To kick off our Content Intelligence fact sheet, we have to start with the definition we’ve created at Content Science:
The key phrase: Actionable insights. Why? Because it encapsulates the most important aspects of content intelligence: people and analysis. Without these two elements, organizations cannot move beyond content analytics to content intelligence.
Unstructured data accounts for 80% of all data according to the IBM report What is Watson? This huge amount of information requires amazing talent on your content teams to help visualize the data. This requires in part getting small data right, hiring the right people from the start, and focusing on content effectiveness.
Gerry Brown of U.K.’s Bloor Research explains that “content intelligence provides a complete 360-degree view of the previously separated silos of enterprise data and text. It provides the ability to slice and dice, drill down and report on text and data as an integrated whole.”
Content intelligence can also be applied to employee advocacy and accountability. As bigtincan CEO says, “By mapping out how an individual employee utilizes information—what content he or she accesses; how often, when and where certain materials make the biggest impact and what is most frequently shared and collaborated upon with their colleagues — companies can build a history of actions to benchmark performance. If an employee begins to access or share content less, this could be an indication that the user is unsatisfied or unmotivated in their job, or simply that the content available is no longer as effective as it once was.”
As Colleen Jones predicted in 5 Content Predictions for 2016, “Content intelligence will emerge as a strategic practice.” Although people have been talking about — or around — content intelligence since 2007 when Gerry Brown of Bloor Research used the term “content intelligence” “to describe what he saw as a union between standard structured data analysis and the mining of unstructured data.” Yet, nearly 10 years later it’s still not a widely adopted practice in part because of data overwhelm, poor leadership, or lack of dedicated resources.
After years of predictions and promises, we are starting to see a more widespread adoption of content analytics within a variety of publishing and marketing environments — but they are usually not called by the term “content analytics” or “text analytics.” Sometimes, they are denoted by the ubiquitous “Big Data“ label or even “artificial intelligence.” So, on one hand, content analytics as a distinct industry is not growing fast — but only because the vision is being realized outside of that label. — Andrew Davies, COO Idio
Moving toward content intelligence requires a system that utilizes multiple data sources and smart analysis from your great content hires.
Content intelligence can inform various teams involved in reaching your business goals beyond just marketing, including sales, IT, web development, and more. Learn how with dozens of additional resources in Content Science Review’s Content Intelligence section.
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