Nuvolo Implementation Prep: Getting Data Right

“You are not alone” are comforting words for anyone to hear.

They are especially relaxing for facilities leaders in IT, biomedical engineering and health systems, who are preparing their data to migrate rapidly to enterprise asset management (EAM) platforms like Nuvolo.

As a Nuvolo Premier Partner and its largest implementer, we help hospitals and other organizations with everything Nuvolo does; From implementation to managed services. But the first step, data preparation is an often overlooked step and a common pitfall in the Nuvolo journey.

Today’s blog provides a glimpse into how we set up customers for success during the data preparation phase.

Understanding Data Challenges

The power of Nuvolo is that it provides organizations with a consolidated ‘source of truth’ to streamline the management of their assets, work orders and service requests across equipment and facilities. Preparing the data associated with all these assets and requests requires a clear understanding of the following data challenges:

net data volume

Most organizations have large amounts of data that have to be mapped and migrated to Nuvolo. To put that in context, here’s the scope of a Nuvolo migration we recently conducted for a large health system in the Midwest:

• clinical: 350K devices, 1.75M work orders, 80k maintenance schedules
• facilities: 350K devices, 5M+ work orders, 200k maintenance schedules, 100k rooms in over 250 locations
• Excessive: All contracts, checklists, manufacturers, models, vendors, and other reference tables configured to support all data.

migration from disparate systems

Organizations often have multiple data sources, such as computerized maintenance management systems (CMMS), building management systems (BMS), and real-time location systems (RLTS), among others, that may need to be integrated into Nuvolo . Integrating data from different sources can be challenging, as each system may have different data formats and structures.

lack of standardization

Inconsistent data can creep into a data set in a variety of ways. For example, a health system with multiple acquisitions may need to merge data elements from the parent organization with data elements from the acquired entity. Different departments may also use different naming conventions to describe the same equipment or asset. It is also common that end users within the same department add data in different formats and spellings to a common system.

missing data

For all the data that organizations have around their assets and facilities, missing data is still a problem. Sometimes, data fields in the system are left blank due to user error, but in other circumstances, key data elements required for robust asset management are present in devices that have not been integrated for one reason or another. , or their current system does not allow recording. Missing data is often the source of Nuvolo implementation issues and a key reason why our customers look to us for help.

how we help

The two fundamental activities we engage in at the beginning of each project help us set our clients up for long-term success. The first is a diagnostic – a data health check – which helps us identify data gaps and the challenges mentioned above.

During that exercise, we will run reports on equipment around scheduled/planned maintenance to identify missing entries in key mandatory areas, empty or incomplete work, and more. As a leading Nuvolo implementation partner, we provide pre-built data maps that quickly identify gaps in client data. Some of the gaps are minor, but others are important to identify in order to meet industry and Nuvolo best practices.

Another important step is to design a data governance framework. This requires involvement and collaboration from key stakeholders in clinical engineering and facility management, but the end result should be a standardized set of processes for collecting, storing and using data across the enterprise.

From Nuvolo Data Mapping to Migration

With a clear vision of any gaps or inconsistencies in legacy data, and an agreed upon data governance model going forward, our consultants can begin preparing the data for the project through the following steps:

1. Data Mapping: Identifying the source systems that contain the data needed for the Nuvolo implementation. This step involves creating a mapping document that identifies the source fields and their corresponding fields in Nuvolo.
2. data cleansing: Identifying and correcting or removing errors and inconsistencies from data. This step involves identifying and correcting duplicate records, incomplete data and incorrect data.
3. data conversion: This step involves creating a data template and converting the data into the format needed to import it into Nuvolo.
4. data validation: The process of ensuring that the data imported into Nuvolo is accurate and complete. This step involves validating the data against predefined business rules and performing data quality checks.
5. Displacement of data: Importing clean and valid data into Nuvolo. This step includes configuring the data migration tool, importing the data, and verifying that the data has been imported correctly.

If all of this sounds daunting, well, it can be. But as we said at the outset, “You are not alone.”

Our Nuvolo consultants have led some of the largest health systems in the US through implementation and are skilled in supporting organizations in the data preparation process. He is also the expert on Nuvolo’s latest release, Sweden.

Want to learn more about Nuvolo or our expertise? Contact.