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Plan your successful CMDB deployment

INTRO

Achieve high‑performing business services using a healthy CMDB

Content Guide

Analyze Performance Analytics – Step 3

You’ve configured your sources and collected your data—now it’s time to analyze it and share the information with your stakeholders.

Examining data in widgets

Widgets are the basic building blocks for data visualization. They configure the indicators and breakdowns and display them in a meaningful way.

With ServiceNow® Performance Analytics, you have several different types of visualizations in widgets, so it’s important to understand and select the visualization that will convey the most insight.

Read on for a few examples of how you can use the widgets to create a specific type of insight, such as directionality, seasonality, etc.

Single score widget

You can use the single score widget to immediately communicate the value of a leading or lagging KPI. Here’s an example of a single score widget that shows the resolution time for incidents (also called the mean time to resolve or MTTR):

Figure 1: An example of a single score widget

This widget displays the current value of the KPI (4.70 days on November 16) as well as the previously measured value (4.77 days on November 15). It also shows the change between the values (a decrease of 0.07 days or ‑1.5%). The light blue area of the chart shows the overall trend for the KPI. You can see the decrease in the value between the displayed data points and that there has been a recent decline in the value of the KPI.

This visualization also includes:

  • Time series aggregation This specific widget displays MTTR on a 28‑day running average  to help smooth out the curve. It doesn’t require any additional data collection for the indicator, because the time series aggregation is set in the properties of the widget itself and is calculated at the time of display. This makes it easy to tailor the visualization for the stakeholder. One stakeholder might want a 28‑day running average while another wants a monthly average. You can point two separate widgets to the same daily indicator to create these different visualizations.
  • Directionality – To drive improvement, KPIs need to have targets and a direction. Are you trying to maximize or minimize the KPI? If you’re trying to reduce MTTR, your goal is to minimize the KPI. You’ll set the direction at the indicator level, but you’ll see its impact on the visualization. In the example shown above, the downward change of 0.07 days is advantageous (indicated by green text) because it shows that the KPI is moving in the indicator’s desired direction. If you wanted to maximize direction of the indicator, the same downward change would display in red text.

Analysis For an organization that measures success based on MTTR, this widget shows improvement since the KPI is trending in the desired direction. But that doesn’t tell the whole story—this is only a lagging indicator for the incident management process. To get the most from the single score widget, you can track a KPI that’s a leading indicator (such as the first call resolution rate) and then take action to improve your MTTR. Using the widget in this way helps you refine the visualization to see the most relevant data points.

Time series widget

The time series widget is the more "classic" Performance Analytics widget. It shows the trend of a KPI over time. This time series widget displays the number of new, daily incidents:

Figure 2: An example of a time series widget

This widget displays the last three months of data collected for the KPI with a data point for every day. You can configure the default time period displayed, and you can provide data consumers with the option to select different time periods. In this example, the Number of New Incidents Indicator is based on the Incidents.New Indicator Source, so you’re looking at process intake on a daily basis.

This visualization also includes:

  • Seasonality – You can use time series widgets based on indicators or indicator sources that measure volume on a single date to assess frequency and distribution over time. This is important because it starts to show volumes you can take action on. For example, if Friday is historically the heaviest day for intake, you might bring in additional staff to process that increased volume.

Analysis – This time series example shows that the heaviest intake days for incident management are Monday through Friday. Given that, you know this organization is on a traditional work week.

The time series widget is versatile. You choose the indicators you display, even multiple indicators at the same time. In Figure 3, you can see the incident backlog (represented by the column time series) versus the average age of the incident backlog (represented by the line time series):

Figure 3: A time series widget showing an incident backlog versus its average age

By displaying these two KPIs together on the same chart in the same time series, you can see the potential interactions between them. The time series widget supports the display of multiple indicators on the same chart (a maximum of seven indicators per the default settings), and it also supports two y‑axes of measurement. In the example, the volume of the backlog in numbers is the left y‑axis, and the age of the back log in days is the right y‑axis.

This visualization also includes:

  • Correlation When you use time series widgets to plot related KPIs on the same chart, you can see how the trends correlate. This example shows a decrease in the recent trend of the incident backlog (in the red box). But it also shows a corresponding increasing trend in the average age of the backlog (shown by the red arrow).

Analysis –  This chart shows that the incident backlog is decreasing at a good pace. There has been a 35–40% reduction in the backlog in the past six weeks, which shows that the organization has a focused effort on closing incidents. This is good, but there’s still a corresponding increase in the average age of the remaining incidents in the backlog. This means that if the organization continues to close the incidents in the backlog, its MTTR will increase.

From this time series chart, you can infer that workers were given instructions to reduce the incident backlog without any guidelines. The result was workers closing the easiest and newest incidents first while leaving the more complex, older incidents to age in the backlog. When you take this approach, your MTTR will start to increase, which is likely not what the organization wants.

On the plus side, these visualizations show how process owners can take action as they see the trends develop. The red arrow points at the trend increase in the backlog reduction—this tells the process owner how to:

·      Change the way people work

·      Reduce the incident backlog

·      Keep the average age of the backlog flat

This will result in a stable MTTR for the process.

