Knowledge demand insights uses machine learning to automate the discovery of knowledge gaps in your knowledge bases. Knowledge gaps are identified when incoming cases are not matched to an existing active knowledge article.

Knowledge demand insights has the following benefits: (1) improves the effectiveness of

self-service by ensuring knowledge articles exist that can address current cases (2) Speeds case resolution by having relevant knowledge articles to deflect future cases (3) improves case resolution by recommending knowledge articles that can best address the current worked case.

The Knowledge Demand insights process first runs similarity and clustering to combine similar cases then compares those cases against existing knowledge articles. If the process doesn’t find a knowledge article that can solve the similar cases, it generates a Pareto chart via a scheduled job.

Note – If you enabled the auto training from article 1 some of your PI models may be already trained. You know the model is trained when you see a [version #] next to it. If that’s the case, you don’t need to re-train just move on to the next step.

Lab 4 – Configure & Train Knowledge Demand Insights

Train the Similarity Algorithm

1. All > Predictive Intelligence Homepage > Similarity

2. Expand the Similarity caret and locate the “Similar Knowledge Articles for HR Task” solution. Click Train. Depending on the amount of data in your instance your model may take 15-30 minutes to complete training.

3. Confirm that the Similar Knowledge Articles for HR Task has completed training, this may take up to 30minutes. The waiting in training status should be replaced by a [Version : #] when training is completed.

Configure and Execute Knowledge Demand Insights Job

4. Once similarity is trained we can kick off the knowledge demand insights job. This will generate the Pareto chart used to identify gaps in knowledge. 5. Go to All > Knowledge Demand Insights >Demand Insights Configuration6. Click on sn_hr_core_case to open the configuration. Notice that the Demand Insights application uses a similarity and clustering solution definition.

7. Modify the Query condition as necessary and hit update. In my case the filter conditions are fine.8. Go to All > Knowledge Demand Insights >Scheduled Jobs

9. Click the [Knowledge Curation]: Generate HR Case Clusters

10. Click Execute Now in the Scheduled Script Execution Form. This will execute the job that combines the similarity and clustering results into a pareto chart. The job may take up to 30minutes to complete.

Using the Knowledge Demand Insights Pareto chart

The Knowledge Demand insights application generates a Pareto chart identifying patterns of potential knowledge gaps.

1. Got to All > Knowledge Demand Insights > Demand Insights for HR Cases2. If there is valid data, the Pareto chart will populate like so.

3. Each bar represents a cluster of similar cases which do not have knowledge articles. 4. Click on the first bar in the chart to show the case details.

5. Notice at the top you can Generate a representative sample or ignore the collection. Ignoring means you don’t agree with the prediction and you wish to hide this cluster.6. Click the “Generate a Representative Sample” then check the select all check box to select all the cases.

7. Then click “Report Knowledge Gap”. This will create a knowledge task with the gap description for the knowledge manager to pick up and review.

8. You should see Feedback Task has been created.

9. Click on the Feedback Task Link. Demand insights has created a Knowledge Feedback task as part of the knowledge workflow.

Congrats you’ve used machine learning to identify gaps in your knowledge base.

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