Guiding Principles:

• Identify template for each of the data models (Incident, Problem, Change, Service Requests, RITMs, Event Management, Application Transaction data, etc.)
• Identify data source and validate query to retrieve the data
• Perform QA on the retrieved data to ensure alignment with template and query
• Transform data using ETL processes for analytics preparation
• Harvest the data to capture historical and forward-looking insights
• Start with a Quantitative Analysis. 
  • Look at Process Behaviour Analysis (PBA) Charts (Opened Volume, Closed Volume, Response Time in Mins, Resolve Time in Mins) from a Time-Series perspective (By Year, By Quarter, By Month, By Week, By Day, By Hour) and see if you detect any signals, patterns, behaviours, abnormal behaviours, anomalies, etc.
  • Look at the Pareto Charts and see if you detect anything abnormal or changes since previous report. Look at Category, Subcategory, Root Cause, Contact Type, Assigned To, Assignment Group, Affected User, Business Unit, Organization, Location, Etc.
  • Look at the MTTR Grouping to see if there is any performance improvement in the resolution of P1-4 tickets in terms of resolving them much quicker.
  • Look at the Matrix Function to see if you can detect changes from a MTM view of top categories (Subcategory, Assignment Group, etc.) compared to a baseline Month.
  • Look at the Compare Function to see if you detect changes in top categories when compare two date/time data points. MTM, WTM, DTD, and HTH.
  • Use the Report Function to look at additional insights for further drilldowns.
  • Use the Search Function to search for specific records that match the search criteria for further drill-down.
  • Use the Dashboard Function to look at the environment from across 16 charts
• Conduct a Qualitative Analysis for further Insights
  • Look at the Slice n Dice Function to see if you can further drill down into the observations you have made.
  • Use the RCA Review Function to perform deeper review of P1-2 tickets
  • Use the QA Review Function to perform deeper review of individual records.
  • Use the Manual PBC Function to build custom PBA charts.
• Tie the findings/observations to gaps to the Operational Management Reference Framework. 
• Convert the gaps to recommendations based on the Operational Management Reference Framework. Transfer the recommendations to the CSI Register and manage to closure.

Notes:

• Turn data-driven insights into powerful narratives or stories that would grab the attention of all stakeholders and compel them to listen
• Narrative is the way we simplify and make sense of a complex world. It supplies context, insight, interpretation – all things that make data meaningful and analytics more relevant and interesting.
• With analytics, your goal is normally to change how someone makes a decision or takes an action. You are attempting to persuade, inspire trust, and lead change with these powerful tools. No matter how impressive your analysis is, or how high-quality your data is, you are not going to compel change unless the stakeholders for your work understand what you have done. That may require a visual story or a narrative, but it does require a story.
• Most people can’t understand details of analytics, but they do want evidence of analysis and data. 
• The most compelling stories of all are those that combine data and analytics, and a point of view.
• Data preparation and analysis often take time, but we need shorthand representations of those activities for those who are spectators or beneficiaries of them. It would be time-consuming and boring to share all the details of a quantitative analysis with stakeholders. Analysts need to find a way to deliver the salient findings from analysis in a brief, snappy way. Stories fit the bill.
• Couching the analytical activities in stories can help to standardize communications about them and spread results.
• Use the data and analytics to find solutions to problems.
• Use the findings to tell the most convincing and compelling story.
• Storytelling is, in fact, the final and most important stage of analytics. Successful communication of findings to stakeholders is as important as conducting robust analytics.
• The strength of the data lies in the power of the narrative
• Hypothesis testing is an approach to determine if the difference in the observed data is real or merely a matter of chance
• Graphics are uniquely suited to present the interplay between several variables
• Build a strong narrative from your empirical findings and then communicate to the stakeholders
• Use the analytics to have fact-based discussions
• Successful communications to stakeholders are as important as conducting robust analytics
• Use the narratives to strengthen an analytics-drive decision making process
• Most analysts are poor storytellers. They are introverts, more comfortable with machines and numbers then with humans. They are taught to focus on empirical disciplines.