The journey towards improved decision-making and Asset Management Maturity begins with Data Quality. Our Touchstone Data Quality Insights Service offers a fast, proven method to probe data health in your IBM Maximo system, allowing you to remediate issues, minimise execution errors, and improve returns on critical asset investments.
Data Quality is the foundation to improve asset maintenance, enhance forecast accuracy and lift productivity. Our service provides a pre-packaged set of common Data Quality measures that can be applied across your IBM Maximo environment to give you rapid insights into your system’s health.
Underpinned by our Touchstone model unifying people, process and technology, this data query service can be run across any IBM Maximo environment. It can be used to highlight poor data referential integrity—a frequent issue that prevents IBM Maximo from functioning correctly—and identify Data Quality issues that impact asset maintenance planning and can hinder an organisation’s progress towards Asset Management Maturity.
Tuned to your specific IBM Maximo environment, our Touchstone Data Quality Service identifies data integrity issues en masse across your entire asset base and calculates the cost and effort required to remedy them.
Our IBM Maximo experts have assembled a ready-to-run list of 50 highly targeted Data Quality measures that can be applied on day one to analyse the health of your system. This rule set is refined to closely match your unique Maximo scenario and a report created to measure data integrity improvements over time.
By identifying IBM Maximo errors quickly and cost-effectively, we free up your resources to work on the business rather than your software. We provide an aggregated view of the data rule breaches and assign time and cost estimates for remedying the highlighted errors. We also provide links to the asset records so you can easily identify the correct area within Maximo to update data directly.
Data problems that continue to occur have a detrimental effect on efficiency and can be exceedingly frustrating for system users trying to execute daily tasks. The value of the service lies in our ability to know precisely where to look for data issues. Our database queries identify problems rapidly and all at once, surfacing issues that often sit undiscovered until a user interacts with the asset.
One of the service’s key benefits is assessing the impact of data quality issues over time. Measuring whether data errors are increasing or if a historical data problem has a cumulative detrimental effect provides a powerful impetus for system remediation and data input protocol improvements.
Data quality is a constant concern at asset-intensive organisations because effective decision-making is severely hampered without a reliable source of truth in IBM Maximo. This impacts return on investment—particularly when expensive assets break down—and prevents accurate forecasting of future maintenance costs.
If IBM Maximo repeatedly fails to execute processes, it causes users to question the system and its deployment. However, data corruption is the real cause of these issues; the system will not work if the referential integrity of the data is flawed.
This has a day-to-day impact on the effective execution of inspection routines and can lead to a failure to find and maintain assets. It also has operational repercussions for other line-of-business systems, such as finance and GIS, which rely on accurate data feeds from IBM Maximo to function effectively.
Flawed data hinders progress towards asset management maturity. It can cause repeated asset breakdowns and costly reactive maintenance processes, while preventing the development of more effective preventative maintenance routines.
However, with greater trust in the system’s data, a roadmap of more sophisticated asset management processes, such as predictive maintenance scheduling for increased asset productivity, can be developed. Data quality improvements also uplift other core processes integrated into Maximo, such as budgeting, forecasting and project planning. In addition, improving information quality allows more advanced IoT and AI innovations to be incorporated, adding more value to existing data.
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