Data science teams need content managers. Here are 3 ways you can incorporate content management into your data science and analytics.
Content management (CM) is the process for collection, delivery, retrieval, governance, and overall management of information in any format. The term applies to the administration of digital content, whether that content consists of images, video, audio, text, or multimedia.
When I think of content management, I think about marketing departments managing their messaging on corporate websites, or HR departments posting relevant articles and information on an internal corporate intranet. The natural inclination is not to think about content management in the world of big data algorithms and statistical analysis.
An emerging school of thought suggests we should change this thinking.
"A great example is large scale agriculture," said Anthony Calamito, Chief Geospatial Officer at Boundless, a provider of geospatial technology solutions. "Many of these companies store vast amounts of data and imagery, but they haven't thought through how to effectively store, index and manage the data."
In part, the issue is technical storage—but an equally important concern is how to retrieve and display the most relevant data to a user.
In the data science world, this issue is addressed by iteratively perfecting algorithms that probe the data to find answers to important questions. But a companion need is to return information of high relevance that builds on these answers and gives users a more complete picture of not only the immediate questions and answers but of surrounding data content that explains the answers so that users have a complete understanding of the information that they can use for business decision making.
This is where content management adds value to big data and analytics. The content manager can repurpose content that applies to other business situations to the specific situation an end user is dealing with. He or she can leverage this information by routing it to others who need to know. Working alongside the data science team, a content manager can also ensure that data content is fresh and that any changes in a given situation or outcome are promptly reported to data users.
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The end result for users of big data and the data science team is that there is now a journalistic side to big data that ensures information stays relevant, is processed in ways that enable it to be easily consumed, and gets to those who need to know about it.
Here are three ways to incorporate content management into your data science and analytics
Adding the content management discipline to the data science team adds relevance to big data reporting because content managers have expertise with the skills to develop and route timely information to those who need it, in ways where content is easily consumed and utilized. This would eliminate cumbersome and user-unfriendly reports where business value is lost because reports are too complex for users to understand.
Having a content management expert on staff doesn't guarantee that this individual has the same level of business depth and understanding as a business data analyst. That's why a great approach is having the two functions work collaboratively, with the business analyst clarifying the business needs, and the content manager finding ways to optimally format and present information to meet those needs.
As your data science team moves toward a content management approach to reporting, review and assess your existing big data reports. Are they being used, and are they delivering what users expect them to deliver? Or are they cumbersome and in need of rewrites or reformatting? Retire unused reports, and revise reports that could be useful but aren't so that they more effectively deliver relevant information to their users.