Data analytics offers an opportunity to analyze heaps of data and extract useful information, which can then support improved decision-making. The occasion is only plausible if one has practical confidence in the data, since weak data can lead to poor choices.

FREMONT, CA: In today’s technological age, data is critical to decision-making. Companies can benefit from technology by collecting, evaluating, and utilizing information to make informed decisions. One research predicts that with the current rate of growth in data, by 2025, the volume of data will be 163 zettabytes. To better understand the number, consider that one zettabyte equals one trillion gigabytes. The illustration raises questions about quality, data storage, and management.

The article discusses the significance of data and its usage in performing consequential reliability studies. The widespread description of consistency is the probability that a piece of equipment, facility, or system will operate without malfunction for a given period under particular operating conditions. Thus, accurate, historically failed data, and its proper analysis is significant for any reliability analysis.

Data analytics offers an opportunity to analyze heaps of data and extract useful information, which can then support improved decision-making. The occasion is only plausible if one has practical confidence in the data, since weak data can lead to poor choices.

3 Key Steps to Enhance Data Quality

1. Deploy the Accurate Database Platform

The solution selected for the organization should not close any maintenance notifications or work orders until all necessary fields are completed. The chosen platform should immobilize shortcuts to ensure consistency of the data gathered.

2. Integrate Current Functions in an Inclusive Solution

The platform ought to incorporate all reliability functions into one solution to better combine information and decrease the number of systems employed in an organization. For instance, if any spare parts were taken from the warehouse, they need to be charged against a formal notification. The occurrence would need a platform that assimilates spare part management with maintenance activities.

3. Employ a Data Quality Assurance Program

Quality assurance activities for the installed solution must include a periodic audit of data quality across the firm. For instance, the quality assurance team can randomly audit 5 percent of the work orders and maintenance notifications for each operating facility to estimate the quality of the collected information. The result of the assessment can then be used to enhance the utilization of the tool further and guarantee an active database.

See also: Top Data Analytics Solution Companies