keywords could then be run across this dataset to identify the company’s product and track movements across borders. Additionally, information in this dataset could be de-duplicated and normalized to provide a ‘clean’ view of only relevant data that could then be shared and acted on. Through a deep search approach, the client can now manage the volume of the above work with only a small team.
With this example, we not only see the power of deep search to create a singular dataset from multiple sources, but also how the solution can save manual labor, a benefit that we will explore further in the next part of this series.
This is the third 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 two blog posts in this series here:
Beyond Standard Search: Getting More Value from Your Data
Beyond Standard Search: Getting the Targeted Data Your Organization Needs