Maximizing the value of your ServiceNow PPM implementation via implementation quality measurement
Like it or not, the quality of your ServiceNow PPM implementation defines the quality of your decision-making. If you base your decisions on inaccurate data, your decisions will not yield the desired results. In project management, data quality is of utmost importance. Because the decisions you make will impact the realization of your organization’s strategy. If you go about this the wrong way, you end up with a so-called Watermelon Project, where data from your system does not represent the actual project state – also referred to as ‘green’ from the outside, but ‘red’ on the inside. Just like a watermelon.
How to measure implementation quality
Once you’re past the traditional ServiceNow implementation Go-Live cake moment, the actual work begins. To prevent Watermelon Projects, it is important to start measuring data quality as soon as you’re live. We advise to do that from the start, as it will help you spot data quality deterioration. ServiceNow allows you to measure data over time, like Spend vs Progress, sudden changes in planning, Burn-rates, Earned Value Analysis, Record Entities Used, Update Ratio, etc. (also known as Performance Analytics).
Root Cause Analysis: OOTB vs. Customized
The project portfolio management plug-in comes with a number of great workbenches, portals, workflows, roles, and logic that support users in fulfilling their role. During implementation, it’s important that changes made to support the organizational processes and governance are well-balanced against Out Of The Box (OOTB) functionality. OOTB does not mean that you cannot change anything. ServiceNow is a flexible platform that can be modified to a great extent. But there are some parts you better not touch to safeguard performance and upgradeability, to make sure you will benefit from your implementation efforts for many years to come. Measuring the amount of modifications (like additional fields, tables, business rules) and clearly identifying the effect on OOTB ensure that you make the right decision. Plus, it will shift the focus to outcome (goal) rather than output (“we need to have it in this way, because we always did it like this”).
Root Cause Analysis: Unclear governance/process
Governance processes can positively impact data quality. If data from ServiceNow is directly used in governance meetings, it will get more attention. Secondly, if there are multiple processes within a single organization that will have a negative effect on data quality, project managers experience a heavy administrative load when following different tools, templates and reports. Finally, it can also be ambiguous which processes there are and what needs to be done accordingly. Measuring the quality of your implementation will identify these issues and help you solve them. Poor data quality can be traced back to ambiguous processes and be resolved by providing guidance on the way-of-working and the role of each persona.
Root Cause Analysis: Knowledge gaps
Most people aren’t keen on administrative work, so they will tend to minimize this kind of work if it is unclear what the data is used for and how it can benefit them. Only when you work with resource plans and assign people to projects, it will be done (otherwise, there’ll be no people). But what about the risks, issues, actions, decisions and planning (other then a task for time writing)? If people do not know how to deal with them or how to register these properly in ServiceNow and understand what it’s used for, few people will actually do it. Measuring what elements of a project are utilized help you identify these issues. Providing additional training to users in utilizing the entire configuration will improve the implementation quality too. Fom our experience, the more elements are used within a project record, the more predictable a project will become – ensuring output becomes more reliable and portfolio dependencies can be managed more easily.
Root cause analysis: Underestimating the transformational aspect
One of the hardest parts of a ServiceNow PPM implementation is the transformational aspect. It is about adopting the tool, collaboration and transparency in order to maximize the added value towards organizational strategy. Sometimes, there’s the perception that captured data can also be used against you. A famous example is providing insight in risks and issues. ‘Red’ issues can be interpreted as mismanagement. A project manager reporting red risks is in deep trouble. He is faced with additional workload and needs to spend time and effort on explaining how this could have happened during governance meetings. Measuring adoption is about measuring how often records are updated, how many people are logging in, if the data is nurtured and how data is utilized. Transformational anomalies need to be addressed via additional support to specific user groups and departments explaining the purpose, what is expected from them and how their effort is being utilized.
Our added value
We are one of 5 recognized ServiceNow ITBM partners in EMEA and have done many PPM implementations. Customers involve us in all different stages – from early stages like requirements/outcome definition to intermediate advice to end-to-end implementation projects. With our extensive expertise in the fields of PPM, ServiceNow and Performance Analytics at hand, we have created a pre-build data quality measurement methodology and dashboard that can be easily implemented in your environment.
Our unique approach provides insights on the following, making sure the full potential of your ServiceNow PPM implementation is realized:
- Utilization of the PPM solution
- Update quality
- Potential areas of improvement
- The degree of customization (potentially affecting performance and upgradeability)
- Identification of process bottlenecks, after which we propose the best way forward
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Jochen Vos