What’s Your Data Hygiene Rating?

Just like the UK’s Food Hygiene Rating, where restaurants are assessed for how well they handle and prepare food, businesses should assess how they handle and prepare their data.

Data is just as critical as food in a kitchen. If handled improperly, the consequences can be costly. Not just financially, but also to your reputation.

How can you improve your data hygiene and avoid any ‘spoiled’ data?

Storage & Security:

Just as food needs the right temperature and environment to stay fresh, your data needs secure, organised storage to remain accurate, accessible, and healthy.

If you're storing critical data in Excel on a network drive, don't be surprised if corruption occurs for any number of reasons. Storing data in a database with access control and input governed by data governance ensures data integrity and security.

Quality Control:

Like checking the quality of ingredients and washing certain items before putting them into the pot, you must check and cleanse your data to remove any 'dirt' or inaccuracies before it goes into your data pot.

If information is input manually without validation or control, you'll end up with incomplete or incorrect data. Dates, addresses, vendor codes, pressures, temperatures. Implementing automated validation for critical fields at point of entry can flag errors immediately and ensure high-quality data.

Regular Audits:

Just as kitchens undergo routine inspections, your data processes should be regularly audited to identify risks.

You haven’t performed an audit on your master tag register in months, and now several asset tags have conflicting information between your SmartPlant database and the 3D model. During the design phase, these discrepancies may go unnoticed, but when it comes to construction or commissioning, they can cause costly delays and rework. Regularly auditing your engineering data, such as pressures, temperatures, and tag-to-document relationships, ensures inconsistencies are caught early, keeping your project on track and reducing the risk of errors during critical phases like procurement and handover.

Training:

Food handlers need proper training, and so do your team members when it comes to managing and understanding data and using specific software. Make sure they know the rules and have read the procedures.

Someone enters information into the wrong field in a P&ID because they didn’t understand the required format. Should it be Mass Flow Rate or Volumetric Flow Rate if it just says 'Flow Rate'? Providing detailed training on the software and procedures ensures that all team members, from designers to document controllers, understand the correct protocols and avoid critical data entry errors.

Standards:

Just like a recipe ensures consistency, standardising your data processes will maintain the quality and reliability of your data.

If someone in the US inputs dates as ‘MM/DD/YY’ and another in the UK as ‘DD/MM/YY,’ confusion is bound to happen. Establishing company-wide data standards and making them easy to find ensures that everyone follows the same ‘recipe’ when inputting or handling data.

By adopting industry standards like CFIHOS or ISO 15926 for asset and tag management, you ensure that all data, whether from design, procurement, or maintenance follows a consistent, recognised structure. Standardisation improves data exchanges and reduces the risk of misinterpretation or errors during handovers.

Disposal:

Old, redundant, or duplicated data can create confusion and errors if not properly managed, especially when critical updates are made.

Your process team updates a P&ID with new design data, but the mechanical team is still referencing outdated information, leading to potential conflicts in equipment specifications during procurement or commissioning. To prevent this, systems should automatically notify the relevant departments when updates occur, ensuring that everyone works with the most up-to-date information. A data management system with clear workflows and alerts will ensure proper communication and prevent costly errors.

So, how would you score? Would you earn a top rating for your data hygiene, or are there areas to clean up?

Rating System:

5. VERY GOOD

4. GOOD

3. GENERALLY SATISFACTORY

2. IMPROVEMENT NECESSARY

1. MAJOR IMPROVEMENT NECESSARY

0. URGENT IMPROVEMENT NECESSARY

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