Knowledge Demand Insights: Getting Smarter about Getting Smarter

[dangrady510]

If you googled "How do I make myself smarter?" what will come back is a number of articles with different studies, theories, and approaches.  Suggestions like "believe that it is possible," "socialize more frequently," "exercise more," and "daydream regularly" (if this one is true, then I should be a genius by now).  

On the other hand, if you google "How do I make my organization smarter?" the results are quite different.  This content focuses on subjects having to do with organizational multipliers, data driven approaches, and a number of articles focused on "working smarter, not harder."

Ultimately, they all boil down to increasing the efficiency of an organization's decision-making capability - which is one of the primary outcomes organizations try to achieve with knowledge management.

Knowledge management is meant to ensure all employees have access to the overall expertise held within the organization.  Organizations that take maximum advantage of knowledge management are better able to react to changing business conditions, increase their employee productivity, and offer better services to their customers.

To paraphrase Bill Gates, relevant content is king when it comes to knowledge management success. 

But with the accelerated rate of change that most organizations are dealing with, how does the content keep pace? 

How can we get smarter about making the collective organization smarter?

This is where ServiceNow's Knowledge Demand Insights comes into play.

 

Many of you may be familiar with the Now Platform's ability to surface similar open or resolved tickets to an agent based on information in the ticket they are working on to help eliminate hunting and pecking and speed time to resolution.  But what if we could use machine learning to help identify opportunities to prevent that ticket from even being generated in the first place?

Knowledge Demand Insights is a configurable Now Intelligence solution that uses in-platform machine learning to automate the discovery of gaps in an organization's knowledge base.

The solution isolates closed or resolved tasks(IT tickets and HR or customer service cases) that don't have knowledge articles references as part of the solution - and then uses machine learning to compare that subset to the current information in the knowledge base to further refine the tasks.

These tasks are then intelligently clustered together to give knowledge managers and service owners visibility and direction into high value content to prioritize as well as the ability to immediately kickoff a workflow to get the content created.

Keeping a consistent flow of relevant content clearly isn't a one and done proposition.  New insights are surfaced on a continual basis as new data becomes available and our machine learning engine automatically identifies new opportunities for improvement - and ticket or case deflection.

Increasing the velocity in which relevant content is made available to the knowledge base not only improves your agents' productivity, but it has a dramatic downstream benefit across your self-service channels(portals, mobile, Virtual Agent).

The end result is a smarter, more efficient workforce that is better equipped to service your customers and with maybe a little more time to daydream. 

Learn more Knowledge20 Predictive Intelligence Hands-On Lab(contains Knowledge Demand Insights)

Other Blog Posts about Now Intelligence:

Hidden Now Intelligence Gems in the Paris Release

HIIT 2.0: The Incremental Benefits of Machine Learning

The Digital Ensemble that Helps the Virtual Agent Create Better Experiences

NLU makes the Virtual Agent the Road More Traveled