Breakdown widget

With the breakdown widget, you can add a dimension of analysis to your visualization. Instead of looking at a monolithic measurement, you can see the individual components that make up that value. This is especially important to identify the best investment for the improvement you need. Take a look at Figure 4 for an example of a breakdown widget that shows the incident backlog by age:

Figure 4: A breakdown widget showing an incident backlog by age

This is actually the same backlog you saw in the previous time series chart, but this visualization shows additional information about the backlog by age. The colors represent the age groups for each data point displayed, which is important for segmentation, which is one of two additional features of this visualization:

  • Segmentation – Using a breakdown widget gives you insight into the specific elements contributing to the KPI. In this example, you can see that the age of the backlog is getting older based on where the color green (31–90 days) appears—in the columns showing the most recent days and measurements.
  • Proportion – The breakdown widget is critical in showing proportion as it relates to the dimension of an analysis. Even though the backlog is aging, the majority of the incidents are still in the 01–05 days range. This means you can fix the age problem more easily with the correct course of action.

Analysis – You can use the evidence in the breakdown widget to back up the analyses you make from the time series widget. The red arrow on the left corresponds to the the increase in the average age of the incident backlog in the prior time series chart. The red arrow on the right shows the point where the 31–90 day range becomes much more visible in proportion to the columns—this indicates a larger number of significantly older incidents.

When you look at the most current data point, most of the incidents in the 06–30 day range have been replaced by those in the 31–90 days range. This is why the average age of the backlog has increased significantly over time, but it also means that remediating the problem requires a focus on more than the incidents in this range. The remaining incidents in the backlog appear overwhelmingly in the 00–01 Day and 01–05 days range.

Workbench widget

The workbench widget is a great way to bring many different data points into a single view. We architected this widget for process or service owners because it gives them the instrumentation they need to have control over the process and take action. In a lot of ways, the workbench widget is like a breakdown widget, only the workbench widget does a lot more.

Figure 5 shows the incident management process by state:

Figure 5: An example of a workbench widget with the Incident Process by State highlighted

Workbench widgets are ideal for viewing a process flow (by state) or the age of records in an active backlog. You can click any chevron across the top (in the red box) to examine that area of the process in more detail.

Underneath the row of chevrons is the spark line for the KPI. Under that spark line (in the green box) are configurable supporting indicators. These are not simply static supporting indicators—they respond in sync with the main spark line. You can hover over the main spark line (or select a date from the drop‑down list above the KPI name) to change the data you see in the supporting indicators.

The breakdown widget shown in Figure 6 lets you view the data one level deeper. In this example, you can see the priority breakdown of the incidents in the Awaiting User Info state of the incident backlog. You can also switch between the breakdowns and the actual records that make up the main indicator number by clicking the Records tab.

Figure 6: An example of a breakdown widget showing the Records tab

Switching between trend‑related data and record data lets you move from strategic to tactical information on one screen. From this view, you can also see a delta record comparison between days or launch into interactive analysis by right‑clicking the list header. This makes workbench widgets ideal for helping process owners gain insight into their processes.

This visualization also includes:

  • Interactivity – Workbench widgets are designed to be interactive and to eliminate clicking back and forth between dashboard tabs to find meaningful data. These widgets are for those who understand the underlying process and want to actively analyze trends and the data that make up those trends.
  • Proactivity – The goal of all useful analytics is to put them into action. By bringing strategic and tactical data together on the same page, process owners have the tools they need to control their leading KPIs and drive the organization’s goals.

Analysis – The workbench widget provides valuable information about the incident management process. Of all of the incidents in the backlog, 196 of them are in the Awaiting User Info state. Not only does this number seem high for the backlog, the trend of incidents in this state is increasing (notice the right portion of the spark line toward the top). From the supporting indicators, you can see that the average age of these 196 incidents is 7.26 days (much higher than the 28‑day average MTTR of 4.70 days), and 57.1% of these incidents have not been updated in the last five days.

When you view the Breakdowns tab, you can see which assignment groups own these incidents and if there’s an assignment group that’s disproportionally putting incidents in this Awaiting User Info state (perhaps because they have figured out that this state puts the SLA clock on pause). Ultimately, you have the data you need to take action and change behaviors to ensure the organization meets its MTTR goals and that customers are happy.

Widgets in dashboards

Now that you understand how you can use widgets to visualize data, you need dashboards to organize the widgets for the various groups who will consume the data.

Fortunately, there are many out‑of‑the‑box dashboards you can use as a starting point. To view the available dashboards, click Performance Analytics > Dashboards in the navigation pane on the left. The last selected dashboard displays in the main content pane.

The drop‑down list at the top of the page lets you select from the dashboards that come with the plugins you activated. Explore these dashboards and determine if they contain the widgets that are most useful for your organization. Many of them include workbench widgets to help drive interactivity along with a variety of widgets that are organized by process onto different tabs.

You also can create dashboards to meet your organization's specific needs. You can think of a dashboard as a blank canvas where you can add widgets that are most meaningful for the stakeholders consuming the data. ServiceNow recommends you work with your different stakeholders (executives, service owners, workers) to build dashboards they can use in their daily work. For example, you can create a dashboard for workers with the prioritized records they need to work on. You can create a dashboard for service owners with widgets from different processes on the same tab. And you can create executive dashboards with red, yellow, and green indicators across the business.

The opportunities for creating dashboards are endless. But be sure people use the dashboards you create. If they don’t, figure out what additional data or visualizations they need. Continually improve your analytics program to drive continual service improvement in your processes.

Tools and resources