The Netflix of Knowledge Management

Mercy Harper's picture

Imagine kicking back with some popcorn to watch Netflix and having to scroll through almost 15,000 available titles. Even with a good search engine and well-organized categories, sub-categories, and sub-sub-categories, you’d be likely to face decision paralysis—and finish your popcorn before you’ve picked a flick. Thankfully, Netflix knows what you like. Its smart recommendation system suggests movies and shows based on what you’ve already watched.

Smart recommendation systems have amazing potential for knowledge management. These systems integrate information about users and their past behavior to filter items and predict which will be most relevant or appealing to a particular user. That means they can do two amazing things:

  1. Improve enterprise search by ensuring the top results are relevant and tailored to individual employees.
  2. Enable alerts that push content to employees based on who they are, what they’re working on, and their prior interactions with KM systems and other work applications.

At APQC, we’re already seeing organizations use smart recommendation systems to provide faster, easier, and more targeted access to content and experts. Many are using Microsoft Delve (and the underlying Microsoft Office Graph, which aggregates user data across Office 365 apps) to push recommendations to employees based on their current network, what they’re working on, and what their colleagues are doing. For example, an energy utility we spoke with applied smart recommendations to its people profile system, helping employees expand their networks and connect with likeminded colleagues across the company.

But there’s a catch: implementing a smart recommendation system can be a little tricky. From a simplistic perspective, there are two different ways to develop a recommender system:

  1. Item-based filtering bases predictions on a user’s prior interactions with content and the system.  
  2. User-based filtering bases predictions on user type and characteristics.

Which approach will prove easier to implement and more effective in the long run depends on several factors including the number of end users, the frequency with which new content is added, and the diversity and number of people in different roles. More sophisticated recommendation engines combine both approaches to generate highly tailored recommendations.

Additionally, smart recommendation systems often require integration across multiple platforms—including those outside KM’s control, such as HR and customer data systems. As a result, technology integration, security, and privacy concerns may arise. And some early adopters have found that the algorithms in off-the-shelf platforms cannot be sufficiently customized to provide accurate recommendations.

Still, implementing a smart recommendation system for KM may be well worth the hassle, especially for large organizations with many employees, clearly defined roles, and lots of knowledge content. These systems might prove to be key to achieving that ultimate KM goal: delivering the right knowledge, at the right time, to the right people.

To learn more about how smart recommendation systems, chatbots, cognitive search, virtual reality and other leading-edge tech can benefit KM, see APQC’s new collection, Emerging Technologies for Knowledge Management.

1 Comment

Anonymous's picture
Nice article Mercy. The correlation and example of using Netflix is spot on IMO. I never thought of that entertainment platform as a KM system at work but in reality it is. Other examples like an Amazon Prime or Google account that assesses, anticipates and then accurately predicts what content, products, services or even emotional desires to fulfill a consumer's needs are all at the forefront of the next recommendation technology that you describe. I would love for the technology to take a leap further and not only make somewhat "broad" recommendations but even more tailored based on past and future patterns of life-work or personal. What movies (content) do I usually watch on Sunday nights in December while it is raining in Tennessee? Or add any other amount of data points that can be applied to my specific consumption habits and patterns of life that I'm willing to share with the platform for a better recommendation. What knowledge do I need at the end of the 3rd quarter IOT make budget decisions for the next quarter? Standard question right? BUT my customized question can be tailored with context to be based on the past 50 3rd quarters' data AND projection modeling that looks ahead and can produce the most cognizant, data related projection for the next 50 quarters? A Siri, Alexa and Watson based hybrid Netflix model for KM that dynamically anticipates and produces recommended courses of action for future organizational decision points of varying complexity. Or even recommends that the organization has a decision point if one is needed. Oh the Rabbit Holes are aplenty with this subject. Bobby Graves