Although ERP systems (Enterprise Resource Planning) and their specific modules, such as maintenance management, reliability management, and integrity management, are used to streamline Operations and Maintenance activities in the Oil and Gas industry, the most crucial aspect remains the data. The performance of any system is only as good as the quality of data fed into it, and this is especially true in the Oil and Gas industry, where the sheer volume of data is overwhelming. Furthermore, this data is enriched with contextual information such as tags, identifiers, and classifications. Through interactions with leading oil and gas companies worldwide, Hofintech understands and realizes the value of quality data. In a recent study, IBM reported that poor data quality alone makes a $3.1 trillion dent in the U.S. economy annually. Poor data quality leads to lower productivity, outages, higher maintenance costs, and many other hazards and risks.

So, how do we ensure accurate asset data? Before we get to this topic, it is crucial first to understand the common issues with asset data. Watch out for these five data discrepancies :

1. Data Silos:

Does your organization face inconsistencies and errors due to maintenance and procurement teams using different data tools? Even if your tools are regularly updated, ensuring everyone consults the exact data version is difficult. The solution to this problem is to eliminate data silos. To accomplish this, it is essential to stress the significance of relying on a single source of truth within your organization. You can do this by encouraging cross-departmental knowledge sharing, gathering feedback, and monitoring progress. Then, consider consolidating all data into a single platform. By embracing a single source of truth, you'll be able to eliminate discrepancies and enhance overall accuracy.

2. Inaccurate/Incomplete Data:

It is crucial to ensure that data is accurate and regularly cleaned. If efforts are not made to remove irrelevant and redundant information, the data will become muddied over time. Decisions based on inaccurate or incomplete data can result in misinformed decisions, negatively impacting system workflow and increasing costs. The ideal scenario is for the data to reflect on-ground reality. Any changes made on the ground should be captured in the ERP or CMMS system. Sometimes, teams only input and retain data relevant to a particular task, which can result in other teams missing the necessary information they need. While Maintenance and Procurement teams may require different data fields, capturing complete and accurate data for all teams is essential. 

3. Duplicate Data:

Your asset data may contain duplicate entries if there are discrepancies in data formatting and naming conventions. This may occur when different units of measurement or inconsistent date formats are used or when data is generated by multiple agencies using different formats. These duplicate entries can cause problems if there isn't a proper system to identify them. Duplicate data can negatively affect procurement and cash flows. To address this issue, it's important to consider data governance. Since all decisions are based on the underlying data, it's crucial to have proper safeguards in place to protect it. Insufficient data governance can create multiple sources of truth, data inconsistencies, and data breaches. Regularly checking for duplicate data and implementing sound data governance practices can help eliminate these issues. 

4. Inaccessible Data:

Data that is inaccessible is as good as non-existent. But what qualifies as "accessible" data? Sharing data on an Excel sheet with team members technically counts as accessible data. However, is this data readily available on multiple devices or on-site for crucial decision-making? Additionally, if there are various versions of the same data with timestamps, how can we ensure that we are using the correct file? For data to be considered accessible, it must be available in real time, anywhere, and on any device.

 5. Unverified Data:

To ensure reliable data, it must be validated and verified. Having access to data isn't sufficient if it hasn't undergone proper protocols and checks. Comprehensive validation processes, intelligent prompts, and alerts should be employed to create quality data. Additionally, data should be updated regularly. By using these measures, organizations can trust their data and make informed decisions.

The first step towards building good data is understanding the errors that can occur during its creation. The five pointers in this blog provide a bird’s eye perspective of the most common problems with asset data. Stay tuned to our blog for valuable tips on building high-quality data for industrial assets.

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