Nuvolo Implementation Prep: Getting the Data Right
“You’re not alone” are comforting words for anyone to hear.
They’re especially comforting for IT, Biomedical Engineering, and Facilities leaders at health systems preparing their data to swiftly migrate to an enterprise asset management (EAM) platform, like Nuvolo.
As a Nuvolo Premier Partner and its biggest implementer, we help hospitals and other organizations with everything Nuvolo; from implementation to managed services. But step one, data preparation is an often-overlooked step and a common pitfall in the Nuvolo journey.
Today’s blog offers a peek into how we set up clients for success during the data preparation stage.
Understanding Data Challenges
Nuvolo’s power is that it provides a consolidated ‘source of truth’ for organizations to streamline the management of their assets, work orders, and service requests across devices and facilities. Prepping the data associated with all these assets and requests requires a clear understanding of the following data challenges:
Sheer Data Volume
Most organizations have vast amounts of data to be mapped and migrated into Nuvolo. Putting this into context, here’s the scope of a recent Nuvolo migration we led 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
- Additional: 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 disparate 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 reconcile the data elements of the parent org with that of the acquired entity. Different departments may also use different naming conventions to describe the same device or asset. It’s also common that end users in the same department simply add data to a common system in different formats and spellings.
Missing Data
For all the data that exist in organizations around their assets and facilities, missing data is still an issue. Sometimes, data fields in a system are left blank because of user error, but in other circumstances, key data elements required for robust asset management exist in tools that haven’t been integrated for one reason or another, or their existing system doesn’t allow for the recording. Missing data is often the source for Nuvolo implementation issues and a key reason our clients look to us for help.
How We Help
Two foundational activities that we engage in at the beginning of each project help us set up our clients for long-term success. The first is diagnostic—a data health check—to help us identify data gaps and challenges like those noted above.
During that exercise, we’ll run reports on devices around scheduled/planned maintenances to identify missing entries in key mandatory fields, empty or uncompleted work, and more. As a leading Nuvolo implementation partner, we provide pre-built data maps that quickly identify gaps in client data. Some gaps are minor, but others are vital to identify to meet industry and Nuvolo best practices.
A second critical step is to outline a data governance framework. This requires participation and collaboration from key stakeholders across 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 view into any gaps or inconsistencies in the legacy data, and agreement on a go-forward data governance model, our consultants can then begin prepping data for the project through the following steps:
- Data Mapping: Identifying the source systems that contain the data required for the Nuvolo implementation. This step involves creating a mapping document that identifies the source fields and their corresponding fields in Nuvolo.
- Data Cleansing: Identifying and correcting or removing errors and inconsistencies from the data. This step involves identifying duplicate records, incomplete data, and incorrect data and correcting them.
- Data Conversion: This step involves creating data templates and transforming the data into the required format for importation into Nuvolo.
- 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.
- Data Migration: Importing cleansed and validated data into Nuvolo. This step involves configuring the data migration tools, importing the data, and verifying that the data has been imported correctly.
If it all sounds daunting, well, it can be. But as we said at the outset, “You’re not alone.”
Our Nuvolo consultants have led some of the largest health systems in the U.S. through implementation and are skilled at supporting organizations in the data preparation process. They are also experts in Nuvolo's latest release, Sweden.
Want to know more about Nuvolo or our expertise? Contact us.