By Brad Bannwart, Lead Knowledge Management Consultant, Verint®
Successful knowledge management (KM) programs focus on measuring, tracking, and optimizing the delivery of value. But how do you link your KM capabilities and activities to specific business outcomes?
KM-savvy organizations define a framework for understanding KM value, review KM system and business analytics, and use a shared intelligence model to provide actionable insights for driving KM effectiveness. Let’s take a closer look.
Defining a KM Value Framework
KM programs can provide tremendous benefits to customers, employees, and the organization simultaneously. These benefits can be both internal and external, strategic and operational.
KM value can be categorized into four distinct categories: customer experience, quality of work, operational efficiency, and employee engagement. Effective KM programs take a balanced approach that drives business outcomes in each of the four areas, without overemphasizing any single area or outcome.
Understanding KM System Analytics
KM system activities can be mapped to these four dimensions of value, resulting in analytics that are aligned to outcomes:
Leveraging KM Business Analytics
- Customer experience is strongly related to user’s interaction with the KM system. Typical KM interaction analytics might include the occurrence of knowledge gaps and/or how many searches or content views were required to find the right information.
- Quality of work is strongly tied to the quality of the information itself. Feedback and ratings for content quality are a great proxy to the overall quality of the KM system.
- Operational efficiency can be measured at both the user and author level. Analytics that measure how much time users need to find and consume information can indicate productivity. Workflow throughput and speed to market can help you determine authoring effectiveness.
- Employee engagement is indicated by KM system adoption and participation. Analytics often include how well users are using the KM system, providing ratings/feedback, and contributing to knowledge.
While KM system analytics are important, relying solely on them is risky. To develop a holistic view of KM performance, you need KM business analytics. These can be defined as supporting data that relates to the effectiveness of knowledge that can’t be measured in the KM system.
Most KM systems can identify when a user searched and found content, but they can’t tell how well the user understood that content, if the content was accurate and up-to-date, or if the content actually helped resolve the user’s issue. This is the KM “blind spot,” a longstanding limitation to truly understanding KM value.
Fortunately, the recent influx of business tools to measure customer and employee feedback, employee productivity, and quality have resulted in rich new data sets that can be leveraged to measure and understand true business outcomes. By incorporating that data into a KM analytics framework, you can evolve a much deeper and richer understanding of KM value.
Evolving a Shared Intelligence Model
To fully optimize value, KM programs must integrate and aggregate both KM system and business analytics into a common view. These are considered “KM KPIs” (key performance indicators).
By sharing intelligence across business tools and customer touchpoints, organizations can create KPIs for each dimension that allow them to monitor, track, and demonstrate KM value. The shared intelligence can be combined with KM data to provide actionable insights to inform and prioritize content development and delivery.
For example, speech and text analytics can offer insight into how effectively KM supports the customer experience. By defining categories that identify confusion, satisfaction, misinformation, and holds/transfers, you can correlate the results with KM content usage data to prioritize your content improvement activities. Example KPIs might include knowledge gaps (KM); customer confusion (speech analytics); customer effort (surveys); holds/transfers (speech and text analytics).
Similarly, quality management (QM) and automated QM systems can help reveal how effectively your KM system contributes to the quality of work. By adding questions to QM assessments, you can determine the effectiveness of knowledge usage and correlate this data with KM behavior data to identify opportunities for coaching and KM content improvement (such as formatting, structure, titles, etc.) Example KPIs might include percentage of positive content ratings (KM); user comprehension (QM); and correct information provided to customers (QM).
Knowledge management remains critical for delivering world-class customer experiences, ensuring knowledgeable, engaged employees, and delivering high quality work as efficiently as possible. While KM analytics continue to improve, organizations that look outside of the KM system for complementary business analytics can gain a much deeper, more holistic understanding of KM effectiveness. By developing a shared intelligence model, your organization can dramatically enhance the value of its KM program.