Want to Enable ITIL Continual Service Improvements? Here’s Why It’s More Important Than Ever to Have an Analytics Driven Process
Given the current global business climate, much of the world has been forced to rely on remote digital services more than ever have. IT service organizations are being asked to help enterprises maintain productivity and service continuity while under exploding user loads. At the same time, they are being told that existing budgets have to be stretched in order to make it happen.
How can IT services possibly keep up with this monumental task? The answer is to approach service delivery as something that can be continually improved on a daily basis. This allows IT teams to not only maintain the current value pipeline but enhance it, improving performance while occupying a similar resource footprint. The ITIL framework refers to this process as Continual Service Improvement – or CSI, for short.
ITIL continual service improvement can be defined as follows:
“Continual service improvement is a method to identify and execute opportunities to make IT processes and services better, and to objectively measure the effects of these efforts over time.”
A key component of making CSI initiatives succeed is measurement. IT service organizations must be able to derive specific Key Performance Indicators (KPIs) that directly measure the performance of a given area, and they must use these metrics to devise action plans to improve that performance area in measurable ways. ITIL analytics can leverage enterprise data to create a positive feedback loop: measuring the scope of any service problems, revealing potential actions that can improve the service area, monitoring relevant KPIs for the effects of remediative actions, and indicating the need for further actions as the process repeats.
Using Analytics to Measure – And Potentially Uncover – KPIs That Can Drive ITIL CSI Initiatives
Metrics and KPIs derived from enterprise data serve as both the baseline for measuring current IT service performance and an indicator of the success of CSI initiatives.
“An important task for CSI is to identify which metrics out of the thousands that are created daily should be monitored,” says BMC. “This is done by identifying, for each service or process, what the Critical Success Factors (CSFs) are. CSFs must be present if a process or service is to succeed. It is recommended that each process or service have identified no more than three to five CSFs (one or two in the early life of a service or process).”
Because continual improvements rely upon data in this way, data quality, visibility, and availability are all absolutely essential for CSI initiatives to function. IT organizations must be capable of aggregating data across all systems of record so that performance can be measured accurately. Data left in siloes can lead to skewing, biases, and multiple conflicting sources of truth.
By being able to analyze data from all relevant systems, IT can get a true measure of the KPI they are most concerned with. For example, if IT wants to improve the mean time to restore vital services, they must be able to measure downtime and resolution time for each applicable incident. Data from the ITSM platform, such as ServiceNow, only tells part of the story. IT service leaders must also be capable of obtaining data from APM systems, performance monitoring software, and even social chatter from corporate messaging services like Slack or Microsoft teams. The latter can be scraped for qualitative data using NLP. Because social chatter gives a direct measure of the average user’s IT service experience, this form of unstructured data provides feedback that other systems may miss.
Visualize and Analyze Data to Enable Rapid Decision-Making
Data alone is not enough to answer questions and drive action toward meaningful service improvements.
The enterprise training firm Invensis emphasizes that “The information which is gathered and analyzed needs to be presented in a proper manner with the right amount of detail so that the information is comprehensible and provides the required amount of detail to support informed decision making.”
KPIs derived from data obtained across all major systems of record can be more accurate and more contextualized to the broader enterprise picture. Expressive KPIs can be developed to give an instant picture of things like user sentiment or current operations risks using a simple score-based system. Having the capability to compile all relevant data can, therefore, give IT teams the objective information they need to act upon CSI initiatives with both precision and accuracy.
What’s more, Machine Learning (ML) algorithms and Artificial Intelligence (AI) models can help IT leaders discover which metrics are the most relevant to a given performance area. While some metrics’ relationship to a performance area are obvious, questions like, “Do these individual incidents with a given application have a unifying root cause?” cannot be answered without tedious trial-and-error. AI models can rapidly determine relationships using functions like a topic cluster engine, while ML algorithms can rapidly test and isolate metrics that have the strongest correlative relationship to a given issue, such as the occurrence of major incidents.
In this way, analytics can not only make data more useful and actionable, but it can also help enterprises reveal highly valuable metrics and KPIs that could not have been discovered through traditional manual data processing.
Leveraging Data to Engage in the 7-Step CSI Process
Data is the fuel that drives the engine of ITSM continual service improvement. Using data, IT organizations can fulfill each of the following 7-steps ITIL 4 guidelines recommend for CSI efforts:
- Identify the strategy for improvement
- Define metrics
- Gather data
- Process data
- Analyze data
- Use the data for improvement decisions
- Implement improvements
Undergoing this process allows IT leaders to extract more value from their daily activities without overtaxing the limited resources they have. In today’s volatile global business environment, this is an imperative rather than a nice-to-have.
“One of the biggest mistakes that an IT service manager or service owner can make is to leave a non-performing service without attempting to improve it with the help of ITIL CSI principles,” writes the DevOps-focused trainer Master of Project Academy.
However, note that improvements do not necessarily need to be made when the case is that a service area is falling short of expectations. Enterprise leaders may be concerned about competitiveness, or they may be evaluating the potential benefits of a new solution. They may also be interested in keeping up with present market offerings, or they could just be performing due diligence to evaluate if the current technology and process are the most efficient possible.
In any case, ITIL’s CSI framework can help IT make measurable progress, day-to-day, using a collective pool of accurate, current, and unbiased enterprise data as their guiding beacon.
To learn more about how using data and AI-powered analytics has successfully driven a CSI program at Rogers Communications, watch our recent webinar: “Using AI Analytics to Optimize IT Service Management (ITSM) at Rogers Communications“.
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