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Remember the analogy of the dirty house/dirty data in Part I of our series? The only way the situation can improve in either example is to take steps towards cleaning up the mess. But then what? Once cleansed, what steps can be taken to keep your data clean? Think of data as a product your organization produces - for profit. What are the necessary work processes involved in data production and how can you improve these work processes to produce accurate, complete, timely, accessible data for rigorous analysis turning data into dollars? Data "production" requires data collection, data storage and data utilization. A faulty work process in any one of these three areas will impact data quality. Regardless of the industry, data collection is essential to maintaining the integrity of your data. Data collection is usually the function of data collectors who generally provide initial input of organizational data. Their job performance is strongly associated with the accuracy and completeness of data. It is important that data collectors know why the data is needed. Being tasked with simply collecting data could easily result in a very crowded database with little relevant data. However, making data collectors an integral part of the data production process through adequate training and technology can carry significant financial benefits. (See this documented case of a 40% reduction in reactive maintenance.) Data storage is strongly associated with the completeness, accessibility and timeliness of data. Once data is collected it must be stored. Even if relevant data is gathered, it will be useless to the data consumer if it cannot be located, emphasizing the importance of a valid data structure and management solution. Getting the necessary data out of your chosen CMMS/EAM system to support data utilization will be difficult without a clear understanding of failure coding and its important role in bridging the gap between CMMS and reliability analytics. The final stage in quality data production is data utilization. Although companies may invest millions of dollars to produce accurate, complete, timely, accessible data in the form of data storage systems and data gathering resources, the greatest ROI of data is realized in this final stage where rigorous data analysis: - Drives and automates technical data analysis to identify and predict failure occurrence and cause
- Leverages critical asset performance data from enterprise and local sources, which include ERP, CMMS/EAM, process historians, and condition monitoring systems
- Publishes key performance indicators to enable identification of improvement opportunities
- Supports continuous improvement of operational, surveillance, and maintenance strategies based upon best practice methodologies and analysis
- Pushes strategies back to execution systems to close the loop and continuously improve asset performance across the enterprise.
More and more successful organizations are recognizing their data as a strategic asset, using it as a basis to measure results, benchmark themselves against competitors, determine what practices are successful and then refine their strategies to improve performance. Maintaining data integrity is critical to the protection of this strategic asset. Join us in the September issue of APM Advisor for Part III of our data series where we'll introduce a data integrity process designed and implemented at Marathon Oil, raising the company's data confidence level to an all time high!
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