How to Turn Your Poor Quality Data Into Treasure Before a RAM Study?
Having accurate data is vital before starting a Reliability, Availability and Maintainability (RAM) study. However, it is unavoidable that organisations have to work with current failure data which is of poor quality. How to turn your poor quality asset data into treasure and make your RAM study more cost-effective？Keep reading to find the solution.
Are You Experiencing These Poor Data Quality Issues?
- Do you have poor quality data and do not know how to begin your RAM study? or
- Are you unable to get valuable insights from your poor quality data to support evidence-based asset management decision-making models? or
- Are you struggling with identifying which data is wrong or incomplete and developing a data standard?
If your answer is YES, you will find the solution after reading this blog.
Data Quality Challenge is Everywhere
When you review your assets data, it is quite common to encounter data quality issues in reliability engineering and maintenance management. Poor quality data can constitute a significant cost factor, which is harmful to your organisation’s evidence-based asset management decision-making models in the long term. In particular, you may face data quality challenges in these following situations:
- Semi-integrated (or non-integrated) systems and suboptimal interfaces: Geospatial Information System (GIS); Enterprise Asset Management (EAM) & Enterprise Resources Planning (ERP); SCADA, Customer Information Systems.
- Implementation of or changes to your organisation’s major systems: EAM & ERP systems changes and/or upgrade.
- Business Process Compliance: non-existent or inadequate business processes in place. If processes are in place, business may not follow them.
- Increased Volume: the amount of data collection required has increased.
Bad data negatively impacts your organisation’s efficiency, productivity, and credibility. However, if good techniques are used, the failure data of the assets and equipment can turn into treasure.
HolisticAM Will Help You Solve The Data Quality Puzzle
HolisticAM applies a variety of techniques and processes to cleanse the data and prepare it for proper analysis before initiating the RAM modelling or any other maintenance improvement activities. We help your organisation:
- Identify bad actors and areas of vulnerability that affect operational availability;
- Quantify which asset/ systems/ components and associated maintenance activities dominate downtime.
Once you know this, the asset/system design can be optimised, including its configuration, level of redundancy, component selection and supporting maintenance strategy. As a result, your organisation will be able to gain valuable insights from your bad data, which will positively support evidence-based asset management decision-making models.
Related RAM Case Study
If you want to know our related project about addressing bad quality data before a RAM study, check out our Light Rail case study.